How It Works
A Process Upgrade, Not a Knowledge Upgrade
Open-source models already know what a credit memo looks like. What they can't do reliably is produce that shape on demand. That's a coherence problem, not a knowledge problem.
| Domain | Model Already Knows | Adapter Teaches |
|---|---|---|
| Financial Services | Credit analysis, risk frameworks | Memo headers, facts-first ordering, field labels |
| Healthcare | Anatomy, SOAP format | HPI → exam → differentials → plan |
| Legal | Contract law, precedent | Executive summary → risks → clause flags → redlines |
| Coding | All syntax and patterns | Decompose, constraint-comment, order solution |
Two-Layer Architecture
Medical school teaches general clinical reasoning. Residency teaches cardiology protocols. Layer 1 is medical school. Layer 2 is residency.
Layer 1
General Coherence Adapter
Teaches the model to use its existing intelligence coherently. Applies to any domain, any task.
Layer 2
Domain Adapter
Teaches domain-specific artifact structure. Stacks on the coherent base for better results than domain-only training.
Drop-In Deployment
We hand you a LoRA adapter. Your team loads it. Zero code changes. Same latency. Coherent output.
| Inference Stack | Compatible |
|---|---|
| vLLM | ✅ |
| TGI | ✅ |
| SGLang | ✅ |
| Ollama | ✅ (merged) |
| TensorRT-LLM | ⚠️ (add-on) |
Also works with Cursor, Continue.dev, aider, and Cline via any OpenAI-compatible endpoint.
Enterprise Engagement
Prove
Week 0
Free general adapter. See coherence in action.
Discover
Week 1–2
We profile your workflows and identify domain gaps.
Engineer
Week 2–3
Custom training prompts designed for your domain.
Train
Week 3
Coherence adapter trained and validated.
Deliver
Week 3–4
Adapter delivered with validation report. If it doesn’t meet your bar, you pay nothing.
Security & IP
We never see your data. Adapter training uses our cognitive prompts, not your proprietary information.
Your data stays in your VPC
For public base models, we deliver the adapter file. For custom models, we ship a Docker container you run in your own infrastructure.
The adapter contains no proprietary IP
The delivered LoRA adapter contains only learned weight matrices—the output of our training process. It does not contain our scoring algorithms, measurement framework, or training methodology. Our IP stays in our pipeline; your adapter is yours to deploy.