ChronexaVENTURE CAPITAL · PRIVATE EQUITY · M&A
Associates spend their weeks logging inbound decks, reading CIMs and chasing portfolio companies for numbers. We build the layer that does that work: every deal captured and scored, every data room searchable with citations, every portfolio metric current, so the team spends its time on the decisions that make the fund.
Usually Affinity, DealCloud or Airtable, plus your data room.Your deal data stays in your tenant. Nothing confidential trains a public model.
Choose an action to see what it writes back across your systems.
01 / WHERE THE HOURS GO
These are the four places the work leaks most often. Most firms have at least two of them running right now.
Decks arrive by email, intro and event. Logging them is nobody’s job, so the fund cannot see its own pipeline, and good companies are missed twice.
Associates read contracts and financials line by line under exclusivity. The issues that matter surface late, when the deal is already priced.
Companies send numbers in different formats, late. By the time the pack is assembled, the quarter it describes is over.
Every quarter the same report is rebuilt from spreadsheets. It is slow, error-prone and the opposite of what LPs expect from a modern fund.
02 / WHERE AI PAYS BACK
Ranked by how fast they usually pay back. The free audit puts your own numbers in the last column.
| Use case | Today | With Chronexa | Typical impact |
|---|---|---|---|
| Deal-flow intake | Decks logged by hand, or not at all | Every deck parsed, scored against thesis and logged | 100%of deals captured |
| Deck & CIM parsing | Associates extract metrics manually | Metrics, team and terms extracted with page citations | 80%less first-pass reading |
| Diligence copilot | Data rooms read line by line | Questions answered across the room with sources | 70%less review time |
| Portfolio monitoring | Quarterly chase for numbers | KPIs collected, normalised and flagged continuously | Weeks → daysper reporting cycle |
| LP reporting | Rebuilt every quarter from spreadsheets | Generated from the portfolio record | 60%+less preparation time |
Typical results from systems we have delivered and comparable deployments. Your audit replaces them with your own numbers.
03 / SYSTEMS WE CONNECT
We connect what you already run first. APIs where they exist, MCP servers and careful automation where they do not.
04 / THE CONTROLS
The controls are designed in from the first workflow, not added after something goes wrong.
Built in your tenant. Confidential documents are never used to train public models, and access mirrors your deal-team permissions.
Every diligence answer cites the document and page it came from. If the room does not support a claim, the copilot says so.
The system captures, scores and summarises. Investment decisions, passes and term sheets stay with the investment team.
05 / THE ENGAGEMENT
Scope, what counts as working, and the price. Built in your own accounts, documented and exportable. The free audit comes first, and so does the first workflow.
RELEVANT WORK
A mid-market private equity firm was bottlenecked twice: associates reading entire data rooms by hand during diligence, and the team stitching portfolio reporting into a picture that was stale by the time it was assembled. We built a private diligence copilot and an automated portfolio-monitoring layer.
Data-room review time fell by about 70%, and the firm now has one current view of portfolio KPIs and signals instead of a quarterly reconstruction.
Read the PE diligence case study06 / START WITH ONE WORKFLOW
A strategy call covers your deal process, your CRM and your data rooms. You leave knowing where AI gives the team back the most time, and what it would take to prove it on your own pipeline.
You will be talking to the engineers who would build it, not an account manager.info@chronexa.io
Rather write it down first?
A FEW GOOD QUESTIONS
Yes, for the reading. A diligence copilot can search an entire data room and answer specific questions, such as which contracts have change-of-control clauses or how revenue concentration has moved, with the exact passage cited. It does not replace the judgement of the deal team; it removes the days of reading that come before it. In delivered work this cut data-room review time by about 70%.
Yes. We connect through their APIs to create companies and deals, enrich records and keep relationship data current. Many funds also run custom trackers in Airtable or Notion, and we build on those when they fit the way the fund actually works.
The system runs in your tenant with the permissions your deal team already uses. Confidential documents are not used to train public models, model providers are chosen for zero data retention where required, and every query is logged.
We collect packs in whatever format companies send, extract the KPIs, normalise them to your definitions and flag anything missing or inconsistent. Over time the companies can be moved to a simple submission form, but the system works with what arrives today.
There are no packages. The audit is free and so is the first workflow. After that, each build is quoted against the associate time and reporting effort it removes, and you approve the scope and price before work starts.