Your hardest data problems, solved.
AI-pilled engineers who identify the highest-value work, build the tools and agents to do it, and cross-train your team to own them long-term.
AI Enablement
Prove AI on one high-visibility pilot, then scale it.
The starting point for a team that wants AI to pay off, not just demo well. We pick a scoped piece of work with high visibility and build the custom AI tools and agents to take it on. That is the pilot. While we deliver it, we set your team up with the right AI architecture and identify the highest-value data and AI use cases to run next. You get a visible win early and a ranked list of what to build after it.
Pilot
Pick one scoped, high-visibility project and deliver it end to end.
Architecture
Stand up the models, context, tooling, and guardrails your team builds on.
Enablement
Train your team on the new workflow so the pilot is not the last win.
Use cases
Map and rank where data and AI will pay off next.
- Context Engineering when the next use case is a trustworthy AI chatbot or assistant
- Analytics Engineering when the next use case is metrics the business can trust and extend
- Data Engineering when the next use case is the platform itself, warehouse to BI
- Pilot selection: one scoped, high-visibility piece of work
- Custom AI tools and agents for the pilot, in use by your team
- AI architecture: models, context layer, tooling, and guardrails
- Hands-on enablement so your team can repeat the pattern
- Ranked backlog of high-value data and AI use cases
A visible AI win, a team equipped to repeat it, and a ranked list of what to build next.
Context Engineering
AI chatbots that get the right answer, not just any answer.
Off-the-shelf chatbots fail on real business data because they do not know your business. Context engineering is the work of transcribing tribal knowledge, table relationships, metric definitions, and the unwritten rules into shapes a model can actually read. We build that context layer, test it against real questions, and harden it before the bot ever ships to users.
Capture
Pull metric definitions, table relationships, and tribal knowledge out of the people who know.
Encode
Translate that knowledge into the semantic layer, glossaries, and sample queries the model can read.
Evaluate
Build a test suite of real questions with known answers. Measure accuracy, not just vibes.
Harden
Guardrails for out-of-scope questions, refusal patterns, and feedback loops for what slips through.
- Semantic layer authoring (metrics, joins, business logic)
- Sample-query library and few-shot examples
- Domain glossary and metric definitions
- Eval suite to catch wrong answers before users do
- Guardrails and refusal patterns for out-of-scope questions
- Connector setup to your warehouse and tools
A chatbot stakeholders trust because it cites the data and explains its work.
Analytics Engineering
Models your team can trust and extend.
High-quality data pipelines and transformations, built with technical expertise and AI-assisted development. Every model is tested, documented, and reviewable.
- dbt models and transformations
- Data quality tests and monitoring
- Semantic layer and metric definitions
- AI-assisted development workflows
Clean, tested models. A repo your team can own after handoff.
Data Engineering
Modern data stack, built end-to-end.
End-to-end solutions spanning infrastructure, data warehouse migrations, and modern analytics stack implementation. We pair senior practitioners with AI-native tooling so a small team ships the full stack at consulting-grade polish.
- Warehouse setup and migrations (Snowflake, ClickHouse)
- ELT pipelines (custom or managed)
- BI implementation (Omni, Hex, Looker)
- Embedded analytics for your product
A production data platform with decision-grade dashboards your team can extend.
Not sure which fits?
Most engagements combine two or three. The intro call is the fastest way to map your needs to a concrete plan.
Book a 30-min intro