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Collect & structure data
Aggregate domain documents, annotation labels, and examples into structured training datasets suited for your target task.
Currently in Development
CogniTune
Domain Model Optimization & Fine-Tuning System
A model optimization and fine-tuning system for adapting language models to domain-specific use cases—compliance, security, and operational AI. Built around dataset preparation, structured training workflows, and rigorous evaluation pipelines.
Why generic models aren't enough
Foundation models are trained on general web data. Enterprise compliance and operational tasks require models that understand specific domain language, frameworks, and output formats.
Domain accuracy
General models hallucinate on specific compliance requirements, framework mappings, and technical audit tasks. Fine-tuned models with correct training data perform significantly better.
Output format control
Enterprise workflows require structured, predictable outputs—JSON, tables, labeled classifications. Fine-tuning enables reliable output formatting that prompt engineering alone cannot guarantee.
Data efficiency
Smaller, fine-tuned models can outperform larger general models on domain tasks while being faster and cheaper to serve—an important property for high-volume enterprise workloads.
What CogniTune is building
Dataset Preparation
Collecting, cleaning, and structuring domain-specific training data is the most critical step in model fine-tuning. CogniTune provides tooling for building high-quality compliance and operational datasets from raw enterprise documents and records.
Model Fine-Tuning Workflows
We apply parameter-efficient fine-tuning methods (LoRA, QLoRA) to adapt language model checkpoints to domain-specific tasks. The fine-tuning pipeline is designed to run on cloud GPU instances with experiment tracking and reproducibility built in.
Evaluation Pipelines
Fine-tuned models are evaluated against curated domain benchmarks before deployment. CogniTune tracks accuracy, hallucination rate, and task-specific quality metrics across training runs to ensure models are reliably improving.
Iterative Improvement
Production feedback loops feed real-world outputs back into the dataset and retraining cycle. CogniTune is designed to support continuous model improvement as new domain data becomes available.
The fine-tuning workflow
From raw domain data to a deployed, evaluated model adapter.
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Configure training run
Select base model, fine-tuning method, and training parameters. Submit to GPU compute infrastructure.
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Monitor training
Track loss curves, resource utilization, and training metrics in real time across distributed compute jobs.
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Evaluate & validate
Run the fine-tuned model through evaluation benchmarks. Compare against baseline and prior versions.
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Deploy for inference
Serve the fine-tuned adapter via a scalable inference endpoint that integrates with CogniAudit and CogniAgents.
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Collect feedback
Route production outputs back to the dataset pipeline for continuous model improvement over time.
Domain use cases CogniTune targets
Compliance document analysis
Train models to understand the specific language and structure of compliance frameworks, policies, and audit evidence.
Risk classification
Fine-tune classifiers to identify and categorize risk signals in enterprise documents with higher accuracy than zero-shot prompting.
Report generation
Adapt generative models to produce structured compliance reports in formats familiar to auditors and reviewers.
Operational log interpretation
Train models on labeled operational data to interpret log anomalies and system events with domain-specific context.
Technology stack
PyTorch
Hugging Face Transformers
PEFT / LoRA / QLoRA
Weights & Biases
Cloud GPU infrastructure
vLLM / Triton Inference
Get early access to CogniTune
CogniTune is currently in active development. Join the waitlist to be notified when we open early access, and to share your domain use case with the team.
Prefer to talk directly? Reach out to the team