Post-Acquisition Integration Tracker
An NLP and ML layer that reads the recurring integration artifacts — board decks, weekly status reports, ops dashboards, strategy memos — extracts integration-milestone status, predicts timeline slippage, and surfaces missed synergies before board prep makes them visible.
- Engagement
- 6–10 week build · ongoing per integration
- Built for
- Operating partners · Integration leads
Post-close integration runs across HR, finance, product, and ops — each with its own status reports, each with its own definition of 'on track.' Operating partners discover slippage when board prep starts, three weeks after the slippage was visible in the docs.
What this is
An NLP and ML layer that turns the existing post-close documentation flow into a real-time integration signal. Three layers:
- Document ingestion. Per-integration document feed — board decks, status memos, ops dashboards, strategy artifacts. Connected to wherever the portco keeps them (SharePoint, Notion, Google Drive, etc.).
- Milestone extraction. Per-workstream milestone status pulled from natural-language updates. Where the team uses structured trackers, those feed in too; the NLP layer fills the gap where they don't.
- Slippage prediction. Trained on prior integrations — language patterns that preceded slippage versus ones that didn't. Surfaces high-risk workstreams weeks before they show up as actual delay.
How it's built
Document AI for layout-aware extraction (LayoutLM-class), embedding models for status-language patterns, classification heads trained on labeled prior-integration data. The "trained on prior integrations" piece is shaped per fund — we start with our base model and fine-tune on the fund's historical integrations as that data accumulates.
What you get
- The weekly operating-partner summary, by workstream.
- Slippage-prediction alerts with the underlying language pattern that triggered them.
- The document corpus indexed and queryable — when the partner asks "what did we last say about pricing strategy?", the answer is one query.
- A retrospective dashboard per integration — how predictions tracked against outcomes, so the model's calibration is visible.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 6–10 week build · ongoing per integration
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Private Equity Suite
Portfolio Intelligence Platform
A unified portfolio platform: ETL pipelines from each portco system (Excel, QuickBooks, NetSuite, Sage, Stripe) plus third-party data (Preqin, Bloomberg), normalized to a single schema, exposed through cross-portco dashboards with covenant tracking and liquidity alerts.
- Same suite · Private Equity Suite
Portfolio Forecasting Engine
Predictive forecasting models per portco — quarterly cash-flow and EBITDA projections trained on the portco's internal financials and sector-comparable data, with structured scenario planning (cost-cut sensitivity, price-elasticity scenarios, demand-shock testing) that feeds straight into the fund's valuation model.
What buyers ask about this one.
Why NLP instead of just a project-management tool?
Project-management tools work when everyone uses them consistently. Post-close, the portco's existing artifacts are the truth — board decks, status memos, ops dashboards — and asking the team to also keep a PM tool current creates a parallel set of records that drift. The NLP layer reads what already exists, which is the truth.
How does the slippage prediction actually work?
The model is trained on prior integrations — milestone-status language patterns that preceded slippage versus ones that didn't. 'Slight delay,' 'awaiting input,' 'pushed to next cycle' — these phrases have predictive content. The model surfaces high-risk patterns three to four weeks before they typically materialize as visible slippage.
What's the operating partner's day-to-day experience?
A weekly summary email: which workstreams are on track, which are showing leading indicators of slippage, what synergies look at-risk. The summary links into the source documents so the operating partner can drill in. The point is to make the integration legible without making it heavy.
Can it tell us when a CEO is about to leave?
It can surface the pattern that often precedes it — language shifts in their written updates, meeting cadence changes, document-authorship patterns. It can't predict the conversation that hasn't happened yet, but it can surface signals worth talking to the CEO about. Used responsibly.
Pricing?
Per integration. Discovery call covers the document corpus and the operating-partner cadence.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026