Sovereign Enterprise Intelligence
Own what your enterprise knows.Control how it learns. Choose who executes it.
METIS, our enterprise AI accelerator, gives your enterprise a governed system to create, validate, compound, and execute its own intelligence — across models, vendors, and infrastructure.
Why governance matters
AI amplifies at machine speed. Are you amplifying learning velocity — or sprawl?
“Speed is the defining organizational advantage.”
Governance is the difference. It turns AI sprawl into compounding learning velocity — so what one agent, application, or analyst learns becomes intelligence the whole enterprise reuses.
EssayEnterprise AI is entering its Ford momentBad code usually crashes. Bad AI semantics execute perfectly and return a convincing, wrong answer.
Leakage produces a beautiful regression. A wrong grain produces significant nonsense. Deterministic validation exists to prevent plausible wrongness, not merely execution failure.
Similarity proposes. Quantitative analysis verifies. Source evidence explains.
The METIS architecture · Patent-pending
Compounding enterprise intelligence.
A governed layer where the enterprise’s semantics, data science, and evidence live together — so what the enterprise discovers once becomes a capability every agent, application, and analyst can reuse.
Proprietary to your enterprise — your definitions, your models, your evidence — and larger after every discovery.
The strategic case
AI everywhere is not enterprise intelligence.
Why do anything
Putting AI everywhere without shared intelligence can make the enterprise fragment faster, not learn faster — agents, copilots, and applications each recreate semantics, features, evidence, and policy.
Why now
The competitive clock is shifting from scale toward learning velocity. Advantage comes from how quickly an organization notices change, investigates it, codifies what works and deploys the learning again.
Why METIS
METIS turns each discovery into proprietary enterprise intelligence that compounds — the business gets faster at noticing, understanding, and acting while competitors relearn the same lessons.
Most “governed” AI governs access to data. METIS governs whether an answer is allowed to be trusted.
The proof
The same governed pattern, across domains.
Customer Experience · Contact Center
Hidden intelligence, made governed and reusable — behavior and attitude, across the life of the customer.
The business thought it had call-type data. It actually had customer intelligence sitting inside nearly every interaction.
METIS made hidden intelligence explicit, governed, and reusable — not just what happened, but where the relationship is heading — without changing a single interaction.
Before
- 1,000 interactions
- Primary reason
- Secondary reason
- Transcript
- product context: buried
- business actions: unstructured
After
- Interaction classification refined
- 71%Interaction classification refined
- Product & service visibility
- 4.5×Product & service visibilityInternet, Mobile, Video/TV
- Carried 3+ actionable signals
- 73%Carried 3+ actionable signalsbilling, repair, service change, equipment return
Getting to a trusted result took dozens of AI pipeline bake-offs — frontier APIs and self-hosted models across the full continuum — that produced very different answers, speeds, and economics. The METIS accelerator is what traversed that range and qualified which result could be trusted.
The same trusted result, for a fraction of the cost. ~20× lower · ~$1,000 → ~$50 on 1,000 interactions · premium reasoning used selectively, not everywhere.
Speed: From multi-second governed reasoning to ~18 ms production inference.
A NeoSavant exercise on 1,000 synthetic customer interactions.
NPS gives you the survey. Governed intelligence can help reveal the trajectory between surveys.
Behavior tells you what happened in an interaction; attitude tells you what it's doing to the relationship — and the same governed layer is designed to connect the two, across the life of the customer.
Built by a team that has engineered noisy data and enterprise systems in production for decades — 6 granted and 13 pending U.S. AI patents.
The AI lab
Part of the METIS acceleratorA governed model farm.
We run governed champion / challenger / continuous refinement across frontier APIs (Anthropic, OpenAI) and self-hosted open models (Llama, NVIDIA Nemotron, Qwen, Mistral) — measured on quality, latency, and cost under one governed evaluation contract. Classical ML for low-latency, high-volume signals; specialized models where they fit; LLMs for richer semantics — kept comparable by the governed layer, promoted only by explicit, human-governed decisions.
