AI Agent Engineering
Agent systems with checkpoints, approvals, and production-grade observability.
How We Engage
AI engineering engagements
Choose the engagement for your system's current stage. Explore the technical capabilities below.
Direct contact with a principal engineer.
Validate
Fixed-fee diagnostic
Production AI Readiness Review
Diagnose one live or near-live AI path before more build effort compounds the wrong failure mode.
Build
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.
Stabilize
Fixed-fee implementation
Known-path corrective engineering
Remediate one visible failure path when the hot path, access authority, acceptance criteria, and rollback path are explicit.
Embedded Advisory
Monthly advisory
Ongoing architecture counterpart
Ongoing technical judgment for teams that need senior guidance without adding a staffing layer.
Buyer Situations
Start with the problem you need to resolve
These examples connect operating problems to the relevant engagement or capability.
Funded Startup
AI-assisted product is close to launch but unstable
State, webhooks, payments, or recovery are blocking real users. Start with review when the cause is unclear; use stabilization when the failure path is known.
Enterprise
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.
Vertical SaaS
AI feature must become a product workflow
Move from assistant demo to a production feature with customer data, approvals, and rollout gates. Choose a bounded build sprint or reserved pod capacity for the workstream.
Knowledge Work
Professional workflows need reliable retrieval
Legal, advisory, tax, research, and support teams need answers grounded in documents, cases, policies, tickets, and source trails.
Customer Ops
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.
Technical Capabilities
Systems we design, build, and stabilize
The capability areas behind the engagement tracks above.
Data Engineering
Data pipelines and event systems that hold up under live operating pressure.
ML & Data Science
Models turned into monitored production systems beyond notebook artifacts.
Vector & Graph Databases
Retrieval and knowledge infrastructure built for downstream product use.
Full-Stack AI Applications
Full-stack AI applications with backend, deployment, and operational guardrails in place.
Next step
Not sure which engagement fits?
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.