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
When teams bring us in
The useful moment is usually before a commitment hardens: a production rollout, a platform choice, a governance boundary, or a build path that will be expensive to unwind.
The system works, but trust is uneven
Latency, cost, observability, eval quality, or permissions are starting to matter more than demo quality.
Need: failure-mode map, remediation sequence, and the evidence required before scale.
The team needs a defensible path
Agentic or deterministic? Buy or build? RAG, workflow automation, voice, or a simpler control plane?
Need: explicit tradeoffs, artifact-backed rationale, and a path leadership can approve.
The team is capable, but under-reviewed
Important calls are being made without enough challenge around control boundaries, sequencing, reliability, or operating cost.
Need: principal-level review discipline without adding another management layer.
What this looks like
Autonomous workflows. Knowledge infrastructure. Streaming data platforms.
Healthcare Anomaly Detection
Streaming ML system processing millions of events per day for insider-threat detection on regulated healthcare data.
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 producing structured, diffable reports for human review.
Codebase Analysis Agent
RAG plus semantic indexing for large repositories, designed to answer useful engineering questions quickly without losing source grounding.
Get a clear technical path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
What happens next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
Direct contact with a principal engineer.