accelerated ai
models in production, not in notebooks.
we pick the one workflow where a model moves a number you already report on — then build the data, the model, the agent, and the guardrails around it until it runs without us.
6 capabilities, one accountable team.
each capability can stand alone. most engagements combine three or four, staffed by the same senior people from start to finish.
ai strategy
a ranked backlog of use cases, scored on value, feasibility, and risk under the ai act.
data foundations
pipelines, feature stores, and lineage so every prediction can be traced to its inputs.
machine learning
forecasting, classification, and optimization models, evaluated against your baseline — not a benchmark.
agentic automation
llm agents with tools, memory, and approval gates that take work off queues end to end.
evaluation & monitoring
offline evals before launch, drift and quality alerts after — owned by your team.
governance
model cards, human-in-the-loop design, and audit trails mapped to iso 42001.
14 weeks from kickoff to handover.
a typical engagement, phase by phase. every phase ends with something running — never just a document.
map the workflow, measure the baseline, confirm the data exists.
a working model on real data, evaluated against the baseline.
pipelines, serving, monitoring, and the ui people actually touch.
runbooks, evals, and on-call move to your team.
what typically moves.
- python
- pytorch
- dbt
- snowflake
- databricks
- vertex ai
- azure openai
- anthropic
- langgraph
- mlflow
- kubernetes