AI engineering engagement paths
Right engagement shape first. Technical surface second.
No SDRs. A Principal Engineer reviews every submission.
Fixed-fee assessment
Architecture review and suitability
Decide what should be agentic, what should stay deterministic, and what has to be proven before build starts.
Open track → BuildSprint or controlled pod
Scoped build, architecture through deployment
Ship one defined workstream through a bounded sprint. Reserved execution capacity comes later, once the architecture is already clear.
Open track → StabilizeFixed-fee teardown
Production audit and remediation path
Independent review for systems already showing reliability, latency, or observability strain.
Open track → Embedded AdvisoryMonthly advisory
Ongoing architecture counterpart
Ongoing technical judgment for teams that need senior guidance without adding a staffing layer.
Open track →Agents + Data Infrastructure + Governance
We build all three in one engagement — no vendor stitching required.
LangGraph, CrewAI, multi-agent orchestration
Checkpoint persistence, HITL gates, structured state management. Agent systems that survive production load.
See capabilities → Data InfrastructureKafka, Flink, Spark, real-time pipelines
Streaming infrastructure that feeds agent systems. CDC, event sourcing, backpressure handling.
See capabilities → GovernancePermissions, audit trails, blast radius design
Tool-scoped RBAC, HITL checkpoint policies, compliance frameworks. Governance designed in, not bolted on.
See capabilities →Technical systems we get pulled into
The capability areas behind the engagement tracks above.
AI Agent Engineering
Agent systems with checkpoints, approvals, and production-grade observability.
Outcome: ship agent workflows with checkpoints, approvals, and observability before reliability debt piles up.
✓ Pipeline active · p99: 38ms · 800 concurrent
✓ HITL approval gate enabled
Data Engineering
Data pipelines and event systems that hold up under live operating pressure.
Outcome: move from fragile pipelines to a data plane teams can trust under live load.
Explore capabilities →ML & Data Science
Models turned into monitored production systems, not notebook artifacts.
Outcome: turn models into monitored decision systems that survive contact with production.
Explore capabilities →Vector & Graph Databases
Retrieval and knowledge infrastructure built for downstream product use.
Outcome: make retrieval and knowledge infrastructure accurate enough for downstream product use.
Explore capabilities →Full-Stack AI Applications
Full-stack AI applications with backend, deployment, and operational guardrails in place.
Outcome: ship full-stack AI products with the backend, deployment, and operational guardrails already in place.
Explore capabilities →Let's architect your next system
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.
From the team behind Production-Ready AI Agents (Amazon, 2025)