The data engineering that makes AI honest and analytics defensible — warehouses, ELT pipelines, dbt, semantic layers, and the dashboards executives actually use to decide things.
AI features amplify whatever data quality you already have. We invest in the pipeline and modelling layer first because we've seen too many AI projects collapse on dirty data. Snowflake, BigQuery, ClickHouse, or Postgres — the choice follows the workload.
A data foundation product, finance, and AI features all draw from — without each team building parallel pipelines.
Concrete deliverables — not adjectives. Each engagement scopes which of these are in play and what success looks like for them.
Drawn from sales calls, not SEO filler. Want a question added? Drop it in the form on this page — we update from real enquiries.
Snowflake for enterprise governance and ecosystem. BigQuery when you're GCP-shaped or AI-heavy (Vertex). ClickHouse for high-cardinality analytics and observability data. Postgres when the data fits.
For any team modelling more than 20 tables, yes. Below that, plain SQL with version control is fine.
When the warehouse needs to push data back to operational systems (CRM, marketing). Census or Hightouch — both ship in production.
ClickHouse, Materialize, or RisingWave depending on the workload. We don't auto-prescribe streaming for batch problems.
Retrieval-augmented generation and LLM-powered features built for production — vector search, chunking strategy, evaluation, observability, and the guardrails that make non-deterministic systems defensible to legal, security, and finance.
OpenAI integrations for products that need the latest GPT model surface — function calling, structured outputs, embeddings, vision, and the Realtime API.
Claude API integrations for products where Anthropic's models earn the seat — long-context reasoning, code understanding, tool use, and computer use.
