AI engineering engagement paths
Right engagement shape first. Technical surface second.
No SDRs. A Principal Engineer reviews every submission.
Fixed-fee assessment
Architecture, readiness, and suitability
Decide what should be agentic, what should stay deterministic, and whether a voice or meeting workflow is ready for a bounded pilot.
Sprint or embedded pod
Scoped build sprint or delivery pod
Build one workstream through a scoped sprint or embedded delivery pod, with architecture control, reliability gates, and production handoff.
Fixed-fee teardown
Production audit and remediation path
Independent review for systems already showing reliability strain, including AI-assisted prototypes blocked by state, webhooks, payments, or observability gaps.
Monthly advisory
Ongoing architecture counterpart
Ongoing technical judgment for teams that need senior guidance without adding a staffing layer.
Where the right engagement starts
Start from the failure mode before the technology label. These are the buyer patterns we see most often when AI work moves from experiment to operating pressure.
AI-assisted product is close to launch but unstable
The demo exists, but state, webhooks, payments, recovery, or traceability are blocking real users.
Multiple AI pilots need a shared operating model
Business units are prototyping agents, copilots, and RAG tools, but governance, data access, and funding priority are inconsistent.
AI feature must become a product workflow
A SaaS team is moving from assistant demo to a production feature with tool use, customer data, approvals, and rollout risk.
Professional workflows need reliable retrieval
Legal, advisory, tax, research, and support teams need answers grounded in documents, cases, policies, tickets, and source trails.
Voice and intake agents need reviewable artifacts
The workflow touches calls, meetings, lead intake, escalation, or follow-up. The output must be inspectable and accountable beyond the conversation.
Agents + Data Infrastructure + Governance
We build all three in one engagement with one accountable architecture path.
LangGraph, CrewAI, voice-agent pilots
Checkpoint persistence, HITL gates, voice workflow boundaries, and structured state. 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 into the architecture.
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 · checkpoints enabled
✓ 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.
Open overview →ML & Data Science
Models turned into monitored production systems beyond notebook artifacts.
Outcome: turn models into monitored decision systems that survive contact with production.
Open overview →Vector & Graph Databases
Retrieval and knowledge infrastructure built for downstream product use.
Outcome: make retrieval and knowledge infrastructure accurate enough for downstream product use.
Open overview →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.
Open overview →Let's architect your next system
Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
No SDRs. A Principal Engineer reviews every submission.
From the team behind Production-Ready AI Agents (Amazon, 2025)