How AI Agents are Rewriting the RFP Workflow

Transcript is auto-generated and may contain minor errors.

Really exciting session today demonstration of the brand new auto RFP agent jump into the new beta releasing later this month which will cover MCP connection. So that doesn't mean anything to you that's totally fine. That's what we'll cover today. And then we could talk a little bit about what's next, like what agents look like in the proposal, in the DDQ, in the bid space over time, and what this might look like all the way up to 2027 as things rapidly change. But I feel like the vision of what's possible with large language models and other types of AI is becoming more and more clear, how serious the effect is going to be, and how different responding to RFPs, DDQs, RFIs, and all of the above is going to be in the coming years. So at Auto RFP, we've spent a long time invested in this. We were founded straight after chat GPT, not even ChatGpt, just slightly before ChatGpt became available via the first API endpoint for the first usable OpenAI

model. And today we've taken that all the way through the product, worked with all the different model families and stayed across the evolution of the chatbot, the assistant, and now the agent. And really today is the time to I think release the agents like to the world and they've got to a level of capability now where they can be useful and not waste a lot of your time. So let's talk about the rise of the chatbot. We started with chat GPT being the very first and claude has become more and more important over time as well and widely adopted particularly in the proposal community as well and they are really quite simple still. So we started with the basic building blocks sometimes called primitance. The first one being prompts right everyone might remember prompt engineering being a big thing. I don't I don't know where that went. I do but it turned out to not be such an important part. So there was the prompt part where we just type stuff in the box. Maybe we even copy pasted from documents back then to get it inside of our prompt box and ask a question. Help

me write this. Help respond to this question given this context and then paste it in below. And that's where it started. And then we started to go, oh no, I don't want to copy paste things from all of these different files, right? I do want to be able to add entire documents. And we started with being able to upload 20 pages and then 100 pages. And Gemini has far exceeded that. Some of them are still, hey, this document's too big. You're not allowed to upload it into the context window. So there was the context part. And that's still even a constraint today for certain models. Then we have the tools. So they started to come out. Probably the first one was web search, right? When they started to release that, we saw that with Chach and Bing back in the day, there was like an integration there and more and more we saw Claude release web search and then of course Gemini being backed by Google has amazing web search and eventually releasing deep research as well. So let's not just call one tool once. let's use deep research where we can call web search a 100 times a 100 different ways

and over time look at 400 different websites. So that deep research was it was a huge expansion into what's possible with tools. Some of them will also have things like create image where it asks a different model to create an image or it maybe does research or yeah different aspects. And then finally more recently there's been all the buzz around MCPS the model context protocol and that is just a standardized format of connectors similar in a way I guess to APIs but but quite different so that just allows the agents and chat bots etc to easily connect with systems and that's a really wellsupported ecosystem now with over 17,000 public connectors available. So those are the four core primitives that we see in our chat bots and in really any agent today. Even things like uh artifacts, right, where it's generating documents are really just using a prompt and then calling a tool to create a document. Projects are

really just having preset prompts with some preset context and some tools all in one place, easily accessible and ready to go. So not necessarily like a feature or a new building block, but just bringing these together in different ways. And even more recently something like skills, which is just bringing prompts, context, tools, some files, different things like that, and bringing it into one big block so you can easily install things like skills. So there's some great skills for web design, for example, that pull in prompts and context of how you should best go about designing the landing page, for example. And that's all out of the box. So we started in this very generic place and now catch GPT and claude can do so many amazing things. You can rely on them for all sorts of tasks. But what we have seen more and more is the move over to specialized agents. And one of the largest markets for this is the software development market. The models are extremely powerful at software development. It's also easy to tell if a AI model has failed or not

when it comes to software development. Sometimes the test either passes or it fails. It generates a result or it doesn't which is not at all really similar to what we have to deal with proposal. What is actually going to cause me to win here? What is truth? And much much harder questions. So the software development focus made a lot of sense for all of the big providers. And what they eventually figured out was we probably want a specialized set of prompts of tools of context. We want special systems here that are going to be way more performant when it comes to that particular use case. So if we look broadly at Claude and Claude Code, recently Claude Code did a bit of an oopsie and released all of their source code by accident. Not advisable, but it really gave the industry a lot of insight into how those bleeding edge tools work under the hood. And if we compare them of what we might know about the Claude client and similar clients

like it to something more specialized, a more specialized agent like Claude code, we have a whole different level of complexity to this. So where Claude might just take you through three steps like you ask a question, it thinks about it, it generates an answer, Claude code has upwards of 10 different steps and that's built off software best practices, right? It's built by engineers who are thinking what is the best process. Oh, it's not just to ask and then think and then generate code, right? It's to plan. It's to search all of the current code that exists and identify how we've done things in the past. Let's not reproduce code that already exists. Let's try and leverage it again. There's a lot of different best practices just like any particular segment of the economy or white collar work that needs to be thought about there to turn it from an average developer or maybe someone who hasn't developed really anything at all to someone more senior with the skills frameworks mental models in order to produce good code. So that's the same with tools, right? Where a basic tool

