Solutions
What we build
Production AI agents for evidence-heavy operations, the data ecosystems underneath them, and the operating change that makes them stick. Four to six weeks to a working system.
01 / Back-office AI agents
The work between the documents and the numbers.
Finance and operations teams spend thousands of hours validating outcomes against contracts, bordereaux, loss runs, and correspondence. We build agents that do that work — and prove it was done right.
01
Ingest
Uploads and normalizes disparate document types and data sources.
02
Reconcile
Compares values across sources and completes calculations as needed.
03
Verify
Discovers errors, prevents them, and scores its own confidence.
04
Communicate
Reports status and results to every participant in the chain.
Every input funnels through one serial pipeline. Sprinkling AI into a step doesn’t change the shape — it’s the same in-to-out workflow.
The agent is a network, not a pipeline. Deliverables consolidate, and work that needs no judgment passes straight through.
Where it applies
Premium & claims audit
Operations teams in this market spend 200+ hours a quarter tying premiums and claims back to contract documents. The agent reads everything as it arrives; people decide the exceptions. One group's quarterly audit time fell 75%.
Bordereaux ingestion & reconciliation
MGAs report in every shape a spreadsheet allows, and building bordereaux by hand takes 3–10 days a month. The agent ingests, normalizes, and reconciles them in hours — flagging only the rows that need judgment.
Commission reconciliation
Manual commission work leaks 3–8% across the specialty market. The agent recalculates every statement against the producer agreement and surfaces each variance with its evidence attached.
Claims intake & reserving
First notice of loss arrives as documents. The agent builds the claim record, routes triage, and drafts reserve estimates with confidence scores attached. Adjusters decide.
Also: reserves & actuarial projections · money movement · compliance reporting · intercompany reconciliation
What it changes
Revenue leakage
Minimized — and over-reserving with it.
Regulatory exposure
Fewer penalties and audit failures, with trails built in.
Fraud
Ghost claims and premium washing surfaced, not missed.
Misstated financials
Flagged for correction before they reach the statements.
Audit stops being a quarterly event and becomes a property of the system — always on, with people on the exceptions.
Lessons from the front lines02 / AI-ready data ecosystems
Fix the layer your AI depends on.
We design data ecosystems on a producer/consumer model — every store has one canonical writer, every product is cataloged with lineage, and unstructured documents become usable AI inputs.
160+ warehouses
Curated, governed data products
Manual, paper-driven governance
Tool-generated catalog and lineage
People-driven ingestion
Automated ingestion and curation
Use-case-driven builds
Producer/consumer self-service
Unstructured data unusable
An AI-ready document foundation
In delivery now for the Defense Health Management Systems (U.S. Dept of War), and documented in our Snowflake-based insurance data ecosystem work.
Read the architecture03 / AI operating model & org design
Make the change hold.
Funding decisions, kill criteria, ownership, and the organization around the system. One healthcare software org redesign cut $75M in annual cost and took new-system definition from years to a few months.
What changes after launch
Leaders can govern the work
Funding logic, sequencing, and kill criteria are explicit.
Teams know how exceptions are handled
Review paths, thresholds, and reporting are defined before go-live.
The system has owners after launch
Runbooks, handoffs, and operating cadence do not depend on the project team.
Executive & leadership coaching
The principals work directly with executive teams whose organizations are absorbing AI — the same work we did inside GEICO, Citi, Microsoft, and Amazon.
Executive AI literacy
Org design for AI-native operations
Change management and engineering culture
04 / How we build
The posture your auditors will ask about.
Built in your environment
Systems run inside your cloud account. Critical documents and financial data never leave your perimeter.
Controls as architecture
Confidence floors, human review on exceptions, decision traces, and fallback paths — designed in, not added later.
Compliance fluency
SOX-ready audit trails, HIPAA security and privacy mapping, DoD overlay experience, full lineage, infrastructure as code.
Cost discipline
Token economics are engineering. We have seen systems carrying 3× more LLM cost than the work requires — ours don't.
Start with the workflow.
A strategy session clarifies what should be funded, what the highest-value workflow is, and what a four-to-six-week proof would demonstrate.
30 min · with a managing partner · no deck