If you run a consulting firm, you know the math. A partner bills at $400 an hour. That same partner spends 12 hours a week writing proposals, summarizing client calls, and turning meeting notes into deliverables. That’s $4,800 a week of billable time spent on administrative synthesis work. Multiply by 50 weeks and you’re looking at $240,000 a year per senior person.

Most firms treat this as the cost of doing business. It’s not. It’s a cost of not having the right infrastructure.

The work I’m talking about isn’t strategic. It’s mechanical. A client meeting happens. Someone takes notes. Those notes get turned into a summary email, a proposal section, a research brief, or a slide deck. The thinking happened in the meeting. The synthesis is just formatting that thinking into the right container for the next step.

This is exactly the kind of work AI agents handle well. Not the strategy. Not the client relationship. Just the repetitive translation of spoken insight into written deliverable.

The Real Cost of Manual Meeting Synthesis

Let’s walk through what happens after a typical client discovery call at a mid-sized consulting firm.

The partner or principal runs the meeting. They take notes in a doc or on paper. After the call, they spend 30 to 60 minutes writing up what was said, what the client needs, and what the firm should propose. That write-up gets sent to the team. Someone on the team reads it, pulls relevant case studies, and starts drafting a proposal or a statement of work.

If the engagement is research-heavy, someone also starts secondary research. They pull reports, run company searches, and build a brief. That takes another 4 to 8 hours depending on the scope.

If the client asked a question the firm has answered before, someone digs through old decks and emails to find the answer. That takes anywhere from 20 minutes to two hours, depending on how well the firm’s knowledge base is organized. Most firms don’t have a knowledge base. They have a shared drive with 6,000 files and a search bar that returns 400 results.

Add it up. One client meeting generates 6 to 10 hours of downstream synthesis work. A firm running 15 discovery calls a month is spending 90 to 150 hours a month on this. At blended rates, that’s $18,000 to $45,000 a month in labor cost. Annualized, you’re looking at $216,000 to $540,000.

That’s the floor. It doesn’t count the opportunity cost of what those people could have been doing instead.

What AI Meeting Synthesis Actually Looks Like

An AI agent built for meeting synthesis does three things. It listens to the meeting, it writes the summary, and it routes the summary into the next step in your workflow.

Here’s what that looks like in practice.

You run a discovery call with a prospective client. The call is recorded. The agent transcribes it in real time. At the end of the call, the agent produces a structured summary. Not a transcript. A summary organized by topic, with action items, open questions, and key client statements pulled out.

That summary gets sent to your team in the format they need. If the next step is a proposal, the agent pulls relevant case studies and pricing from past proposals and drops them into a draft. If the next step is research, the agent runs a first-pass search on the client’s industry and competitors and builds a brief with sources.

If the client asked a question your firm has answered before, the agent searches your past work and surfaces the answer with a link to the original document.

This isn’t speculative. We build these agents for consulting firms. The core agent is what we call a Knowledge Agent. It reads every document, deck, and transcript your firm produces. It indexes them. When someone asks a question, it searches the corpus and returns an answer with attribution.

The Knowledge Agent pairs with two other agents. The Proposal Generation Agent takes the meeting summary and builds a proposal draft using your firm’s past work as a template. The Research Agent runs structured secondary research at the start of every engagement, with sources and a one-page brief.

All three agents are part of Omni Ops, the operational layer of the Omni platform. They don’t replace your people. They replace the 10 hours of manual synthesis work that happens after every client conversation.

The Proposal Generation Agent

Let’s start with proposals. Most consulting firms write proposals from scratch every time. A partner opens a blank doc, pulls a few slides from an old deck, rewrites the scope, and sends it over. The whole process takes 8 to 20 hours depending on the complexity.

The Proposal Generation Agent changes that. It takes the meeting summary, your firm’s past proposals, and your standard pricing model, and it builds a first draft. The draft includes an executive summary, a scope of work, a timeline, and a fee structure. It pulls case studies that match the client’s industry. It formats everything in your firm’s template.

The partner reviews it, edits it, and sends it. Total time: 2 hours instead of 12.

Here’s what the agent doesn’t do. It doesn’t write strategy. It doesn’t invent new service lines. It doesn’t make pricing decisions. It just assembles the pieces your firm has already created and formats them for the new opportunity.

One advisory firm we work with used to spend 15 to 20 hours per proposal. They were writing 8 to 10 proposals a month. That’s 120 to 200 hours a month of senior time. After deploying the Proposal Generation Agent, they cut that to 40 to 60 hours. They didn’t change their win rate. They just stopped paying $60,000 a month for assembly work.

The Research Agent

Research is the other major time sink. Every consulting engagement starts with secondary research. Someone on the team pulls reports, reads industry analyses, and builds a brief. That brief gets used in the kickoff meeting and referenced throughout the project.

The problem is that 70% of that research is repeated across clients. If you work with three clients in the same industry, you’re pulling the same reports three times. If you work with five clients in adjacent industries, you’re running overlapping searches and summarizing overlapping findings.

The Research Agent automates the first pass. You tell it the client’s industry, their competitors, and the key questions you need answered. It runs structured searches, pulls sources, and builds a one-page brief. The brief includes a summary of the industry, a competitive landscape, and answers to your key questions with citations.

Your team reviews the brief, adds context, and uses it in the kickoff. Total time: 2 hours instead of 8.

The Research Agent doesn’t replace deep domain expertise. It replaces the mechanical work of pulling and summarizing publicly available information. If your firm has proprietary research or unique frameworks, the agent can reference those too. It just needs access to your knowledge base.