like web search or research or a few other things, Claude code has a lot of very specialized tools that allow it to search tens of thousands of files very quickly at the same time which is needed in that particular use case. It has a lot more opinions. Quarter is often regarded as the model or at least the client that has like the best prompts. So you get really nice writing from it. You still get m dashes and things like that, but you might not get as many robust this, robust that. It's not X, it's Y type responses from it. In clawed code, you have very specific opinions. It will even get to the point of don't say this or don't use this tool, do this or don't use this coding approach, use this exact coding approach. So, it tries to overcome many of the challenges even with the state-of-the-art models, but they still fail on these tasks. So they've basically put it inside of the tool as part of clawed code to work around that. It's also got special loops in it that go okay if I fail how do I try again? And also it's able to work

with users and files. So, it's got a much more clean interface of being able to actually work with the user in their workspace because it assumes a semi-professional person that is aware of some software development practices is able to install a certain install claude code but also work within a development environment. So, it's specifically built for that use case, making it a lot faster to work with than if you were trying to copy paste all of the code in and out of claude. With Clawude code, it's actually doing that instantly for you. can edit hundreds of files at the same time if it needs to. And then on the skills side, Claude comes out of the box with very few skills where if you use Claude code even out of the box, it's going to have 11 plus preset skills ready to go. So basically, if I log into Claude, it can technically build software. It can do it reasonably well, but Claude code is just on an entirely different level in terms of the quality, in terms of the level of automation and kind of hands off the wheel. So you can think of this as like maybe Claude can give you some driver

assist on the road where Claude code is much more moving towards fully autonomous driving, right? And there's a huge delta between those two. And these specialized agents are having far superior results. So we're seeing things like if I was to go into to claude itself and go make me a new landing page for auto RFP, it comes up with this which is yes technically a landing page. Whereas if I go with a tool, a specialized agent in this case, Lovable, which really focuses on user interface design and shipping code, when I put that same prompt in, it's navigated to our website, it's taken our brand assets, and it's produced a better result. So, if I'm to jump in, they technically both completed and created landing pages. It might be impressive if I've never seen a landing page before. I've never created one before myself, but as someone who knows a little bit about code and what marketing websites should look like, Lord has given me raw code that's not ready to publish. There's no

built-in security check. It's made up those brand colors, hasn't really got those from anywhere, and even the buttons on the website that it provided don't work in the first instance. But interestingly enough, it does have better copywriting when compared. Whereas Lovable on the other hand had really high quality code compared to Claude. It was instantly ready to publish on the web. So I could just click a button and that would go straight out to a website. It automated a security audit off the back of it. It was actually matching the existing brand colors and the buttons actually work. Right? So when it comes to these two things like if I'm in a hackathon or I need to make marketing sites for a living like Claude is going to be useful if I'm playing around with the concept. But if this is my profession, then lovable is definitely going to be the way that I go there. And this has been true. So across across the chat, like we've got a lot of generic chat bots for the Geminis, the Chachts, the Claudes, and more recently, Open Claw has been a huge kind of hype cycle around that.

Really interesting ways that it incorporated new tools, more skills, more memory into the client, but still kept a fairly general use case where it can really do anything. And then more and more not just across coding but also other areas I think thin AI is a good example within the customer success space but we're seeing a lot of different kind of coding agents that are working very well and then to tie it back to our space here proposals like what really is there there's really not much and it's because it takes so much time to develop this kind of skill set and these massive companies have been very focused on doing it for the generic the coding they may ever produce a specialized proposal agent. So that is exactly what we intend to do is put massive amount of resources across our client base into building truly the best agent in the space. A hypers specialized one that sits on the cutting edge with the clawed codes the lovables of the

world but for our specific use case by incorporating these building blocks and entirely new but also very focused on our use case way to provide the best results. So that's something that we've been doing and putting a lot of work into really since we started the foundations have started have been built but more recently being able to put them all together in one place so that we can really show what's possible now that the model layer is ready. So if we just think about the agents and how they interact specifically in our space is with a generic agent you're probably experiencing a generic tone and style. It's very hard to collaborate with 20 plus subject matter experts inside of a chat GPT thread. Virtually impossible. It doesn't really respect formatting. Sometimes I'll ask Claude to like edit this existing document and it will just make me like a new HTML document or like some random markdown file that I need to copy paste. It's very limited in the context that it can search. So even with

