Agent Foundation Models
Small models that act,
not chat.
Ontolith builds 3–7B parameter models trained for agentic execution: tool calling, planning, and structured outputs. Specialized, sovereign, and certifiable. A fleet you own, not API calls you rent.
10–50×
Lower unit cost than frontier APIs on the workflows that matter
99.5%
Schema-valid structured outputs, backed by SLA
3–7B
Parameters per model, one specialized model per workflow
100%
Inside your perimeter, on-prem, VPC, or fully air-gapped
The problem
Agents don’t fail for lack of intelligence.
Production agents fail on economics, reliability, and sovereignty, three problems general-purpose frontier models were never built to solve.
The economics break at scale
Agent workloads are high-volume and repetitive. An enterprise running 10,000 agents makes millions of tool calls a day, and frontier-API pricing collapses under that load.
Reliability, not IQ, kills agents
Agents fail in production because models emit malformed outputs, invalid tool calls, and undetected low-confidence plans, failure modes general models were never optimized against.
An entire market is locked out
Banks, defense, healthcare, and governments increasingly cannot use US cloud AI APIs at any price. They need capable models inside their own perimeter, with audit-grade provenance.
The position
Not cheaper models. Specialized vs. general. Owned vs. rented.
A small model fine-tuned on one workflow routinely matches or beats a frontier model on that task, at a fraction of the cost, with lower latency, and with the option to run fully on-prem or air-gapped. The savings are supporting evidence. The headline is a capability no frontier lab can neutralize with a price cut: certifiable, sovereign, workflow-owned intelligence.
The product
A model fleet you own, and the tooling to run it.
One specialized model per workflow, dozens per enterprise. Each improves monthly on your own data and never leaves your infrastructure unless you choose.
Agent-native models
Trained on tool-call traces, planning trees, and multi-step execution data, not conversation. Built for the work agents actually do.
Guaranteed structured outputs
Schema-validated JSON and function calls with published reliability rates, enforced by a constrained decoding engine so the model cannot emit an invalid action.
Sovereign deployment
On-prem, air-gapped, VPC, and quantized edge variants for GPU-poor and classified environments. Certifiable model passports where “we called an API” fails audit.
Continuous fine-tuning
Monthly tuning on your own traces, with before/after evaluation evidence. Your fleet keeps improving on your workflows, and you own every artifact.
Start where you are
You don’t have to own a fleet on day one.
Keep your existing providers. The Ontolith router deploys inside your current stack in weeks, proves the savings on your own traffic, and converts that traffic into models you own over time.
Route
Keep your OpenAI and Anthropic accounts exactly as they are. The router sends each request to the smallest capable model and escalates to a frontier API only when confidence drops. No migration, no rewrite.
Prove
Router analytics show exactly what the small models handled, what escalated, and what it saved, measured against your own baseline. You pay 15% of the savings we can verify, and nothing if there are none.
Own
The distillation pipeline turns your highest-volume traffic into specialized models you own. Over 6 to 12 months the front door becomes the fleet, and every API call you make today is training signal for it.
Ontology-conditioned inference
Not smarter. Informed.
Every Ontolith model receives your enterprise ontology, the structured record of your people, roles, systems, and policies, as grounding at inference time. Instead of hallucinating what an approval workflow means, it reads your actual approval chain: who can approve what, under which policy, in which system.
This is precisely how a 5B model beats a 400B model on enterprise tasks.
How the ontology works →The Fleet Console
Model ownership as an ops workflow.
Version, evaluate, promote, roll back, and monitor every model in your fleet from a single pane of glass. Reliability against SLA, router savings for the CFO, and a passport registry your auditors can actually use.
Inside the console →Who it’s for
Built for the enterprises the cloud can’t reach.
Government & defense
Certifiable, air-gapped models where foreign cloud APIs are legally off the table.
Banking & insurance
Data residency, audit requirements, and massive repetitive agent workloads.
Capital markets
Millisecond latency and confidentiality for trading desks and asset managers.
Healthcare systems
High-volume structured tasks on PHI that can never leave the perimeter.
Legal
Contract review and due diligence with privilege kept inside the building.
AI-native enterprises
Agent fleets at a scale where frontier-API economics stop working.
Why we win
A position frontier labs cannot occupy.
Unownable for labs
Frontier labs cannot credibly sell “you own the model, air-gapped and certified”, it contradicts their API business model.
The metric nobody owns
Execution reliability, not benchmark IQ, is what kills agents in production. We define it, measure it, and win on it.
Compounding, in your favor
Every month of your traces makes your fleet better. The lock-in compounds for you, because you own the artifacts.
Evaluation sandbox
Convert one workflow. Prove the model.
Qualified enterprises get a hands-on evaluation sandbox, one high-volume workflow converted to an owned model, with measurable cost and reliability gains.