Per-customer continual fine-tuning
Incrementally update customer-specific adapters from approved outcomes without forgetting prior capability — with an automated regression harness that verifies every improvement before it ships.
Research
The technology is the product.
We are a research lab first. We invent new model technology and commercialize it; industries are proving grounds, not the point. Here is the agenda.
The inventions
Four capabilities that, composed, produce a living model.
- 01
Continual learning
Methods to keep a model improving in deployment — updating from real outcomes without a retrain cycle and without catastrophic forgetting of prior capability. The evaluation discipline that makes this safe is as much the work as the training itself.
- 02
Streaming & live models
Always-on models that consume a continuous data stream and act in real time. The unit of work is not a request and a response; it is an environment observed and acted upon, without pause.
- 03
Recurrent language models (RLM)
Persistent recurrent state for unbounded memory and genuinely long-horizon tasks — carrying the full history of a process rather than re-reading a fixed context window.
- 04
Agentic systems
Autonomous, multi-step action that drives the underlying software end-to-end, with human approval at the points that matter. The agent owns the workflow; people manage agents.
Composed, these are living models: persistent memory, continuous learning, real-time streaming, autonomous action — one object that runs 24/7 and gets better the longer it runs.
How the learning compounds
Applied research tracks that turn a customer’s approved outcomes into model improvements at production scale.
Autonomous skill extraction
Agents synthesize reusable procedures from completed multi-step work, verified before promotion. Procedural patterns generalize across customers; customer data never does.
Reward modelling & alignment
The approval workflow — approve, reject, edit — is the training signal. We align to each customer’s house style and risk appetite, and surface low-confidence work to a human rather than acting alone.
Open by default, where it helps
We run a dual track. Generic frameworks are open-sourced — for adoption and for credibility with the ML community. The enterprise agent, orchestration, and proprietary skills are gated behind commercial licensing.
Expect a measured cadence of technical writing and selective open-source contributions: enough to signal capability and attract people who want to work on hard problems, without giving away the compounding advantage.
Business first, lab as the engine.
The lab is the moat, but it is funded by what customers buy today. We lead with working deployments and let the frontier research deepen them — not the other way around.
Bring us a live environment.
A continuous stream, real drift, decisions that matter. We’ll bring a living model that works inside it. First engagements are a scoped, paid pilot on one workflow.
Become a design partner