Pith turns the data room, vendor docs, and market reading you do under a deal clock into a cited reading memory per deal — auto-tagged to the target, every claim linked to its source, briefable for the investment committee.
A tech-DD advisor reads at volume under a tight deal clock: the data room, vendor and architecture docs, market and competitor reports, security advisories and CVE histories, cloud-spend exports, engineering-team and velocity signals, open-source and licence disclosures, the target's AI and data strategy. The deliverable is a findings memo the investment committee can act on — and every line in it has to be defensible back to a source, because a contested fact can move the price or kill the deal. That reading scatters across tabs, downloaded PDFs, and a notes file that goes stale the day the data room updates. Pith makes the per-deal knowledge layer first-class. You bookmark while you read; Pith tags each save to the right deal (auto), builds a cited wiki of the target, its tech stack, and its market where every claim links back to the source it came from (auto), and assembles a briefing for the IC (on demand). PDFs from the data room come in via OCR; semantic search finds the passage; source-conflict detection flags it when the data room contradicts a public or market source — which, on a tech-DD, is exactly where the real findings live.
What changes for you
Scenario 1
A per-deal reading memory of the target's stack
From the kick-off, you read into the target: the architecture overview, the cloud bill, the CVE history and breach disclosures, the engineering-team and hiring signals, the open-source and licence inventory, the competitor teardowns. Pith auto-tags every save to the deal and builds a cited wiki organised by concept — scalability, tech debt, security posture, cloud spend, IP and licence risk, AI strategy. Two weeks in you have a sourced picture of the target's stack instead of a folder of links, and a PDF dropped from the data room is OCR'd, searchable, and tagged like everything else.
Scenario 2
Brief the IC with findings that carry their citation
When the deal reaches the investment committee, your target wiki already organises the reading by concept and every claim links back to the filing, advisory, or data-room document it came from. Generate a briefing — text plus a podcast-style audio version for the commute — and walk in current on what the diligence found and where each finding is grounded. When a partner asks 'where's that CVE number from?', the source is one click away, not a half-hour back through the data room.
Scenario 3
The data room contradicts a market source
The vendor's own deck claims a microservices architecture; an ex-employee teardown and a market report describe a monolith mid-migration. Pith's source-conflict detection flags the contradiction the moment both land in the deal's reading memory, with both sources attached. You surface it as a finding for the IC — with the conflicting citations side by side — instead of it slipping through because two documents said different things three weeks apart.
Founder's note
I'm a tech consultant, not a deal lawyer, and I built Pith because I could never find — or source — what I'd read when it mattered. Tech due diligence is that problem at its sharpest: a mountain of reading under a deal clock, and findings that have to survive an investment committee tearing at them. So I built the layer that keeps the reading cited, scoped to the deal, and ready — where every finding traces back to the document it came from, and a contradiction between the data room and the market doesn't quietly slip past.
FAQ
How does Pith handle confidentiality and the data room?
Bookmarks and wiki pages are workspace-scoped to your firm and tagged to the deal, isolated at the database level. Data lives in Frankfurt, Germany — EU-only residency — and we never train models on your content. Data-room PDFs come in via OCR and are treated as private to your workspace; apply your engagement's NDA and clean-team policy to what you save, exactly as you would with any tool that touches the data room.
How is this different from Notion or NotebookLM?
Notion makes you author and maintain the page by hand — a curation tax you don't have time for on a time-boxed deal. NotebookLM works in batch uploads, so you re-upload every time the data room moves. Pith captures continuously from your reading flow, auto-tags each save to the deal, and keeps the target's cited wiki current automatically — and it flags source conflicts, which a notebook won't.
Does it work for a solo advisor, or only a deal team?
Both. A solo advisor runs a personal workspace with one tag per deal and gets the cited wiki, auto-tagging, OCR, and the IC briefing with no team to manage. A deal team shares a workspace so everyone sees the same cited target wiki, and you can query either from Claude or ChatGPT over the hosted MCP server, with citations.
What happens to the deal's knowledge after close — or if it dies?
Archive the deal. The cited wiki and bookmarks stay accessible for the record and drop out of your active filters, and you can export the deal's knowledge as Markdown or JSON for your files. Reactivate any time — useful when a passed deal comes back round, or a portfolio company you diligenced needs a second look.
Can I share findings with the investment committee?
Yes — signed-URL shares with time bounds and revocation. The IC sees a read-only version of the target wiki or the briefing, with the citations intact, so they can open the source behind any finding themselves without an account or a copied-out deck.
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Last reviewed: 9 June 2026