a lot of new connectors, they're using keyword search. So, it's really struggling to find different content that it needs to respond. Even though I've technically connected it to Google Drive and I've technically connected it to Confluence, it's just unable to find that because like me, it's just typing keywords in, hoping for a match, not finding anything, and then taking it back to me to deal with. And it's quite slow, right? So, you've got these models now that are impressive but very slow with their thinking modes and such. And then there rather than having easy tools that are built for specific use cases, they've got very generic tools. So even something like Claude, it doesn't necessarily have like native document editing. It actually edits the documents through writing code. So it takes a long time because you're writing an entire script in the background just for you to edit two words in a word document. So very slow and it can also be very manual. And I said those are like some of the main drawbacks is there's many more of just the generic agents at the moment. So what we wanted when we were building the proposal agent was

something that learns the tone style win themes and and captures that over time that's super easy to use with multiple users at the same time. Can 20 people use 20 different agents at the same time on the same document? How can we get it to respect your templates, respect your formatting? How do we have it just be able to search across thousands or tens of thousands of files, documents, integrations that actually find what you need? And then how do we just have it instant so it's just natively editing documents? There's no waiting for code and how do we connect this with all of your different systems in a safe way as well. So what we built with in the foundations of our approach much like any other client and the open claws and the chachi pts of the world first we started with the models love the openclaw approach where you can bring in any model that you want what we have done in our infrastructure is we have partnered with each of the major providers in the most secure way possible so we're talking

about open AI we were able to work through Microsoft Azure's open AI service with zero training on data and then provide the latest and greatest model there GPT 4.5 and then with AWS we're able to provide secure version of Opus 4.6 six and then with Gemini the Google we've got Gemini 2.5 flash so basically a foundation of how do we get enterprise ready models with zero training and also hosted in Europe if you need it there in the US or Australia where you need it there and build that into the foundation and we don't want to be stuck with one model family we want to be able to easily switch between for different use cases because the use cases within our space are so varied sometimes you just need to do a quick search. Sometimes you really need deep reasoning. Sometimes you need taste and style more than anything else. And unfortunately, there's no real model to rule at all at the moment from our benchmarking. So bringing these different things together really gets people to the cutting edge, gets to the

best available models for different use cases. We're able to stitch them together in very interesting ways. The next thing is the context. So integrating the model with our existing integration layer. So that's where we connect into 15 plus systems like Confluence, Google Drive, SharePoint, things like that and then actually recreate a lot of that content in system so they can be easily searched. So rather than Claude that has to go out and search by keyword, we're able to use a very powerful and faster AI search inside of our platform to find content across all of those different systems. Meaning that we're not having to upload files. We're not having to rely on the keyword search, but we're getting way faster results and we're getting way more specific context in that context window to make sure we're getting the best results. Finally, as well, the tool piece from the base layer. So, we wanted the best-in-class web search and scraping.

So, we wanted to be able to go into websites, documents, particularly for driving customer insights. We wanted to be able to access as many websites as possible. go as deep into those websites as possible so that our customers can research their prospective clients and take that insight into their response. Because as we know, it's one thing to have a fast draft these days of all of the basic answers. That's great. But where we want the agent to go is take it a step further, research this particular customer, pull in more context, and help me win this thing by providing a more bespoke, a more insightful response. And then finally, the connector layer. So we did a lot of work actually on the like enterprise security side because MCP is still a very early standard. So there's huge risks in some of the connectors. For example, you might connect GitHub to the system so that you can search so that the system can search through GitHub and maybe there's interesting context there that you need for your particular use case. But at the same time, a lot of MCPs will allow you

to delete things in GitHub using their MCP or create things that they maybe shouldn't create. an agent still can't fully be trusted. So, we wanted to build a secure approach to deploying MCP that allows our customers one to define which MCPs their users are actually allowed to select and then having a readonly first approach where we really heavily recommend that you only use it for context. So from searching from these different systems, although also allowing our customers to do rights, which means things like Google calendar. I'll get into a few more use cases later, but basically not only do we want to bring you the models in that fashion, we want to be able to give you these cutting edge or sometimes even bleeding edge technologies in a way that's safe. So that is some of our engineering time as well. Cool. And then finally, the prompting as well, right? Like that is a basic part of it. But this is going to be huge as it evolves is much like claude code. How

do we start to develop more and more specialized prompts in the back end? How do we start to identify as a customer base where the gaps are in these different models and then can we give it explicit instructions to not do that and build that up over time. So much like legacy SAS players where it was just like we we write a feature for one person and everyone gets it. I think this is going to be amazing for agents as well where we write one prompt, we write one skill and then everyone gets the unlock from that. So there's going to be a huge amount of resources deployed there as well to basically build out the many necessary prompts and skills and etc on the back end that inform the agent and how it approaches response. Great. That's enough of my ramble and we will jump in to some real examples of the agent. Cool. I'll just jump straight into the platform here for those not familiar

