The full stack of modern data work.
Senior analysts and engineers, paired with AI-native tooling. Pick one capability or combine them into an end-to-end engagement.
Data & AI Strategy
A roadmap for where to bet on data and AI.
The starting point for a large organization. We assess current state, identify where the business will get the biggest return from data and AI investment, and sequence the work. The output is a roadmap of named bets, the metrics that will prove them out, and the delivery pathway for each one (context engineering, analytics engineering, or data engineering).
People
Build data literacy and AI fluency at every level.
Process
Find manual work that costs you time and automate it intelligently.
Platform
Choose, implement, and integrate tools that fit your business.
Adoption
Tools only work if people use them. Change management from day one.
- Context Engineering when the bet is a trustworthy AI chatbot or assistant
- Analytics Engineering when the bet is metrics the business can trust and extend
- Data Engineering when the bet is the platform itself, warehouse to BI
- Current-state assessment
- Value map: which bets, in which order, and why
- Roadmap with measurable milestones and named owners
- Platform selection and RFP support
- Change management and adoption plan
A roadmap with named owners, a value map, and the accelerator paths to deliver it.
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