One strategy firm we work with runs 12 to 15 engagements a year. Each engagement used to start with 10 to 15 hours of secondary research. That’s 120 to 225 hours a year. After deploying the Research Agent, they cut that to 30 to 50 hours. They didn’t change the quality of the research. They just stopped repeating the same work across clients.

If you want to see how this applies to your firm, the AI audit for consulting firms walks through the specific agents we’d build for your workflow. It’s a 60-minute session. No deck. Just three outputs: a process map, a cost model, and a build plan.

The Knowledge Agent

The Knowledge Agent is the foundation. It’s the agent that makes the other two possible.

Here’s the problem it solves. Every consulting project produces IP. Decks, reports, models, research briefs. Almost none of it is reusable because no one can find it. Someone asks a question the firm answered six months ago. Instead of searching for the answer, they just redo the work. It’s faster.

The Knowledge Agent indexes everything. Every document, every deck, every meeting transcript. It doesn’t just store them. It reads them. When someone asks a question, the agent searches the corpus, finds the relevant documents, and returns an answer with attribution.

This isn’t keyword search. The agent understands context. If someone asks “What pricing model did we use for the last financial services client?”, the agent returns the proposal with the pricing section highlighted. If someone asks “What’s our point of view on ESG reporting?”, the agent returns the three decks where the firm has written about it.

The Knowledge Agent also powers the other two agents. The Proposal Generation Agent pulls from the knowledge base. The Research Agent adds to it. Over time, the knowledge base becomes the firm’s institutional memory.

One boutique firm we work with had 4,000 documents in a shared drive. No one could find anything. Partners were rewriting the same content across proposals. After deploying the Knowledge Agent, they cut proposal prep time by 40%. They didn’t hire anyone. They just stopped losing time to search.

Why This Matters for Your P&L

Let’s tie this back to the numbers. A consulting firm doing $5M in revenue typically has 3 to 5 senior people. Each one spends 12 to 18 hours a week on synthesis work. That’s 36 to 90 hours a week across the firm. At blended rates of $250 to $400 an hour, you’re looking at $9,000 to $36,000 a week. Annualized, that’s $468,000 to $1.87M.

Most of that work is mechanical. It’s not strategy. It’s not client-facing. It’s just formatting insights into deliverables.

AI agents cut that time by 60% to 80%. You don’t eliminate the work. You eliminate the repetition. The partner still reviews the proposal. The team still adds context to the research brief. But the first draft is done. The assembly is automated.

For a $5M firm, that’s $280,000 to $1.5M in recaptured capacity. You can bill it. You can reinvest it in business development. You can use it to take on more complex work. The point is, it’s no longer locked up in administrative synthesis.

If you’re serious about recapturing that capacity, we’ve built a practical guide that walks through the first agent you should deploy. It’s called Deploy Your First Business Agent, and it includes a worksheet for mapping your current workflow, identifying the highest-cost repetitive task, and scoping the agent build. It’s free. No email gate. Just download it and use it.

What the Build Actually Looks Like

Most consulting firms don’t need custom AI infrastructure. They need three agents, a knowledge base, and a workflow that connects them.

The build starts with an audit. We map your current process from discovery call to deliverable. We identify where synthesis work happens, who does it, and how long it takes. We calculate the cost. Then we scope the agents.

The Knowledge Agent goes first. It’s the foundation. We connect it to your shared drive, your CRM, and your email. It indexes everything. Once it’s live, your team can ask it questions. That alone cuts search time by 70% to 90%.

Next is the Proposal Generation Agent. We build it on top of the knowledge base. It pulls your past proposals, your case studies, and your pricing model. It uses the meeting summary as input and produces a draft as output. The draft goes to the partner for review. The partner edits it and sends it.

The Research Agent comes last. It connects to your industry data sources, your proprietary research, and the knowledge base. It runs structured searches and builds briefs. The team reviews the brief, adds context, and uses it in the kickoff.

The whole build takes 6 to 10 weeks. You don’t need a data team. You don’t need to hire engineers. You just need someone on your team to own the process and work with us on the workflow design.

We’ve built this for firms doing $2M to $20M in revenue. The ROI is usually 3x to 8x in the first year. Not because the agents are magic. Because the cost of manual synthesis is so high that even a 60% reduction pays for itself in 90 days.

The Next Step

If this sounds like your firm, the next step is an Omni Audit. It’s a 60-minute session. No deck. No sales pitch. Just three outputs.

First, we map your current workflow. We identify where synthesis work happens, who does it, and how long it takes. Second, we calculate the cost. We show you what you’re spending on manual synthesis and what you’d recapture with agents. Third, we scope the build. We tell you which agents to deploy, in what order, and what the timeline looks like.

You walk away with a process map, a cost model, and a build plan. If you want to move forward, we build it. If you don’t, you keep the outputs and use them however you want.

Book a 60-min Omni Audit and we’ll run it in the next two weeks. Or if you want to see how this applies to consulting firms specifically, see Omni for consulting firms for the vertical-specific breakdown.

The firms that move first on this don’t just recapture capacity. They change their cost structure. They turn synthesis work from a senior-person problem into an infrastructure problem. And they reinvest the time into client work that actually compounds.

The question isn’t whether AI agents can do this work. They already do. The question is whether you’re going to keep paying $200,000 to $500,000 a year for manual synthesis, or whether you’re going to automate it and redeploy that capacity into revenue.

For more on how operational AI fits into the broader platform strategy, visit the Omni Ops page. If you want to explore other use cases across the platform, the EDNA blog and insights library have detailed breakdowns of agent builds across verticals.

The audit is the starting point. Book my Omni Audit and we’ll show you what this looks like for your firm.