Data and AI engineering, from raw source to agents in production.

Without replatforming. We model meaning over the warehouse you run, Snowflake, Databricks, Fabric or BigQuery, certify the metrics with the business owners who define them, and make that layer the query path agents and BI use. Retrieval then runs against governed definitions rather than raw tables, which is what makes citation-grounded answers possible.

By finding which layer is failing before choosing any technology. An architecture assessment reviews the estate against four layers plus governance running across all of them, names the gap blocking production, and costs the options. Two to three weeks, ending in a written architecture position and a costed shortlist. In our experience the blocking layer is data or context at least as often as it is anything to do with models.

With enforcement outside the model, not with better prompts. Retrieval is scoped to a governed semantic layer, so the model answers from certified definitions rather than raw tables. Output is citation-grounded, with every claim traceable to the records behind it. A validation layer tests groundedness and catches invented information before it reaches a user, judging the path the agent took as well as the answer. Agents are certified above a 95 percent evaluation threshold before they receive traffic.

By treating cost per operation as a design target rather than a monthly surprise. Warehouse cost and performance tuning is part of the foundation build. Model calls run in-VPC with no egress where architecture and data classification allow. Spend is metered per user against individual keys rather than pooled at the account. Retrieval is scoped through the semantic layer so context windows carry what matters and nothing else. Autonomy is scaled to the outcome, because token, orchestration and verification cost all rise with it.

Through declared contracts and a certification gate, not through prompts. A master agent routes intent to certified sub-agents, with certification status read from the metadata catalog before an agent can receive traffic. Agents share one contract with a small number of typed roles, analyse, retrieve, graph and act, selected by capability mapping. Interoperability runs on MCP, A2A, agent cards and registries, so an agent added later does not require the existing fleet to be rewritten.

Our Digital Engineering practice builds the products, applications and experiences the intelligence runs inside. A governed semantic layer nobody can reach is shelfware; an application with no trustworthy data behind it is a faster way to be wrong. You engage one team and one accountable lead whichever side the work sits on.