Acquisition intelligence
DiliPilot
A source-linked workflow for turning seller documents into structured spreads, reconciliations, candidate earnings adjustments, financing scenarios, and report preparation.
Focus
Financial diligence workspace
Build status
Deployed prototype
Demonstration
Sample data / synthetic workflow
Validation
Customer discovery and market research; no public traction claimed
The challenge
Small-business acquisition diligence is fragmented across incomplete documents, judgment-heavy normalization work, and financing assumptions that need an auditable review path.
The approach
AI extracts, classifies, and proposes against structured inputs. Deterministic services calculate financial outputs. A human reviews and approves reportable work.
- 01
Document manifest and missing-item checklist
- 02
Historical financial spread and reconciliation workflow
- 03
Candidate adjustment review and evidence trail
- 04
Sources-and-uses, debt-service, and liquidity scenarios
My ownership
As co-founder, Zayyan led customer discovery, market research, positioning, and workflow design, and contributed to the review-oriented product flow. This summary focuses on his work; collaborator contributions are not represented as his own.
Inspectable evidence
The published case study and product capture are the inspectable evidence currently available. No customer data or private diligence materials are exposed.
Current boundaries
DiliPilot is pre-revenue. It is a deployed prototype for diligence preparation, not a claim of completed transactions, production underwriting, or customer adoption.
Next
Continue hardening buyer review flows and source-linked export readiness.