Short, practical notes from the work we do: getting data ready for AI, migrating platforms, and shipping AI that holds up in production.
Natural-language analytics
It's rarely the model. It's usually ambiguous column names, missing metric definitions and no example queries. Before you tune instructions, write down what "revenue" or "lowest fare" actually means, document the tables, and give the space a handful of trusted SQL examples. Accuracy follows the semantic layer.
Migration
LLM-assisted conversion is good at the repetitive part: turning SSIS packages, stored procedures and ADF pipelines into Spark or dbt. It's weak at the parts that sink migrations: undocumented business rules, reconciliation and cut-over. Use it to speed up conversion, then spend the time you saved on testing.
Leadership
Month one: understand the data estate and the decisions people actually want to make with it. Month two: pick two or three AI use cases the data can support today, and fix the governance gaps they expose. Month three: put a team and a roadmap in place that runs without you.
AI agents
A demo is a prompt and an API key. Production means answers you can evaluate, guardrails you can audit, cost you can forecast and failures you can see. Budget as much for evaluation and monitoring as for the agent itself, and ground it in data you already trust.
Delivery
Most programmes fail on roles, not tooling. You need a solution architect owning the target state, a governance lead owning standards, and a delivery lead owning the plan before you scale up engineers. Staff the shape of the programme, not a headcount number.
Want the longer version of any of these? Ask us directly.