From the engineering team
Practical insights on building AI systems that scale — no filler, no marketing fluff.
The 200ms voice latency budget — where every millisecond goes
A frame-by-frame breakdown of a sub-second voice agent's turn budget. STT, network, LLM, TTS — and the surprising places we've shaved 80ms.
Your eval set is your spec — write it before the prompt
Why the most expensive AI mistake we see at customer engagements is teams tuning prompts before they've written the regression test that defines 'right'.
How we design multi-agent systems for production, not demos
The orchestration patterns that survive contact with real customers — and the demo-ware that doesn't. Drawing on a year of LangGraph in production.
Where RAG stops being RAG and starts being a search problem
After two dozen RAG deployments we've stopped calling it RAG. Here's the search and retrieval stack that actually works in production.
Shipping a real MVP in 14 calendar days — and why most teams can't
The pre-engineered scaffolding that makes a 14-day MVP possible. Auth, billing, RBAC, audit, deploy — already done before kickoff.
The case for boring infrastructure under interesting AI
Postgres, Redis, Temporal, Terraform. Why we pick technology that will be running in five years over the framework trending this quarter.
Voice AI compliance: what HIPAA actually requires (and doesn't)
A field guide to the voice AI compliance questions we hear most often from healthcare CIOs — and the misconceptions to leave behind.
Operating agentic systems: the on-call surface no one warned you about
Agents introduce a new category of incidents — drift, runaway loops, tool-use failures. Here's the runbook we've evolved over a year.
Vendor or build: an honest decision tree for AI features
When to buy OpenAI's stack as-is, when to wrap, when to fork. The framework we use with CTOs every week.
Ready when you are
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