An AI lab that pits model approaches against each other on your problem — governed and measured.
From NeoSavant's governed call-center evaluation runs.
| Model | Quality | Latency | Cost efficiency | Governability | Best-fit role |
|---|---|---|---|---|---|
| Claude (Sonnet / Opus class)API | Governed reasoning · reviewer · adjudicator | ||||
| GPT / OpenAIAPIBake-off in progress — ratings publish when completed. | varies | varies | varies | varies | Reviewer candidate |
| QwenSelf-hosted | First-pass semantic · hybrid base | ||||
| NVIDIA NemotronSelf-hosted | Executor — capacity-limited in these runs | ||||
| MistralSelf-hosted | Cross-family reviewer candidate | ||||
| Classical MLSelf-hosted | Narrow, high-volume signals |
five-dot role-fit ratingvaries = no like-for-like comparison yet— / pending = awaiting sign-off
Role-fit ratings from separate governed runs across different cohorts and tasks — not a head-to-head benchmark, and no single “best model.”
Raw model strength isn't enough on its own — the same governed definitions and validation that let a frontier model shine also expose where a weaker one quietly goes wrong.
Classical machine learning still earns its place: on narrow, high-volume signals it runs in milliseconds at effectively zero marginal cost, and holds its own.
Stable agreement can still be wrong — models can agree, confidently and repeatedly, on an answer governance rejects, which is exactly why acceptance is independent of who (or what) produced it.
How it works
Part of the METIS acceleratorTrusted Executable Intelligence.
Analytical intelligence whose accepted form is constrained by independent semantic contracts and governed acceptance — regardless of whether a human or a machine generated it. So trust doesn’t depend on trusting who produced the answer.
Built on patent-pending governed-intelligence methods.
The loop that decides who wins.
BI answers questions the enterprise has modeled. METIS can investigate questions the enterprise has not modeled yet — then preserve the useful discoveries for the next question.
Each verified discovery is codified as governed capability, so every turn of the loop starts smarter than the last.
Born from platforms running in production today
One compounding capability — from call center to emergency response to human movement.
- 60M+
- users served by architectures we've built
- Million-device
- edge scale — computer vision on mission-critical infrastructure
- 1M+
- biomechanical calculations per session
- 5M+
- events per day — nowcast, forecast, cascade & decision models fused into one live city model
Born from marquee work across insurance, public-safety, and frontier biomechanics platforms.
METIS · flagship accelerator & reference architecture
The governed layer, engineered.
Not another chatbot. Not another dashboard. The definitions, models, and evidence are proprietary to the enterprise — and they get stronger with use.
Our flagship governed-intelligence accelerator and reference architecture — a portable SDK and architecture, built on patent-pending governed-intelligence methods.
SDK
A portable SDK — governed capability shipped as versioned code, in your account or ours.
Models
A governed model farm — frontier APIs and self-hosted models, qualified side by side.
Lab
An evaluation lab — quality, latency, and cost measured under one governed contract.
Intelligence-as-code
Semantics, features, tools, policies, and evaluations as portable intent — above any single runtime.
Works across
- Multi-cloud
- AWS · Azure · Google Cloud
- Agent frameworks
- MCP (Model Context Protocol) · LangGraph · Google ADK (Agent Development Kit)
- Model platforms
- Amazon Bedrock · Microsoft Foundry · Google Vertex AI
- Model families
- Anthropic Claude · OpenAI · Llama · NVIDIA Nemotron · Qwen · Mistral
Start bounded
A six-month intelligence experiment should not require a ten-year platform bet.
A METIS accelerator engagement starts bounded and enterprise shaped, in your account or ours — answering real questions on real data, codifying what matters, and transferring through Build–Operate–Transfer so you decide from evidence.
Intelligence Sovereignty
Own your intelligence layer. Don’t rent it.
Build it, own it, compound it — rather than ceding it to an outside platform.
Open-table formats make data portable. The expensive asset is what the enterprise learns about itself — your Trusted Executable Intelligence. It’s the “data sovereignty” enterprises already understand, extended to the intelligence layer itself.