AI in investment banking that shows the source page for every number it drafts
We build agents for the deal work your analysts repeat on every mandate: comps spread from filings, first drafts of CIM and pitchbook sections, buyer lists, data room answers and model tie-outs. Every figure carries the document and page it came from, a mismatch goes to an analyst, and the deal data stays in your own cloud.
Jason, our COO, runs the call and scopes the first workflow with you.
Illustrative run. Companies and figures are sample data, and the checks are set per workflow during scoping.
Clients include
AI tools for M&A deal teams, built around how your team works
Start with the work that repeats on every mandate, follows a template your team already trusts, and gets checked by an associate before it leaves the building. These six workflows meet all three.
Comps and financial spreading from filings
The agent reads 10-Ks, 10-Qs and earnings releases, spreads the line items into your template, and stores the document and page beside every figure it writes.
How the agent finds the right figureCIM and pitchbook first drafts
Business overview, industry and financial summary sections drafted from the company's own materials into your firm's template, with each claim tied to the page it came from, for an associate to edit.
Try it on your own templates firstBuyer list building
The agent screens strategic and financial buyers against the criteria your team sets, checks each one against your CRM history, and writes the reason each name made the list.
Built on self-hosted n8nData room Q&A on the sell side
Buyer questions are matched to the data room documents that answer them, and a drafted reply cites the file and page. A second agent checks each reply against its source before your team sends it.
How a second agent vets every replyModel and deck tie-outs
Every number in the deck is traced back to its cell in the model and its page in the filing. Transposed digits, stale hard-codes and totals that do not foot come back as a short list for an analyst.
See a sample tie-out reportPrecedents and company profiles
Transaction precedents and one-page company profiles assembled from the sources your firm licenses and its own past deal files, each line linked back to where it was found.
How every run is loggedHow an investment banking AI build runs
The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work
Jason, COOAI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.
- Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- The documents and folders each agent may read
- The checks a figure must pass
- The timeline, before you sign
Maciej, CTOBuild
Built against your templates and past deal files, in your environment, as production software from the first week.
- Staging first, on deal files you choose
- Your analysts use it on live work before go-live
- Self-hosted n8n in your cloud
- Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Runs behind your own auth and folder permissions
- Every run logged for your compliance team
- Your analysts and associates trained on the system
- Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow, scoped with you
What your deal team gets from a build with us
We build each agent around your templates and your review steps, and run it in your environment.
| What you get | |
|---|---|
| You receive | Working agents inside your models, templates and document stores |
| Every figure | Stored with the document and page it came from |
| Where deal data lives | In your own cloud, on self-hosted n8n |
| Before it reaches a client | An analyst approves, and the run is logged |
| Timeline | Scoped per workflow and agreed before you sign |
| After launch | The engineers who built it monitor and extend it |
What deal teams ask about AI in investment banking
Ask us something elseWill AI replace investment bankers?
The agents we build take the assembly work underneath a banker's judgment: spreading, first drafts, list building and tie-outs. Pricing a deal, advising a board and running a negotiation stay with your bankers, and every output in our builds waits for an analyst's approval before it goes anywhere.
How is AI used in investment banking deal execution?
Mostly on repeatable, document-heavy work: spreading comps from filings, drafting CIM and pitchbook sections, screening buyers, answering data room questions on the sell side, and checking that the deck, the model and the filings agree. Each of those has a template and a reviewer already, which is what makes it a good first build.
How do you stop the AI from getting a number wrong?
We do not let the model do the arithmetic. It extracts each figure with its source page, a database recalculates totals and ratios, a second agent compares the draft to the page, and any mismatch waits for an analyst.
Does deal data or MNPI leave our environment?
The workflow runs on self-hosted n8n in your own cloud, and agents read with the permissions your people already have, so your information barriers apply to the agent too. A model call carries only the pages its task needs, under your firm's agreement with the provider, or goes to a model you host. Every run is logged with what it read and who approved it.
Which systems can the agents work with?
Excel models, PowerPoint templates, your document management system and data room exports, your CRM for buyer history, and licensed data sources through the access your license allows. The AI Audit confirms what each of your systems exposes before anything is quoted.
Do you sell an off-the-shelf AI tool for investment banking?
No. We build custom agents around your templates, checks and permissions, and run them in your cloud. If you are weighing a packaged product against a build, our build vs buy guide sets out how to decide.
How long does a build take, and what does it cost?
We scope each workflow and give you the timeline and price before you sign. To prove one on your data first, an AI proof of concept takes 2 to 6 weeks and most fall between $35,000 and $75,000. The AI Audit costs $10,000 to $100,000, takes 4 to 8 weeks, and both fees are credited toward the build.
Who runs the system after launch?
The engineers who built it. They watch every run, fix what breaks and add the next workflow in weekly sprints that you direct, under our AI managed services.
Industries that work the same companies from another seat
Private equity buyers, venture investors, lenders and strategy consultants read the filings and models your deal team builds.
AI in private equity
Deal screening, data room review with a page citation for every finding, and portfolio KPI pack monitoring in the firm's own cloud.
See AI in private equityAI for venture capital
Inbound deck screening, diligence file checks, founder updates and LP letter drafts, with a partner approving every message.
See AI for venture capitalPut your first workflow on the board
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
