From prototype to production-grade AI
without the missteps in between.

We partner with engineering and product teams to design generative AI systems
that are reliable, observable, and actually fit the business problem.

Three focus areas, one engineering-first philosophy.

Most AI projects do not fail because the model is wrong — they fail at the seams: brittle agent loops, unclear data contracts, missing evals, and digital twin pipelines that drift from the physical asset. We help teams close those gaps with concrete architecture decisions, hands-on reviews, and integration playbooks tailored to your stack.

Generative AI Architecture

Model selection, retrieval design, prompt and context strategy, evaluation harnesses, and cost/latency trade-offs — turned into a reference architecture your team can build on.

Agent Orchestration Reviews

Independent review of multi-agent systems: tool boundaries, state and memory, failure modes, observability, and guardrails — with a written report and prioritized remediation plan.

Digital Twin Integration

Bridge generative AI with digital twins of physical assets, processes, or facilities — covering data ingestion, simulation feedback loops, and human-in-the-loop decision support.

How an engagement typically looks

1. Discovery & Architecture Review

A focused engagement (typically 1–3 weeks) to understand your goals, current stack, and constraints. Output: a written architecture assessment with concrete recommendations and risks.

2. Agent & System Deep-Dive

Hands-on review of agent orchestration, tool design, evaluation coverage, and operational readiness — benchmarked against patterns we have seen succeed (and fail) in production.

3. Digital Twin Integration Plan

If digital twins are in scope, we map data flows between physical systems, twin models, and AI agents — identifying where generative AI adds leverage and where it adds risk.

4. Ongoing Advisory

Optional retainer for design reviews, hiring support, vendor evaluation, and second opinions as your platform evolves. Light touch, high signal.

Planning a generative AI initiative?

Get an independent perspective before you commit to an architecture or vendor.