Every software company knows the drill. A request for proposal lands in your inbox. You spend three to five days pulling requirements from scattered emails, PDF briefs, and meeting notes. A solutions architect assembles the estimate. Someone formats it into a document. By the time the proposal reaches the client, the assumptions may already be stale.
That process is the bottleneck that nobody fixes. It is accepted as a cost of doing business. But a group of developers at GeekyAnts decided it did not have to be that way. They built DealRoom.ai, a system that uses four coordinated AI agents to produce a technical proposal in under 60 seconds.
Where the time really goes
DealRoom started by measuring where presales teams actually spend their time. The breakdown is revealing. Reading and reviewing source documents consumes 40 percent of the effort. Feature estimation takes another 25 percent. Formatting and assembly eats 20 percent. The final 15 percent goes to review and revision.
The estimation piece carries the highest risk. When the team examined actual project sheets from delivered work, they found the same feature estimated at 40 hours by one architect and 120 hours by another. That was not incompetence. It was the absence of a shared knowledge base. Without institutional memory, every estimate starts from scratch.
Four agents, one pipeline
DealRoom is not a single language model that reads a document and spits out a summary. It is four specialized agents, each with a defined role, passing structured work through a pipeline.
The Analyst reads the original source materials and extracts structured data: features, user roles, priorities, technical constraints, integration requirements. It interprets intent rather than just scanning keywords.
The Architect maps those findings into systems, modules, and a recommended technology stack. Crucially, each feature is enriched with hours and complexity data drawn from a knowledge base of historical project actuals. The estimates reference what similar features actually took in production, not what a language model guesses.
The Estimator produces three delivery strategies. Conservative covers a minimum viable scope with a lean team. Balanced covers the full scope with a right-sized team and standard timeline. Aggressive deploys a larger parallel team at a higher cost. Each strategy includes a cost breakdown, timeline, and risk profile.
The Devil is Advocate reviews everything the other agents produced. It challenges assumptions, flags timelines that do not account for third-party API stabilisation, and surfaces compliance requirements that the Architect missed. The proposal survives internal challenge before it ever reaches the client.
Why presentation matters
Most proposals land as static PDFs. The client opens it, scrolls through, and sends questions that take 48 hours to answer. DealRoom instead produces a web-based interface. The scope map is an expandable hierarchy. Stakeholders can toggle features on and off, and the cost, timeline, and team size update in real time. No revised estimates. No back-and-forth.
Every estimate carries a confidence score. A high score means the figure is grounded in historical data from delivered projects. A low score flags features estimated without precedent. That tells the presales lead exactly where to focus manual review.
The hard lessons
The team drew three conclusions worth repeating. First, pure language model pipelines are not sufficient for production estimates. Document interpretation requires LLMs. Cost calculation does not. Separating the two produces outputs that are both analytical and accurate.
Second, internal challenge before client delivery is a quality mechanism, not overhead. Proposals that went through the Devil is Advocate review carried timelines 15 to 20 percent longer and were far more defensible.
Third, the format of delivery shapes how the analysis gets used. An accurate proposal in a static document loses stakeholder attention before the scope is reviewed. The same content in an interactive format keeps people engaged through the decision.
Where this is heading
DealRoom currently supports healthcare, e-commerce, and edtech domains. Fintech, logistics, and SaaS are next. The team is building a feedback loop that routes accepted proposals back into the knowledge base so the system gets more accurate with every engagement.
The lesson for any business that writes proposals is straightforward. The bottleneck is not that your team cannot estimate. It is that every estimate starts from scratch. A structured knowledge base, coordinated agents, and a review layer can cut cycle time from days to minutes without sacrificing quality.