AI in production, not in a slide deck.
RAG, copilots, agents, and ML features shipped to production for B2B teams. We build with the model you should use (not the one with the loudest marketing), instrument every inference for audit, and design for cost + latency from day one.
Most AI projects fail in the hand-off from prototype to production. We build for the second half: cost monitoring, latency budgets, prompt versioning, evaluation harnesses, fallback paths, and audit logging — alongside the feature itself.
- ·Architecture document with model selection rationale (cost / latency / accuracy trade-off)
- ·Production system with retrieval, prompt management, evaluation harness
- ·Cost + latency dashboards per route
- ·Prompt versioning with A/B testing capability
- ·Eval suite (regression tests for prompts + outputs)
- ·Fallback paths for low-confidence outputs
- ·Audit logging compliant with your data residency requirements
- ·Documentation + handover for your engineering team
- ◇Anthropic Claude, OpenAI, Google Gemini, Mistral, Llama (model providers)
- ◇Cloudflare Workers AI + Groq (FREE / cheap fallback)
- ◇Voyage AI / OpenAI / Cohere (embeddings)
- ◇pgvector / Pinecone / Weaviate (vector store)
- ◇LangChain / LlamaIndex / Vercel AI SDK (orchestration — used selectively)
- ◇Helicone / Langfuse / PostHog (LLM observability)
Use case definition, success metrics, acceptable hallucination tolerance, latency budget, cost ceiling.
Smallest end-to-end that proves the use case. Demo to stakeholders. Decision: build or kill.
Retrieval, prompt management, observability, evaluation. Each component independently testable.
Roll to production, monitor evals + cost + latency, tune prompts and retrieval.
- ◆Data residency for prompts + completions
- ◆Audit logging for regulated industries
- ◆Prompt scrubbing for PII
- ◆Tenant isolation for multi-tenant AI features
- ◆Vendor due diligence for AI sub-processors (DPDPA + GDPR)
Eval Suite + Production Dashboard — a versioned test set that runs on every prompt change with pass/fail thresholds, plus a Grafana/PostHog dashboard showing cost-per-route, latency p50/p95, and eval score over time. Lets you ship prompt changes confidently.
Which model providers do you use?+
Whichever is best for the use case. Claude for nuanced text. GPT for tool use. Gemini for long context + multimodal. Llama-via-Groq for speed + cost. We do not have vendor lock-in.
Will the AI features leak my customer data to the model provider?+
No — we use enterprise tiers with no-training clauses, route to data-residency-compliant providers, and instrument PII scrubbing in the prompt pipeline.
How do you handle hallucinations?+
Domain-grounded retrieval, confidence thresholds, human-in-the-loop for high-stakes outputs, instrumented evaluation, and explicit fallback messages instead of guesses.
Can you fine-tune?+
Yes — but we usually find that prompt engineering + retrieval gets 80% of the way for 5% of the cost. We fine-tune when the task is narrow, high-volume, and has a clean training set.
Is this cheap or expensive?+
Depends on volume + model + retrieval strategy. Typical B2B feature: $200-2000/mo in inference. We design for cost from day one — not as an afterthought.