Production AI engineering since 2010.
Design judgment before code. Live systems stabilized. Experienced architects close to the work.
Production-Ready
AI Agents
A Developer's Guide to Building Scalable, Reliable and Observable AI Agents
What makes this urgent
A system is straining. A decision is blocked. The cost of wrong architecture is becoming visible.
A promising AI initiative feels fragile
Latency, retry debt, observability gaps, weak evals — surfacing now that the system is moving to production.
Need: honest failure-mode map. Credible path back to trust.
High-stakes build. Wrong design is expensive.
Should this be agentic? What governance belongs in the architecture? Which delivery path before effort compounds?
Need: tighter design path. Explicit tradeoffs. Rationale leadership can defend.
Good team, but architecture review is missing
Key architecture calls without challenge around control boundaries, sequencing, reliability, or operating cost.
Need: stronger review discipline. Fewer months lost undoing week-one decisions.
What this looks like
Autonomous workflows. Knowledge infrastructure. Streaming data platforms.
Autonomous Content Engine
Multi-model pipeline with research, generation, Pydantic validation, diagrams, and CMS publishing guarded by human review points.
Autonomous PPC Engine
Live signal detection, creative generation, and page deployment loops for paid acquisition systems under constant iteration pressure.
Multi-Model R&D Platform
Adversarial multi-model workflows for research, synthesis, and structured output where reviewability matters as much as speed.
Competitor Intelligence Agent
Multi-agent research workflow that cut manual analysis from hours to minutes while preserving structure and decision traceability.
Codebase Analysis Agent
RAG plus semantic indexing for large repositories, designed to answer useful engineering questions quickly without losing source grounding.
Healthcare Anomaly Detection
Streaming ML system processing millions of events per day for insider-threat detection on regulated healthcare data.
Bring us the system that matters
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.