Eight capabilities separate a model built to execute from one built to chat. They run from the pretraining objective, through the decoder, to how each model is tuned and reinforced on your own work.
Published reliability, backed by SLA, enforced at the decoder.
Trained on tool-call traces, planning trees, and multi-step execution data, not conversation data. The pretraining objective is the job, not the chat.
Schema-validated JSON and function calls with published reliability rates, 99.5% valid, backed by SLA. Reliability is a contract, not a benchmark claim.
Validity is enforced at the decoder level. An Ontolith model cannot emit a malformed action, an out-of-schema field, or a call to a tool that doesn’t exist.
Native confidence scores and fallback plans in every output, so your orchestrator knows when to escalate to a human, or to a larger model.
A managed pipeline tunes each model monthly on your own traces, with before/after evaluation evidence. Promote or roll back versions with confidence.
First-class performance in your operating languages, including low-resource government languages frontier models treat as an afterthought.
No labeled data to start. Describe the agent, its tools and a couple of examples; the advisor picks the recipe and synthesizes the training set, filling any decision branch your examples miss with reward-verified cases. You bring the intent, not a dataset.
Beyond imitation: models are reinforced toward outputs that verifiably pass, schema, your enforced policies, and argument correctness, scored by deterministic checks you already trust. No human preference labels, no reward model to game.
Three representative builds cover the range of enterprise agent work, from high-volume single tasks to long-horizon orchestration. Every deployed model is specialized to a customer workflow and delivered as a signed, versioned artifact.
High-volume single workflows: extraction, triage, classification, routing. Runs on edge NPUs, industrial hardware and quantized on-prem targets.
Tool-calling and multi-step workflow execution with ontology grounding. Runs on on-prem GPU, VPC and air-gapped clusters.
Complex planning, long-horizon agent workflows and fleet-lead orchestration. Runs on on-prem GPU, VPC and hosted inference.
The router sits in front of your existing providers and becomes the front door for 90%+ of traffic, without you migrating anything.
Each request goes to the smallest capable model and escalates to a frontier API only when confidence drops. You keep your existing OpenAI or Anthropic accounts.
The distillation pipeline turns your frontier-model usage into your own small models over time. Every API call you make today becomes training signal for a model you will own tomorrow.
The same gateway screens every request for prompt-injection on the way in and for data loss, PII and secrets on the way out. Repeat or near-duplicate calls are served from a semantic cache, savings on top of routing, before a GPU ever runs.
Model ownership runs as an operational workflow, not a research project. Promotion and rollback are one action away, and every version carries its evidence.
Live metrics, version lineage, trajectory and golden-set evaluations, and before/after evidence sit on each model, so a promotion is a decision with proof, not a guess.
If a promoted version regresses, rollback returns instantly to a known-good model. Nothing changes in production without a record of why.
When a model’s structured-output validity drifts below its SLA, the fleet does not just alert, it drafts the correction and waits for a human to approve it.
The fleet opens an incident, diagnoses the likely cause of the drift, and drafts the exact retraining needed to correct it.
A human with the right role approves. No autonomous change reaches production, so self-healing never means self-deploying.
The models are the core, but the surrounding set is designed to make the category impossible to enter without competing with us on every front.
A pluggable long-term memory component, episodic and semantic, purpose-built for the models, so fleets retain workflow experience across sessions.
We publish and maintain the industry benchmark for agentic execution reliability. Owning the scoreboard defines the category and sets the metric competitors must meet.
Optimized builds for edge NPUs and industrial hardware, opening factory-floor, vehicle, and robotics deployments no cloud API can reach.