Agentic workflows
Multi-step AI agents that plan, call your tools and verify their own output before a human ever sees it.
- —Task decomposition and tool-calling design
- —Human-in-the-loop approval gates
- —Deterministic fallbacks for regulated steps
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(a) Capabilities
We take responsibility for the whole life of a system: the discovery that justifies it, the engineering that ships it, and the monitoring that keeps it honest in production.
Multi-step AI agents that plan, call your tools and verify their own output before a human ever sees it.
Retrieval grounded in your own documents, with citations, per-role permissions and a measured accuracy budget.
Fine-tuned and orchestrated models wrapped in the interfaces your teams already work in.
Rule-based automation for the parts of a workflow where a model would be the wrong tool.
Pipelines, feature stores and deployment rails so models stay live in production, not stuck in a notebook.
Secure, cost-capped infrastructure with the observability needed to prove the system is running.
Engagement models
We map the workflow, the data and the cost of doing nothing. Two weeks, one report you can act on.
A working slice on your real data, in front of the people who will actually use it.
Wired into your stack with guardrails, authentication and rollback baked in.
We stay on the wire: monitoring, evaluations and monthly cost reviews.