How do you use AI for due diligence? An AlphaSense workflow, from a blank prompt to a data-room red-flag list.

Diligence has always taken weeks, and most of that time goes to a single unglamorous task: pulling scattered information together. The earnings calls, analyst reports, expert transcripts, and filings each live somewhere different. That's the part AI changes. Inside a purpose-built platform, you can get a sourced read on an industry, build the committee slide, pull deal comps into Excel, run channel checks, and scan an entire virtual data room for red flags, in hours, with every claim traceable to the document it came from.

The workflow below runs inside AlphaSense, whose library of company filings, broker research, and expert interviews is the material a diligence process usually spends weeks assembling by hand. Some steps are prompts you type while a few are pre-built agents you run. Both are shown below with the exact wording to use, parameters in brackets, and the full sequence sits at the end.

Where does diligence slow down?

The instinct is to blame access, but the documents were never the problem. The hold-up is turning a pile of them into a view you can act on.

"The real bottleneck has never been access; it's always been synthesis. That's the part AI is changing—it changes the unit of work." — Kyle Turner, Head of Solutions Engineering, Affinity

A deal team that used to bounce between dozens of tabs can now work from one place, and the newer agents go further, reading source material and surfacing the parts that matter.

How do you get your arms around an industry fast?

Start at deal inception, when someone has floated an idea at committee and a junior analyst has to decide whether it's worth pursuing. One natural-language prompt does the first read.

Act as a private equity associate evaluating the [industry] market in [region]. Surface the market size, growth trajectory, demand drivers, competitive landscape, M&A activity, and any white space, and cite the source for each point.


The platform returns a structured report, exportable as a PDF, in a couple of minutes. Then one more prompt turns the findings into a committee-ready slide.

Turn that market read into a committee-ready slide summarizing [industry], with the key figures and the source behind each one.


If prompt-writing feels like the hard part, it isn't the bottleneck it seems: you can have Claude draft the prompt itself, then paste it into AlphaSense.

How do you know you can trust the answer?

This is where generic AI research falls down. A general model returns an answer with no way to see where it came from, and it will trust whatever it scraped off the open web. When there's money on the line, that isn't enough.

"Access to the information isn't the same as intelligence. Our purpose-built retrieval layer understands the context of the question and the workflow behind it, so the blind spots are few and far between." — Ben Collins, AlphaSense

Every claim in the report links back to its source, so you can click into a figure and land on the exact broker report or filing it came from. The read is drawn from a curated library of more than 500 million documents, including roughly 270,000 expert-interview transcripts, rather than the open internet.

How do you pull deal comps and channel checks?

Two of the most repetitive diligence steps are pre-built as agents, so you run them rather than write them from scratch. Run the precedent-transaction agent with this brief:

[industry] precedent transactions: return the deal comps, valuations, and the screening methodology, as a table I can export to Excel.


Then run the channel-check agent across the expert-interview transcripts:

Synthesize what practitioners are saying about [industry], organized by company and theme.


Both give you the outside read that shapes a thesis, without the weeks of manual assembly.

Can AI read a data room?

The newest step is the one that used to eat whole weeks: reading the data room. You import an entire virtual data room into a dedicated diligence workspace and question it directly.

Import this data room. What are the red flags? Are there customer-concentration issues I should be aware of? What are the gaps in the information provided?


From there, a pre-built agent scans the contents and drafts a diligence request list, the specific documents to ask the banker or target for, based on what's missing. A recent connection with Intralinks lets you sync a data room straight into the workspace, and cross-check whether the narrative in the CIM holds up against what brokers and analysts are saying about the market. You still make the judgment calls. The agent just surfaces where to look.

What you end up with

A diligence process that runs in hours instead of weeks, with a sourced trail behind every claim: the market read, the slide, the comps, the channel checks, and the data-room scan, all in one place. The diligence picture answers whether a company holds up. Paired with the relationship and deal history a firm keeps in Affinity, the AI-first private capital CRM, it also answers who at your firm already has a line into the company, so the two reads inform the same decision.

The full workflow, run it in AlphaSense

Here is the whole sequence in order. The typed prompts go into AlphaSense's prompt box; the agent briefs go into the named pre-built agents. Change what's in the brackets to fit your target.

1. Get the sourced market read (prompt)

Act as a private equity associate evaluating the [industry] market in [region]. Surface the market size, growth trajectory, demand drivers, competitive landscape, M&A activity, and any white space, and cite the source for each point.


2. Build the committee slide (prompt)

Turn that market read into a committee-ready slide summarizing [industry], with the key figures and the source behind each one.


3. Pull deal comps (precedent-transaction agent)

[industry] precedent transactions: return the deal comps, valuations, and the screening methodology, as a table I can export to Excel.


4. Run channel checks (channel-check agent)

Synthesize what practitioners are saying about [industry], organized by company and theme.


5. Scan the data room (prompt)

Import this data room. What are the red flags? Are there customer-concentration issues I should be aware of? What are the gaps in the information provided?


The data-room agent then drafts your diligence request list from what's missing. Connecting Intralinks lets you sync a data room straight in.

The diligence read is half the picture

While AlphaSense tells you whether a company holds up, Affinity tells you who at your firm already has a line into it, so the outside read and your own relationship history inform the same decision. Talk to our team about putting both to work in your diligence process.

More AI workflows across the deal lifecycle

This is one stage of a four-part series on connecting AI to where your firm's work already lives.

• Start here: Putting AI to work across the private capital deal lifecycle—the full thesis and all four workflows
• Sourcing (Grata): How do you find off-market companies to acquire?
• Sourcing (Crunchbase): How do you find emerging startups before they raise?
• Monitoring (Lumonic): How do you monitor a private credit portfolio?

FAQ

How do you use AI for due diligence?

Use a platform with a curated, source-linked content library to generate a sourced market read, pull precedent-transaction comps, run channel checks against expert interviews, and scan a virtual data room for red flags and gaps. The advantage over a general AI tool is that every claim traces back to a named source you can verify.

Can you trust AI for investment diligence?

Only when the output is traceable. A general model produces answers with no verifiable basis; a purpose-built research platform links each claim to the filing, broker report, or expert transcript behind it, which is what makes the output defensible when there is capital at stake.

Can AI review a virtual data room?

Yes. You can import a data room into a dedicated workspace and ask for red flags, customer-concentration risk, and missing information. A pre-built agent can generate a diligence request list of the documents still needed, and a data-room connection lets you cross-check the CIM's narrative against outside market commentary.

What is a channel check?

A read on a company or market drawn from conversations with people close to it, typically industry practitioners. Running channel checks across a library of expert-interview transcripts returns those outside perspectives quickly, which helps confirm or challenge an investment thesis during diligence.

How do you conduct private equity due diligence?

Private equity due diligence works through a company's market, financials, customers, and risks to decide whether an investment holds up, drawing on sources like filings, broker research, and expert interviews. Running it through a purpose-built AI platform compresses the synthesis: a sourced market read, deal comps, channel checks, and a data-room scan for red flags, each traceable to the document it came from.