Three practices. One rope.
Everything here exists to produce one thing: time saved, errors reduced, throughput gained. Strategy & Org brings the team and the operating model, Applied ML builds the systems, and ModelOps keeps them current as the frontier moves — see it run live in the ModelOps demo.
Strategy & Org
AI adoption consulting
Your whole team actually using AI day to day, not just licensing it.
Organizational structuring for AI
The roles and reporting lines that AI work actually needs.
Team workflow integration
AI built into the tools your team already works in.
Forward-deployed engineering
Our engineers build alongside yours, then hand you the codebase.
AI safety and governance
Catch a bad release before your users or regulators do.
Applied ML
Agentic workflows
Multi-step work that runs itself and escalates only when it should.
Embeddings & chunking strategies
Making sure the AI finds the right information before it answers.
Document AI
Paperwork read by machines, so your people stop retyping it.
Model tuning
Teaching the model your domain so it performs like a specialist.
Applied machine learning
Patterns in your data turned into predictions you can act on.
ModelOps
Net-new capability assessments
What AI can now do for your business that it couldn't last quarter.
Eval-validated metric lifts
Upgrades ship only when your own benchmarks say they're better.
Efficiency and consolidation gains
The same work, done faster and with less to go wrong.
Model routing & token-cost governance
Frontier capability where it pays for itself; smaller models everywhere else.