with auto RFP. Just quickly, what we're looking at is a live project. So, we're inside of the project view. The project has already been drafted. So, there's already been an AI agent worked through all the content, find what it could generate the best response as it possibly could out of the box. Great. That's what we're looking at is this draft. And here I've got some questions that are form part of this particular RFP. In this case, we're doing a little something a little meta. We're doing a RFP for proposal software. So they've got some questions around the security of the platform, around the implementation of the platform, our service level agreement, all of those questions you might have for a vendor like us stepping through that. So first thing here is we've got some questions on data residency and it searched our content library and then provided these basic responses which is all we had in the content library at the moment. It's as specific as it got. So our objective here is I know that this is an important aspect for this particular prospective

customer. they are in this case we can say they are enthropic and we want to be able to customize this further to focus on their demands right we don't want to be talking about Sydney Australia or Frankfurt Germany if their only focus is United States-based hosting and we want to make sure that our certifications and everything mirror exactly what they need and that we're highlighting that as part of our response so we're not just putting all those context in a box we're actually being responsive or even better we're being insightful when providing our response So this is where one of the many surfaces the agent is available in but probably the primary one dayto-day is within the actual tool itself I can simply open the agent on the right side. So, it's here with me in the application. And then below I can see much like a chat GPT type interface, I've got the ability to type in a prompt. I've got the ability to attach any files that I

would like and send this message. And then I also have our specialized tools that we've developed like our web search that integrates very specifically to yeah search multiple different pages, scrape their context, even access sometimes things like PDFs and etc. We've got content here which allows us to search our content library. So all of the context in the system and the systems that we're integrated with very quickly. We also have the edit tool. So again, rather than having to basically just talk with this bot and then copy paste it into the interface, how can we start to give the agent the exact same tools that I have access to? So I've got access to edit this document. Why can't the agent just do it for me? So I'll switch that on. And then finally, the ability to document. So, not only can it edit the response that I'm going to give to the customer and insert it back into their file, but it has the ability to build out documents or artifacts or

addendums, attachments, whatever you want to call them. It's able to generate new documents like a service level agreement, like a security overview, whatever we might need, it's able to do that. And it's only able to do that in a generic way that it thinks is best. It's able to do that in different proposal formats and different branded templates and we'll step through all of that. So let's start with this example here. So I've got a number of different questions. I can select the questions that are in scope and this is defining the context. So I could work with the entire project and all questions throughout it or I could work with just specifically these security related questions. So I might start there and work on some data residency stuff to try and take this to the next level. So first thing I'll do is type in do some research on anthropics data residency requirements on the web. So I'm just going to see if it can find anything about where they host their different options. Maybe they've got different

APIs. And great, it's already been able to jump in very quickly there, find data residency on the claude API docs. And it's also found their internal trust center and being able to pull from there. So in this case, just to be clear, Enthropic, we're saying that this is an example prospective customer. So we're researching them and then we've got some context here. Great. So now that I know that, I'm going to say, yeah, we should probably update these responses to be more responsive to their needs. And based on this, it does look like they will want maybe US hosting. So we should align with that and their security requirements. So let's do that and let that run. The agent will think about that and then of course it has access to that edit tool. But we don't want the agent just randomly making edits without us seeing them first of course. So here is another part of the interface where much like what they have accessible in the coding type agent, we've got this in our workspace so that we can actually see what it's recommending. So I can see

here it's refactored quite a bit of this and it has gone and actually what we previously had is this little list here of all of the different places weighted pretty equally. It's gone through and said great our primary hosting region is in Oregon, United States. Great. So our other available regions also include and then just highlighting four clients requiring EU only hosting. Great. So it has tailored that a little further much more serious about US-based hosting now. So I can accept that change. And here it's even put in some stuff about bolding the different requirements that they that aligns with them and then yeah talking about how we align with their industryleading standards including those held by anthropic. Awesome. Great. So now I've just taken that from basic generic response there to something a little bit more high level. But maybe I remember on the call with the customer before they sent over this questionnaire actually the chief information security officer might have wanted a little bit more detail than that. Then I could even take this to the next level and ask

create a brief and highle overview document of our security approach and US hosting option for their CISO. So now past just researching it, providing me that context, and then applying that edit within the response, it's going to go even a step further and generate a document here based on all of that research and putting it all together. So again, it could do further research into our content. We could ask it to research the web more or whatever we really need and then put those building blocks together. And here you can see a very simple preview of the document on the right. with different headings, subheadings, formatting options, images, tables, and I can actually edit this document. So unlike some generic clients where I actually can't edit the underlying document, I just need to reprompt the AI, I can actually just jump in here and screenshot things and add them straight in even to the point of maybe I want to insert a table or something like that and start to work that way. And what's nice about this is although this is very

