We help teams move from AI experiments to production. From LLM agents and RAG copilots to automated workflows and decision systems — built with evals, observability and senior craft so it actually holds up in production.
Every engagement starts with the business outcome and works backwards into the right AI tech — not the other way round.
Eliminate manual copy-paste across tools — ticket triage, document extraction, lead enrichment, content ops — with pipelines that combine deterministic logic and LLMs where each helps most.
Turn fragmented signals into clear recommendations. Forecasting, anomaly detection, ranking and prioritization — surfaced where your team already works.
Tailor experiences for each customer without hiring an army. Recommendations, dynamic copy, smart onboarding and assistive UX that adapts to context.
Connect Notion, Slack, Drive, GitHub, Zendesk and your data warehouse into one searchable assistant — with access control and source citations on every answer.
Avoid AI theatre. Get a focused roadmap of the AI opportunities that move metrics, scored on impact vs effort — with the team to execute the winners.
Each is a real engagement type — not a vague capability. Tap into the linked service page for delivery details and tooling.
Agentic workflows that do real work — read your docs, call your APIs, write back to systems of record. LangGraph or pure-code orchestration, tool-use everywhere, human-in-the-loop gates on anything irreversible. Built with evals before launch and tracing once it's live.
Internal and customer-facing assistants that actually know your company. Retrieval-augmented generation on your real knowledge base, hybrid semantic + keyword search, per-tenant access control and source citations on every answer — not vibes.
Where models genuinely beat heuristics, we build them. Forecasting, classification, anomaly detection and ranking — wrapped in pipelines you can retrain, monitored for drift, and integrated into the surface where the decision actually happens.
Stitch fragmented data sources into a single trustworthy view, then layer AI on top to extract, classify and route automatically. Built on n8n, Temporal or pure code depending on your stack — with retries, idempotency and audit logs baked in.
Six concrete reasons teams pick us when AI has to ship to real users — not just impress a board deck.
We ship behind feature flags every week — so AI features reach real users sooner, not after a six-month research project.
Every AI feature ships with evals and a regression suite. You'll know when the model drifts — before customers do.
From assisted support to personalized onboarding — AI surfaces that feel like a teammate, not a chatbot.
Idempotent pipelines, retries, dead-letter queues and tracing. AI workflows that survive flaky APIs and noisy inputs.
We pick the right model for the job — small + distilled where it works, frontier where it has to. With cost monitoring and budgets per workflow.
We design the platform once, well — model providers, eval harness, observability and access control — so adding the next AI feature is a week, not a quarter.
Every AI engagement follows the same outline — adapted to your stack and risk tolerance. No black boxes, no surprise pivots.
We map the workflows, data, constraints and stakeholders. Output: an opportunity backlog scored on impact vs effort, and a clear 'don't do this yet' list.
We pick the first 2–3 projects, decide build-vs-buy per opportunity, and write the AI architecture that the rest of the work hangs off — model providers, evals, observability, access control.
We design the prompts, retrieval, tools, schemas and guardrails — and the eval dataset that defines 'good enough to ship'. Reviewed with you before a line of feature code is written.
Weekly sprints behind feature flags. We integrate into your real systems — auth, data, APIs — not a sandbox demo. Every step shipped with tests and observability from day one.
We run the eval suite on every change, tune cost vs quality vs latency, and pilot with a small user cohort. Nothing scales to all users until evals and pilot metrics agree.
Production launch with monitoring, drift detection, fallbacks and a clear on-call runbook. Optional retainer for tuning, new features and adapting to new model releases.
A 30-minute call is enough to know if it's a fit and what the first 2–3 weeks would look like. We'll send a written proposal within 48 hours.