AI Agents you can trace and prove. Not another guardrail. An inference control layer.
UACL lifts a 27B model from 84% to 92.4% on GPQA Diamond:
frontier accuracy on a single GPU, inside your own environment,
without fine-tuning or training.
Built for AI-native teams in legal, finance, and health.
SOC 2 in progress.
84% → 92.4% on GPQA Diamond
(27B model)
8.4%
Model accuracy boost
1
GPU
0
Training runs
0
Data leaving your environment
Today you fix AI after it answers. UACL fixes it before.
One layer replaces most of the guardrails, judge models,
and retries you stack on top of your model. Fewer integrations.
Same infrastructure.
and retries you stack on top of your model. Fewer integrations.
Same infrastructure.
app → guardrail →
judge model → retry →
model
judge model → retry →
model
app → UACL →
model
model
UACL REPLACES:
Guardrail passes
Judge-model calls
Post-hoc citation matching
Fine-tuning runs
KEEPS:
Your model
Your model server
Your data
Your environment
AI that shows its work, guards your data, and costs what you planned.
Rules your AI can’t ignore.
COST EFFICIENCY
Use 148x smaller models that will operate at frontier levels.
SOVEREIGN
Runs inside your environment. Our API sees only vectors at inference time.
SAFETY CONTROLS
Every answer is grounded in verification of your inputs, not in what the model memorized.
AGENT SECURITY
Agents stay controlled and monitored against their goal.
EXPLAINABLE
Every claim traces to the source passage in your documents.
SELF-IMPROVING
Adaptive RAG that learns from your data and puts it ahead of the model’s training data.
Your AI's inflight recorder. No more blackbox answers.
The Universal Agentic Control Layers, built for regulated enterprises.
UACL
Pollinator
WHEN YOU NEED FRONTIER-GRADE WITHOUT THE FRONTIER BILL.
Fit a frontier-grade model on 1 GPU without fine-tuning or training. Let your LLM provide the language and UACL provide the reasoning.
Model agnostic, can be applied to any model.
One accurate loop, instead of 6+ agents.
Prevents hallucinations inflight, instead of afterwards.
Swaps models without rebuilding
Affordable frontier-grade
UACL
Espalier
WHEN YOU NEED AN AUDIT TRAIL OF THE AI’S ANSWER.
It answers from your sources or says “I don’t know,” so failure is something you can design for.
Traces each answer back to its source.
Flags conflicts when two sources disagree.
Provides an auditible log.
It answers "I don't know" when a knowledge gap exists.
Explainability and verifiable controls
UACL
Taproot 10
WHEN AGENTS NEED BOUNDARIES TO ACHIEVE THEIR GOAL.
Monitors each agent against its goal and logs every action, so you can see what it did and why.
Observes and logs every action.
Monitors every task to keep it aligned with its goal.
Observable end to end
Controls before incidents, not after
Preventing rogue agents
UACL
Regenerative RAG
WHEN EVERY QUERY MAKES THE NEXT ONE SHARPER.
Adaptively improves with enterprise data at inference time, without training.
Accuracy improves with use.
Prioritizes enterprise data over model training data.
Adapts to your knowledge base.
Inference-time training.
Adaptive RAG
If you can’t show your work, you can’t ship it.
Designed for regulated enterprises
Legal
UACL turns your contracts, case files, and regulations into structured, reviewable work product. Every clause, claim, and conclusion links back to the passage it came from.
REQUEST API ACCESS
Works with your stack, in your environment
UACL sits beside your current model server.
It doesn’t replace your model, your vector store, or your identity systems.
SOC 2 Type II: in progress.
YOUR DATA STAYS IN YOUR ENVIRONMENT.
UACL is an API to your infrastructure and never trains on your data. Zero data retention.
Test UACL on your own model.
No training. No fine-tuning. Nothing leaves your environment.
We’ll send access within 1 business day.
Have a Question ?
Does anything leave my environment?
We provide you with an Open AI Compatible API. You run the LLM in your environment which is where the documents get processed. UACL only sees vectors at inference time, without any logs, training or prompts stored. Your documents never leave your environment.
Does it work with my model?
UACL works with all open-weight and closed-weight models.
How is this different from guardrails?
Guardrails catch errors after the answer. UACL corrects reasoning before it’s written, reducing your compute bill and risk of hallucinations.
What happens when it doesn't know?
It says “I don’t know” instead of guessing.
Does it retrain my model?
No. No fine-tuning, no training, no gradients. It boosts accuracy through the reasoning approach. Having said that we offer Regenerative RAG which is a self-improving RAG model that trains on new data at inference time keeping your model updated in realtime.
What does it cost to run?
It depends on which API you want to run, we offer 4 different products, or you can roll all of the products into one. UACL boosts small models to frontier accuracy while at the same time reducing the multi-agent approach to post- hoc deterministic layers. Since UACL can boost a smaller model to a higher level of reasoning, if you are already running a large open-source model you could choose a smaller one or get greater accuracy from the one you are already running. UACL reduces compute bills of frontier model accuracy without having to compromise on the quality of its answers.