simple and high level here I was to build this whole thing out might be more interesting but ultimately what I will do is I will export this into a template. So it's not going to come out in a generic kind of AI slot based template but in anything that I've uploaded. So here for example only I've added three different types of branded documents we might do. So, I've added one called branded attachment just for no real fancy headings, just straight into the context. I've got one that is a branded one with a cover, right? So, like an auto RFP cover and then a more structured type document. And then even an RFP cover letter, right? Might include CEO's signature or something with an overview and populate with the client's name and then have aspects of the artifact or the document we're building here. So in this case I can go for a branded cover. Click export and then that will simply get the document ready and download it. It's straight for me to preview. So

cool. It's downloaded right there. And then I've got that already open in here in Google Docs just so we can quickly jump through. So here this is the branded template. It supports anything that's in in Google Docs or Word document formatting. So it can be quite complex. And then here you can see great it's got the it's got the cover table. It's got all of our fonts, colors, headings, etc. And then it's gone ahead and actually exported all of that different response material into a document ready to go further edits if I want to work in my Google Docs workspace if I want further or I could just use that and attach it directly to the proposal, send it to them, whatever I want to do there. So that's a really quick example of just one of the different use cases. Another use case could be something like an implementation plan is like a constant struggle for a lot of people where you need to make a more bespoke document for that particular customer. Be it an implementation plan, be it an approach, be it your assumptions,

whatever that might look like. So here they say provide a realistic implementation timeline and playbook for an organization of 500 employees. And then what RFPs come through? It's just being not like the base model has just found the stuff that we've provided for this in the past and put that there. But we know we want to work on this and develop something a little bit more detailed. So let's say in this case that we're going to be really hands-on with Enthropic and that's what we've put forward to them. So let's say rewrite the full roll out based on research of Enthropic's office locations and one week deployment per office. So here rather than just using one tool at a time, it's actually able to go through search the web. So it searched entropic office locations, it's found all their office locations across a few sources there and then automatically suggested an edit updating our roll out from the really short one to hey here's all of your offices and here's all the rollouts. So doesn't require me going to

a bunch of pages, copy pasting all of the different office locations and then going back to the RFP document and working that in or prompting an AI and then having to do that, right? It's just simply done. And then on top of that, I can just to show off add create a full implementation document 600 words or more with a table of rollout dates based on an assumed May 30th, 2026 kickoff date. Right? So now model's able to reason and go great. So if it's May 30 30th kickoff date 2026 then we can roll all of this through the different dates and then I'm going to make a document for that in our format maybe even an implementation specific branded format and here we can see great the implementation plan for anic got all the team training etc. And then we've got our different dates for the actual deployment post those first few steps. Here's what the actual office kickoffs and end dates will look like as well as

the required resources, some of the key risks, etc. So I could go look, yep, that looks great or maybe I want to do some further research whether it's entropic or in our internal content. Right? So if I had more content, I could go search the implementation we did for open AI, right? And then that will actually go through and be able to search the content, etc. So yeah, then apply that. So that's another kind of quick example of what's possible there. To round this out, we can also have something as simple as the service level agreement. So again, can pop that open and say create a document for the SLA, nice table and 200 300 words.

So it just allows me to get away from yeah having to manually create new document, copy paste all of that workflow and just makes you move so much faster and honestly like gives me at least the willpower to go that extra step that step that you might not otherwise take with tight deadlines and provide that extra document that they ask to attach optionally to give a more detailed implementation plan rather than just paste the generic thing in there and expect to win. We know that from our win rate report earlier this year, the number one difference between the people with that win over 50% and those that win under 50% is the amount of customer insight, is the amount of responsiveness to their particular requirements. So this is basically how you achieve that without having to hire more people, double down or start spending all of your Sundays adding that. So here the next big step is integrating

that with a far broader range of tools. So we've got all the core stuff in there now which is great but the next immediate step what we have in beta now and will release this month is the MCP connectors. So again MCP allows us to connect to yes 17,000 now different servers. Most applications these days support an MCP server where we can connect. So that could be simple organizational context. So there's an MCP for Slack for example that allows us to search through maybe different threads where you keep some information or some context. Maybe you've even got different Slack threads for certain customers that are going through the RFP process. You could keep the agent in line with that and it can get its updates from the chat. Confluence and notion are also supported which takes us past the native integrations that we have out of the box already in auto RFP and gives us a few extra tools to be able to search further and do more interesting things as well as Jura and

