Generative AI consulting and development — from idea to working system, end to end.

It depends on the problem, not the technology. Knowledge access → RAG. Multi-step workflows that need to act → agents. Tone, format, or domain reasoning → fine-tuned or custom LLM. Reliability and cost discipline at scale → an eval platform on top. The two-week discovery sprint matches the right service to your problem with numbers, not a sales pitch.

Yes. Most production GenAI systems combine two or three of our services — RAG for grounding, agents for workflows, evals for safety. We scope the bundle in discovery and price the full roadmap, not just the first stage, so you know what twelve months looks like before you commit to week one.

Discovery is a paid fixed-scope sprint ($15K–$25K, two weeks). Build phases are quoted on fixed scope after discovery — a single-service pilot runs $40K–$90K, a multi-service production rollout $150K–$400K depending on data, integrations, and eval rigor. Ongoing run-the-system support is monthly retainer based on volume.

That’s the most common path. Most clients start with a single high-value use case (a RAG copilot or an agent), ship to production in 8–12 weeks, and add the next service once that one is paying back. We design the first system so the second one plugs in without a rewrite — shared evals, shared data layer, shared observability.

In-tenant deployment on your cloud (AWS Bedrock, Azure OpenAI, GCP Vertex, or on-prem open-weight models), zero-retention API configurations, PII redaction at ingest, and full audit trails as defaults. We design for DPDP, HIPAA, SOC 2, and GDPR from day one and sign DPAs/BAAs upfront. Your data never trains anyone’s model.

We’re model-agnostic and bench 2–3 candidates against your real eval set in week one. Typical picks: Claude or GPT for reasoning-heavy work, Gemini for long-context tasks, Llama or Mistral for cost-sensitive high-volume workloads. We re-evaluate every quarter as new models drop — and we never lock you into one provider.

Three layers, on every system we ship: source-grounded retrieval with citation enforcement, an automated eval harness that runs on every release with hallucination as a release gate, and human-in-the-loop checkpoints on high-impact actions. We measure and report the rate weekly — not just claim the feature.

Three engagement models: fixed-scope project delivery (clear scope, fixed price), dedicated pods (4–8 engineers, ongoing), or staff augmentation (senior engineers embedded in your team). Every pod includes an ML engineer, a backend engineer, a prompt/evals specialist, and a solutions architect. We tell you which fits in discovery — no overselling either way.