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“Why did margin move last month, and what should we do about it?”

Answers from your own data, then agents that act on them.

A dashboard tells you what sales were; conversational AI tells you why, and a reasoning agent what to do next month. We build all three on a warehouse your team owns.

What we build

Your systemsWhat we build
OrchestrationData quality testsAccess controlLineage and provenanceVerified queries and evals

Ask in plain language, get the answer with the definition it used, the capture time and the sources.

Why did margin move in September?
Delivery cost rose 4 points on two accounts. Revenue held.
margin v3 · finance.ledger · delivery.tasks · 06:00 today

Our pipeline, asked where the next $1M is.

On 11 October we asked Ask AI where $1M of new revenue was for the quarter. It read the CRM, delivery, emails and calls, ranked the deals that mattered with odds and sources, and said it could not read receivables.

Ask AI, 11 Oct 2026.

Where can we find $1M of new revenue in the next quarter? Rank the opportunities and say why.
Reasoning · 0 steps

Out of the box, AI answers questions from your data correctly about 80% of the time. Reporting needs 99%.

Ask a model for last month's sales a hundred times and twenty answers will be wrong, and you will not know which. So we engineer determinism per use case, and we test it.

Verified queries

Thirty to forty question-and-answer pairs per domain, written with the people who ask them. The agent is trained toward them.

Evals

Every change to the semantic layer re-runs the verified set. Drift is caught before a user sees it.

The feedback loop

Thumbs down from a business user becomes a case for the data team, then a new verified query. The system improves with use instead of needing maintenance.

Provenance

Every number shows where it came from and when. If a view errors, the answer says so instead of guessing.

Case studies

/ Hedge fund, public equitiesNew York

A hedge fund kept its Excel models and got a data platform underneath them

Phases 0 to 3 delivered
Before
Hundreds of Excel models, no history
After
Every model read nightly, dated
Data intelligence
/ Property and casualty insuranceSoutheast US

A P&C insurer went from stored procedures to a warehouse, then to AI across the company

Data platform in production
Before
Logic buried in SSIS jobs, one data engineer
After
Fivetran, Snowflake and dbt in production, four hires, Claude rolled out
Data intelligence
/ Healthcare, patient intakeNew York

A patient-intake company prototyped an ontology: 105,000 conditions, one answer per question

Prototype delivered
Before
Four codes for one disease
After
105,000 conditions mapped to one concept each
Data intelligence
/ Consumer products, manufacturingGeorgia, US

An outdoor-cooking manufacturer asked its warehouse 'what changed this week' and got an answer with sources

Pilot delivered
Before
Weekly business review built by hand in Power BI
After
A semantic layer and an agent answering the MVP questions
Data intelligence
/ Consumer products, manufacturingGeorgia, US

A consumer brand's data lead turned a list of AI ideas into a budget the CFO could sign

Roadmap presented
Before
An Excel backlog of agent ideas, no AI owner, no budget line
After
A ranked roadmap, a first ask of about 15% of it, an AI vertical under the data lead
AI strategy

The other three practices

AI enablementA skills platform so every team automates its own workAgentic engineeringEngineers embedded with your team, building the software your company runs onAI strategyA forward-deployed AI strategist, a day a week, who finds the AI projects and defines them

Own the AI that runs your company.

Built inside your company and yours to keep. Thirty minutes with a practice lead.

Book a first call