many more. Then you've got the customer context parts. This is really exciting because it allows us to do things like search the opportunities context in Salesforce, in HubSpot, in Microsoft Dynamics, in any system that supports the MCP protocol, which is most will be able to have the agent go, hey, actually look up this customer, what were their pain points, what are potential wind themes that could work here, and actually work that into the response, which will be an absolute game changer. And that's the same with call transcriptions as well. So can connect Gong is a common one and there's so many others that are supported by MCP really all of the major ones at this point. So not only can we get the customer context from the CRM we can also go hey look at every meeting we've ever had with them highlight all of the critical requirements that they mentioned find them in this document and tell me which ones they are so I can jump in there further or whatever creative prompt you can come up with to work that context in. really interesting stuff as well is

going past just reading and using as a context layer and starting to use it as a project manager. So things like realizing during the bid that I actually need to create some time to do the implementation plan. What I could do very easily as we step through is go hey I need some time with implementation manager X. Could you schedule that in my Google calendar and provide them a quick overview of what we're going to be talking about? And of course, it's looking at the RFP. So, it can take all of the required context that they talk about implementation, take that into a Google calendar invite, and actually write that invite and send it to the person and give you that feedback directly within the tool. So, that's just like incredible things that we can do. And I think we'll develop more and more of that natively into the tool, but that gives you a starting point. You can prompt it and do that. I think some of our vision around this that I'll talk about more is that we want it to be able to automatically do these types of tasks and take more actions for you where it makes sense. There's also so many industry or

customer specific connectors that might exist. There's great registries online where you can search. So if you search MCP server list, you'll have a bunch of different websites that index them all. There is in a financial services regulatory context. There's FINRA where I can search things like broker check and so many other information sources and pull anything from a form ADV all the way through to other things in a software context. You can do amazing things as well. You can integrate with yeah jurors of the world about your product road map or linear if you use that tool even things like GitHub where if you are someone who has access or could be authorized to access the actual application code it's really interesting because you can answer really technical questions without the need for one of your subject matter experts to search through all of the code themselves and find a particular answer to your question. you might be able to get a great first draft there and then assign them in as a reviewer after doing kind

of the hard work for them. And then there's different research contexts whether you're in healthcare and something like PubMed articles might help you sell or maybe there's look at any academic research to do with this particular painoint that they're accessing and pulling in that context. So, I think we'll see a lot of companies get huge amounts of competitive advantage by connecting interesting information sources and then building that into their workflow to be more accurate, but also much more informed than their competitors. It's impossible for a bid manager, for a pre-sales engineer, for whoever to know everything about everyone and to have the 20 hours to do all of the customer research and understand their space, etc. But the agent can give you just so much leverage to come more informed to a deal than virtually anyone else on earth at the moment. And I think really now is the time to to make use of that in your win rates while this isn't a commodity but actually something new. And yeah, again 15 17,000 plus public

servers. And let's touch on this a little more. So here we have our organization settings integrations. We have all of our different content integrations and everything that we've done to date. And what we'll have here is our agent MCP servers. So this is where the admin of an account can log in and then actually add an MCP server. So we're basically whitelisting the sources in which they can have access to. So here we've got some native ones just so you can click on them and get started. But again, we support over 15,000 of these on the MCP standard. So you can just click add a custom server and that will have your Salesforces and everything else of the world right there. You put in the URL and it will start the login process. Really nice benefit of MCP is that we don't have to go and integrate with a thousand different APIs, 15,000 different APIs.

We can simply do that one connection and you can connect to anything. And just to show you that flow here, I'll go ahead and delete. Actually, I'll keep this. I'll keep this. So, let's say I add the connection. I would go through maybe click Slack here and then sign into Slack and connect it. What that will show me is a list of all of the tools that makes available. So here we've got grain which is similar to gong for those not familiar. It's a meeting recorder at its most basic. So here right we've given the agent the name of the tool a description of what it is the meeting recordings and transcripts right and we can give it access to different tools. So is the AI allowed to list my meetings or not? Is it allowed to fetch meetings that I've attended? Is it allowed to search companies that I've met with? Is it allowed to so many different things like list the open deals that I'm working on? And by default here, we've only given it read access. So, it's actually safe. You can go in there and make sure that if someone logs into auto RFP and they authorize something like a

gong or a grain. It's not just going to come in and start to fetch deals or create collections or do anything crazy. It's just going to be able to read and do things that are useful and that make sense in this context. So that's something we spent some time on there. And then what that means is that every user when they actually log into auto RFP will have the different tools available. So here not only the document tool and the edit and everything we've been through, but actually we've got the linear tool turned on here and then grain not connected where I could click on grain and then connect that for myself. So really cool thing about MCP as well is it will respect my permissions. So within GitHub, within linear, within Confluence, that user and their agent will only be able to use whatever they're actually authorized to use. So that's a huge benefit of our type of approach versus just one MCP connector that connects all of your information as an admin and then anyone

