Multi-step agents with grounded tools, audit trails, and human-in-the-loop checkpoints.
Capability
Generative AI & Agents
Generative AI is now a deployment problem, not a research one. We build the systems that put foundation models into production: agentic workflows, retrieval pipelines, finetuning jobs, inference infrastructure, and the governance layer your auditors will eventually ask about.
Components
What we build.
Each engagement assembles a different combination of these components, but the parts are stable.
Finetuning, distillation, and continual training on your operational corpus.
Hybrid retrieval pipelines that respect access control and citation requirements.
Self-hosted or hybrid inference — sized for cost, latency, and data-residency constraints.
Evaluation harnesses, prompt registries, model lineage, and policy enforcement.
Stack
Tech we deploy with.
The list is descriptive, not prescriptive — the stack meets the operation.
PyTorch vLLM LangGraph LlamaIndex Triton Ollama Postgres + pgvector Weights & Biases
Detail
How it deploys.
We start from the eval, not the demo. Before anything ships, we build the test set and scoring harness that defines “good enough” for your use case — so progress is measured, not vibed, and regressions are caught before users see them.
Agents are grounded and contained. Real tools, retrieval that respects your access control and citation rules, and human-in-the-loop checkpoints on anything consequential. Autonomy is granted where the audit trail and rollback justify it — never by default.
It runs where your data is allowed to live. Self-hosted or hybrid inference sized for cost, latency, and residency, with a governance layer — prompt registry, model lineage, policy enforcement — your auditors will eventually ask about. The hard part was never the model.
Domains
Where we apply this.
Engage
Start a project around generative ai.
Tell us the operational gap. We'll respond with the shape of the engagement within one business day.
Other capabilities