AI where it actually saves hours or closes deals.
LLMs and agents are not a strategy on their own. We build the parts that earn their keep — internal automation, customer-facing copilots, and the unglamorous evaluation work that keeps them honest.
Where we usually start
- AI agents and copilots. Domain-grounded, scoped to a real workflow, with the guardrails to ship them to customers.
- RAG and knowledge systems. Document ingestion, retrieval, evals, and the boring data-quality work that decides whether the answers are useful.
- Workflow automation. n8n, Temporal and custom workers replacing manual ops — escalation, scheduling, reconciliation, classification.
- Model evaluation and guardrails. Eval harnesses, regression suites, output validation, prompt versioning. The work that makes AI predictable in production.
- Voice, vision and multi-modal. Where the input or output isn't text — calls, images, video, structured documents.
How we think about it
The gap between a working demo and a system you can put in front of customers is much wider than most teams expect. We spend more time on retrieval quality, evals and failure modes than on prompt-writing. If a use case can't survive an evaluation harness, it shouldn't ship — we'll say so.
Tools and providers we use
Anthropic and OpenAI are the default for general-purpose models. Mistral and open-weights for cost-sensitive or on-prem workloads. pgvector or dedicated vector DBs depending on scale. Temporal for long-running orchestration. We're provider-agnostic on the model layer — we'll pick what fits the workload, not the marquee.
Engagement shapes
- Discovery sprint — two weeks to map use cases, evaluate which are worth building, and produce a written plan.
- Build engagement — fixed-bid or T&M, depending on how settled the requirements are.
- Managed AI practice — ongoing improvements, evals and on-call once the system is live.