can go willy-nilly and search all of the meetings in the company. This will just search that individual's users meetings, what they've been shared. So now that we've got that an interesting one and just one example of many here is that I can now go to a question let's say about the road map. I can open my agent and with the linear tool already active here, I can go through and have it search our linear. And knowing that this prospect is maybe super interested in our reporting road map and all the different things we're working on there, it can search our linear search all the different road map items and then actually put that together for me and then provide that in the response. So we'll give that a second. Interesting thing about the linear agent as well in particular is it can actually talk to a linear agent. So, we're already having agentto agent

discussions in some of these integrations. Cool. So we can see here it's actually gone through and then it's got all of our reporting enhancements and it's got great here's our insights redesign we're going to be doing this and that and I go look rather than get into that level of detail maybe yeah let's just make a summarized road map document with a table for the reporting elements Cool. Now I've got that and we can go. Okay, cool. So rather than maybe responding in line here, let's go update the response to refer to that. attachment. Cool. So now rather than having that respond in line, we're going look that's

maybe a little bit big. Maybe it exceeds the word count. It should be an attachment or something like that. Great. So a detailed road map of our reporting initiatives plan items is available in the attached summarized reporting road map which is exactly what it will be downloaded as and then eventually attached. And I've done that across a number of different prompts here. But there's nothing stopping a user as well from doing something like this. where I can go firstly search linear for this then do this and you might even have workflows where you want those compound elements first search Salesforce for this then consider this then do web research then update the relevant responses with requirements that that interact with that context so cool it's able to go through there and do that all in one foul swoop that is the foundation and really the first release of our AI agent. So, we're just getting started here. This is the absolute V1 and we see the future of

this as working with the top winners on Earth, our customers and others to build skills directly into the platform based off our research findings. So things like customer insights, how can we really understand how to do those best, how to respond in different types of RFPs and respond the best way and have that data backed and work on that with domain experts so that our platform builds from all of that collective knowledge automations as well. So not just triggering the agent for these different use cases by talking to it and working backwards and forwards there, but actually building it throughout more and more of the process. So using it to automatically triage RFPs to the right people to flag conflicts as they appear or even near the due date. Can you please have a final check and then suggest changes 3 days out from the due date. So you not even having to prompt anything but it actually coming to you, maybe even sending you an email, a Slack message going, "Hey, it's due in 3 days. So I did a final check. Looks like this

is missing. Could you jump in and help me respond and finish that off?" And then a lot more tools. We want to get to the point where the agent has access to all of the same tools as a human. So that means assigning users, maybe as editors and reviewers, maybe exporting the files and checking the export, doing all of those types of actions that you can within auto RFP like flagging content, updating it, etc. And really getting all of that to the agent over time so that you can act through your agent rather than just being able to act through the user interface. And then there'll be the concepts of ambience and really interesting and we've got some really interesting thoughts around how this applies that we might not share at this stage because they are that interesting. But things like being able to forecast what users are working on. So, if you're working on a bid and you're making a certain type of change, maybe you're updating something from first person to third person, we see you do that a few times. Can we automatically trigger the

agent to suggest how that should happen across the rest of the document for you automatically without it being annoying? So how can it just be sitting in the background always thinking always working with you like a colleague but you not having to jump in there and think about how it could be useful but it itself taking the responsibility for that and jumping in where it makes sense. So that's some of the stuff that we're actively exploring and working on today. And then I think broader than that when we think all the way out to 2027 I think by then the need for a proposal agent and this is more broadly as well is it should have onetoone context and the ability to commit actions just like a human user inside of the platform. It should have every feature, every tool. It should be super heavily customizable. You should be in a way to create like a basic version of some of your subject matter experts. Here's the kind of tools that they would have or rely on. And here's their approach and really here's how they think. And I would like you, the agent, to come in and think like

them and work as hard as you can before bringing them in. And I'd like you to learn about your colleagues in your team, maybe in that subject matter expert unit. And maybe assign them when you don't know the answer or follow up with them if they're going to be late to a request. Like, how can we start to hand off ownership or not not ultimate ownership, but really a lot of responsibility to the agent to follow up things and help us keep on track. And we'll also do things like every good agent has really specialized memory and memory will be I think very hard to do in the proposal space in a good way. So I think we're going to want a very specialized type of memory that we will develop as part of the agent as well. So similar to open claw had a lot of innovations there. We'll need to look into similar innovations but I think they'll be in a completely different direction. We will also need completely new user interface paradigms. So we're already starting to explore what happens when certain processes or certain types of questionnaires can be entirely

