Most operating teams move faster with senior advisory than with a black-box vendor. We default to guide-me engagements — strategic direction, architecture review, and embedded mentoring — and reserve do-it-for-me implementations for the cases where in-house capacity isn't the constraint.
We install AI-powered workflows on your own infrastructure, running your own models, under your own control. No third-party SaaS in the middle. No data leaving your environment. No model dependency you can't replace.
Why this is the moat: Once an autonomous workflow runs inside your environment on your own model weights, you own the operating layer. You get data sovereignty for regulated workloads, sub-second latency on private inference, IP protection, predictable cost at scale, and zero vendor lock-in. Strategically, this is the difference between renting AI capability and owning operational intelligence. We sometimes also refer to this as "Sovereign AI Installation" — same outcome, different framing for buyers who already understand the strategic stakes.
Engagements limited — first design partners only
Five engagement shapes covering everything from data foundations to agentic execution. Most begin with a short audit so we can recommend the right starting point.
Control the record before you automate the workflow
We map the operational record across your vendor stack, establish continuity-grade access and verification paths, and put governance where operators actually need it.
Your first step toward autonomous operations
A structured 2-week deep dive into current data pipelines, decision logic, and AI infrastructure. We map every gap between where you are and where you need to be, and deliver an executive-ready phased roadmap.
Build the foundation that makes AI work
We embed with your engineering team to architect and build the complete operational AI stack: from data ingestion through evaluation and decision routing into automated execution.
Autonomous agents that execute real business logic
Multi-agent systems with proper task decomposition, tool use, error handling, and human-in-the-loop escalation paths — designed for production reliability, not demo dazzle.
Senior AI architecture leadership without the full-time hire
Embedded part-time AI architecture leadership: strategic direction, team mentoring, architecture reviews, vendor evaluation, and stakeholder communication.
Every engagement uses the same operational architecture — a 5-layer stack that turns operational data into governed, automated execution. This is the blueprint your engineers will see in every review.
Continuous Signal Ingestion
Ingests, normalizes, and indexes high-velocity telemetry, ERP data, logs, and human inputs. Bridges siloed enterprise systems into unified AI context.
Dynamic State Management
Retrieval-augmented generation architecture and temporal memory. Retrieves the exact operational context required for reasoning.
Probabilistic Decision Making
Multi-agent orchestration and foundation models evaluate context, generate hypotheses, and output candidate actions.
Policy & Safety Verification
Hard-coded rulesets that evaluate LLM outputs against business logic, compliance, and safety parameters before execution.
Verifiable Actions
Translates approved intelligence into hard operational execution via APIs, triggering workflows or alerting human operators when needed.
Tell us a little about what you're trying to build and where you're stuck. We'll route you to the right engagement shape — guide-me by default, or design partner if you want to be one of our first on-prem deployments.