Claude in production.
Eval-first by default.
We are an Anthropic build partner. We engineer agent fleets, MCP servers, RAG pipelines, voice agents and multimodal features that pass legal review and survive real customers.
Six concrete deliverables.
Every AI & Agents engagement maps to a specific deliverable below. We commit to it in the SOW, demo it weekly, and you own the result.
Agent fleets
Multi-agent Claude systems with handoff, escalation to humans, structured outputs and complete audit logs.
AI & AgentsMCP servers
Bespoke Model Context Protocol servers. Auth, rate limits, observability, kill switches, version pinning.
AI & AgentsRAG pipelines
pgvector, Pinecone, Weaviate, Turbopuffer. Hybrid sparse + dense retrieval. Citations on every answer.
AI & AgentsVoice agents
Vapi, Retell, Twilio Voice. Real-time barge-in, function calling, instant escalation to a human.
AI & AgentsEval-driven CI
Golden sets in version control. Pass-rate gating on every PR. Regression alerts in Slack.
AI & AgentsSafety & guardrails
Prompt-injection defense, output filters, red-team checklists, content-policy alignment.
AI & AgentsThe tools we reach for.
Solid line: what we use every day. Dashed line: what we reach for when the brief justifies it. We will work in your stack if you have a strong reason; otherwise these defaults serve us well.
Four steps. Real demos every Friday.
From signed SOW to first demo is one week. No discovery loops that bill for months without showing software. No silent stretches between status decks.
Discovery
One 60-min call. We define the eval before the prompt. Two-week paid discovery for fuzzy scopes.
Prototype
First working agent + first eval, end of week one. Demo on Friday.
Productionize
Tool design, guardrails, observability, evals in CI. Staging with real data.
Launch + iterate
Canary, gradual rollout, post-launch eval review. Continuous tuning on retainer.
The questions buyers ask first.
Do you actually use Claude in production yourself?
What is MCP and why does it matter?
How do you handle hallucinations?
Do you do fine-tuning?
Ship the agent.
Skip the demo loop.
A senior AI engineer reads your brief and replies within one business day with concrete next steps. Usually faster.
The Hivemind agent took our support backlog from days to seconds. CSAT held at 4.7. The team wrote evals before they wrote prompts and that is why it actually worked.
Quick answers.
The questions buyers in this service ask in week one.
Do you only build on Anthropic Claude?+
Claude is our default for production agents because of tool-use quality, structured output, and the MCP ecosystem. We also ship on OpenAI, open-weights (Llama, Mistral) where data residency or cost demands it.
How do you evaluate an LLM feature before shipping?+
Promptfoo and Inspect AI for offline. Custom regression harnesses wired to CI. Judge-model evaluation with calibrated thresholds. Real-customer-trace replay for end-to-end.
Do you use RAG or fine-tune?+
Both. Start with prompt engineering + retrieval. Add fine-tuning when latency, cost, or behavior cannot be achieved otherwise.
How do you handle hallucinations in production?+
Structured generation (JSON mode), grounding constraints, output validators, retry-on-format-failure, human-in-loop for high-stakes paths, and content moderation guardrails.
What does an embedded AI engagement look like?+
2 to 4 senior AI engineers + product designer. Discovery week. Eval harness shipped before features. Weekly demo. Standard month is $32k-$85k.