automated. What does that even look like for a user? Because then I don't necessarily need to log in and look at it like I need to do this or that. How do we prioritize humans time to the highest leverage things? Like we're learning that just copy pasting stuff and doing basic admin work isn't going to help the win rate. And there's so much more that you need to be doing as the bar raises and raises. So we need you to be able to allocate your time differently and think about how you do that to actually stay ahead of your competition. And that will require a lot of work to be automated and that will require a lot of different user interfaces where you still trust the ultimate automation. So I think we started and we've got a we've got the trust score type UX um which we yeah which we started and built that is now becoming more widely available across the industry which I think is great but I think we're going to need even more transparency even more specialized types of interfaces to display that and for you to understand how the agents are working and where you're most needed. So

yeah, it's going to be a really exciting time, but I definitely see this space much like software engineers and if you've seen the graph recently where they have the software engineering hiring is actually going up when everyone said it would completely collapse and go down. I think we'll see something similar here is huge amounts of leverage come to the fold for us in the proposal management space or in knowledge management in general because we're going to be doing amazing things within our tools that previously weren't really part of our role will be huge return on investment and an important part of a more important part even I would say of our organizations going forward. Thank you for coming through that. I hope that was useful that there's some takeaways there and at least giving you a glimpse into the future of what this looks like what you might be thinking about whether you're an auto RFP customer and you'll actually be able to use a lot of this tomorrow whether you're using Claude or generic chat bots but you can still think and integrate some of this into your approach or even other platforms I I gen genuinely hope you you got something out of this today and yeah you can log join

like more topics about MCPS where agents going and etc. I'd be really happy to answer any questions on anything. Yeah. Related to agents, of course, the product, where we see this going, MCPS or otherwise. >> We did get a question through the chat, which I believe that you responded to just now, but do you foresee a connector to Confluence? >> Yeah. Yeah. So, auto RFP already has a connector to Confluence like natively in the content. >> Oh, sorry. I got some feedback vendor. >> And then on top of that, yes, the Confluence MCP is also supported. So yeah, not only one but two Confluence integrations will be available and yeah, you'll be able to see the different use cases. One will be in the agent and one will just be in the nature of how auto RFP responds. When is this available? And Angela asks, so the agent will be live tomorrow. you will have a notification about it and how to enable

it because we're still allowing you to self opt into this process if you're not quite ready for the for the agent power or want to try it out first. So, it will be in your organization settings, feature flags, agents, you'll be able to turn it on tomorrow. That will give you everything including the documentation and then MCPs will be out later this month after we test it a little more with customers. But also, please feel free to reach out to me. If you've got a burning desire to join the MCP beta, then yeah, please reach out. We can turn that on for you and you can be at the cutting edge of it and help us work through that as well. Yeah. Yeah. There's like longer form proposal specific questions. Yeah. So, it's interesting with the longer form stuff. I guess it depends on the nature of those documents. I think what we saw some of today is like being able to work with larger documents as artifacts and then attach those in. And then yeah, a lot of the examples inside of our demo account today were very short sharp answers. We can do slightly larger than that, but we're certainly not doing the types of RFPs that you

would see in like the construction industry, facilities management, architecture, that kind of thing where it's very long form. That's not our specialty at Auto RFP, but yeah, we're going to be working with partners and actually sharing some of our IP with people in the space so that they can build that out in other industries as well. Um, cool. And then, yeah, it's interesting, Maurice, on the visuals side of things, charts, elements, and others. So, what RFP does allow for the upload of images into the actual responses themselves. And a really interesting aspect of MCPs is there's MCPs that allow you to actually generate images whether that's an image model or whether that's something that builds diagrams and etc. So that's just something that we're starting to see is like you can ask an MCP to generate a diagram and then on depending on the MCP you could access that diagram and bring it into auto RFP. So I think we'll be exploring ways to automatically bring it back into auto RFP but for now I think

you will be able to do interesting things around at least creating the diagram and maybe getting it from its source and plugging it in. So yeah I think visuals charts and elements uh directly in our sites and PowerPoint's also interesting. I think PowerPoint also has an MCP. So I'd be interested to see yeah can like what a prompt would do. Please create a a PowerPoint with 15 slides about this RFP response and see how it does. But yeah, these are all all things that we need to test out and more and more as the different use cases become common, we will build them out more natively. So turn a prompt into a single button and build skills around something to ensure you get the best results. Any other questions? Yeah, feel free to pop them in the chat. But know our session has ended for today. So really appreciate everyone's time. Have a great rest of day and yeah, look forward to seeing you on the next one. If you're not currently a customer and you want to

dive more into this, you can sign up, step through a demo. Otherwise, yeah, invite you to try this out. Looking forward to customer feedback. And again, just the beginning of what's going to be an exciting era of agents. No, thank you. Have a good day all. Bye.