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Every AI conversation at your firm ends in the same place: the data.
Admin systems that disagree with each other. The same borrower spelled four ways. Reporting assembled by hand, again. One person who knows where everything lives. The firms whose AI works did the plumbing first. Nobody puts that in a keynote.
- The first trustworthy dataset lands in weeks; an 18-month transformation is how these projects die
- Diagnostic from $15,000, then a phased build at a fixed fee per phase
- Every number ties to the old way before anything switches over
- You keep the platform, the documentation and the standards
Where this runs: private credit & specialty finance and life, annuity & pension risk transfer.
Built from zero, twice.
An $8B+ alternative asset manager, employee twelve, no data team, valuations in Excel. Now at a private credit platform scaling from $450M toward $3B: consolidating the loan, factoring and fund systems into one governed platform with the reconciliation controls and permissioned LP reporting institutional capital expects.
For a more detailed track record, read the About page.
The tangle becomes a tree. Then every number ties.
The model. Firm, fund, vehicle, borrower, facility, collateral. Entity resolution so every counterparty exists exactly once, and pipelines with audit trails so every number can say where it came from.
The tie-out. Reconciliation controls prove the new numbers match the old before anything switches over. That is the whole trust story with a CFO, and it is the reason the platform gets used.
What it unlocks. Once the entities resolve: plain-English questions against governed data, live anomaly monitoring, agents that read documents into the record, and models with something trustworthy to learn from.
Already have a data team? Then you likely need architecture and standards, not headcount. Most engagements here make an existing team faster.
Phased, with a reconciliation gate at every step.
- Weeks 1–3Map
- Inventory of every system, spreadsheet and administrator feed in play
- Interviews with the people who are the database today
- Target data model: entity hierarchy, definitions, owners
- Reconciliation gaps ranked by dollar impact
- Quick wins picked to fund patience for the rest
- Phased plan, fixed fee per phase
- Weeks 4–10Build
- Pipelines from source systems into one governed store
- Entity resolution: every borrower, policy or counterparty exactly once
- First governed datasets, tied out to the old numbers before anything switches
- Data quality checks and lineage on every field
- Definitions and documentation written as we go
- OngoingRun
- Observability: freshness, breaks and reconciliations monitored
- Change control for definitions and new sources
- Your team trained to own it, or hired through Your First AI Hire
- Quarterly review of what to add next
Run the borrower test first if you like. The diagnostic starts at $15,000 and the first trustworthy dataset lands in weeks.
Book 30 minutes →Asked on most first calls.
Warehouse, lake, which vendor?
Decided by your workloads and budget. We take no fees from any platform, so the answer has no thumb on the scale. A $500M fund needs a sharp small stack, not a lakehouse cathedral.
How is this different from a data consultancy?
The person designing it has run this exact build inside a regulated fund, and stays accountable through a fractional retainer if you want continuity instead of a handoff.
Will the tool we already bought survive this?
Usually, and it starts working. Most monitoring platforms fail on the entity layer under them, not on their own features.
What does this cost?
The diagnostic that scopes the build is from $15,000, waived if it does not identify value worth ten times the fee. The build itself is phased, fixed fee per phase, priced at the end of the diagnostic so the number comes with a plan attached.