★ 4.4
★ 3.5
★ 2.2
★ 4.5

We integrate your data to surface your biggest bottlenecks, deploy agents to automate workflows, and implement alongside you.
A deal arrives. DealSage scores it against your criteria, benchmarks it against every deal you have seen, and builds the model, all before your first call.
A deck arrives. DealSage pulls the metrics, benchmarks them against every company you have backed and the sector, and flags where the story bends, before the partner meeting.
A mandate lands. DealSage pulls the comps from deals you have run, rebuilds the model, and lays out the full relationship history, before the pitch.
A target surfaces. DealSage scores it against your thesis, benchmarks it against every company you have screened, and shows how the last similar one played out.
DealSage unifies the data scattered across your systems, shows where margin is made and lost, automates the reporting, and tells you what is coming before it lands.
Your firm’s knowledge is real. It’s just scattered, disconnected, and impossible to query.
One structured layer of entities, records and your own custom objects, that every app and every agent then runs on.
A foundation, then your custom objects, then the apps and agents you configure on top.
Consultants hand you a roadmap but won’t own the build. Vendors ship tools but don’t know how your business runs. Neither owns the outcome, or drives the change that makes it stick.

See how DealSage helps investment teams capture knowledge, automate workflows, and make better decisions.
Schedule a callNo. DealSage creates a persistent intelligence layer for your firm by connecting emails, calls, documents, CRM records, and deal activity into one system. AI agents can then operate using your firm’s complete context rather than a single conversation.
You can, and DealSage routes to them. The difference is context: a chat starts blank every time, while DealSage gives the model your firm’s full, structured history, so answers are grounded in your deals rather than the public internet.
Maths never runs through the language model. Every calculation goes through a code interpreter, and every extracted figure carries a citation back to the page or cell it came from, so you can check it in seconds rather than re-derive it. Validation checks flag anything that does not tie out before it reaches you.
We design and build it with you. Every engagement runs consult, design, build, implement, so agents and workflows are configured around how your team actually works. Setup takes longer than off-the-shelf software, and in return the system fits your process from day one.
No. We connect to what you have and you migrate at your own pace, if at all. For most CRMs we keep your existing interface running while the underlying data flows into DealSage, so nothing changes for the team day to day until you decide to switch.
We usually move in parallel with your IT and compliance review rather than waiting for it to finish. We are SOC 2 audited, and your security team can self-serve the review at our Trust Center, where the report is available under NDA along with our policies and control detail. We answer your questionnaire directly too. The earlier your security team is looped in, the less it delays the build.
Visit the Trust Center ↗Your data lives in your own tenant, encrypted in transit and at rest, with row-level access controls and SOC 2 Type II attestation. It is never used to train third-party models. Our Trust Center has the full picture: live control monitoring, policies, and the audit report under NDA.
Visit the Trust Center ↗We are not tied to one model. Different tasks route to different frontier models, one for reasoning-heavy work, another for parsing and extraction, and we swap in better ones as they are released. If you would rather run everything through your own Claude or OpenAI enterprise agreement, you can plug in your own API key and the usage runs through your account.
Private equity, venture capital, investment banks, corporate development teams, and the portfolio companies they back. Anyone whose edge depends on turning scattered deal knowledge into decisions.