AI that works on your data, in your environment — not someone else's cloud
Generic AI products are trained on public data and deployed on third-party infrastructure. We build AI systems that learn from your proprietary data, run in your environment, and give you total control over outputs, costs, and privacy. No API keys. No per-token billing. No vendor dependency.
What sets our AI delivery apart
Fully Private & On-Premise
Your data never leaves your network. We deploy self-hosted models (Llama, Mistral, custom fine-tunes) on your hardware or private server — with no external API calls during inference. HIPAA and confidentiality requirements are met by design.
Domain-Specific Accuracy
Public LLMs hallucinate in specialized fields. We fine-tune and adapt models on your domain corpus — clinical records, lab data, engineering manuals, legal documents — so outputs are grounded in what your organization actually knows.
Predictable Cost Structure
We charge for the build, not the usage. Once deployed, your AI system costs you only the electricity and hardware it runs on — no subscription, no per-query fees that grow with adoption, no vendor price changes.
Full IP Ownership
Every model weight, fine-tune dataset, pipeline script, and inference server configuration is yours. We deliver complete documentation and hand off the keys — you are never dependent on us or anyone else to run your AI.
AI deliverables, end to end
LLM Integration & Deployment
Selection, configuration, and deployment of open-source large language models (Llama 3, Mistral, Phi-3, Qwen) on your server hardware, optimized for inference speed and memory footprint.
Retrieval-Augmented Generation (RAG)
Vector database design (Chroma, Qdrant, pgvector), document ingestion pipelines, semantic search, and RAG orchestration that grounds your LLM's answers in your own knowledge base.
Custom Fine-Tuning
Dataset curation, prompt engineering, supervised fine-tuning (SFT) and RLHF/DPO alignment on your domain-specific corpus to produce a model that answers like your best subject matter expert.
ML Pipelines & Predictive Models
End-to-end machine learning workflows — feature engineering, model training, validation, and scheduled retraining pipelines — for classification, regression, anomaly detection, and forecasting tasks.
AI-Powered Interfaces
Chat-based front-ends, search portals, document Q&A tools, and AI-assisted workflow dashboards — custom-built to integrate with your existing systems and internal applications.
Computer Vision Systems
Object detection, image segmentation, and classification pipelines for microscopy, quality inspection, satellite imagery, and sensor data — trained on your labeled dataset and validated for your use case.
How we build private AI systems
| Layer | Technologies & Methods | Why We Use Them |
|---|---|---|
| Foundation Models | Meta Llama 3, Mistral, Phi-3, Qwen2, GPT-4 (optional API) | Open-source models allow full on-premise deployment; we select based on parameter size vs. hardware budget. |
| Inference Engines | llama.cpp, Ollama, vLLM, ONNX Runtime, TensorRT | Optimized inference frameworks reduce latency and GPU/CPU memory consumption by up to 60% vs. naive loading. |
| Fine-Tuning | Hugging Face Transformers, PEFT/LoRA, Axolotl, Unsloth | Parameter-efficient fine-tuning with LoRA adapters drastically reduces training compute while achieving domain accuracy. |
| RAG & Vector Search | LangChain, LlamaIndex, Chroma, Qdrant, pgvector, FAISS | RAG anchors generative outputs in verified source documents, reducing hallucinations and enabling traceable citations. |
| ML Frameworks | PyTorch, scikit-learn, XGBoost, LightGBM, TensorFlow | Task-appropriate framework selection — deep learning for unstructured data, gradient boosting for tabular, classical ML for explainability. |
| MLOps & Serving | MLflow, DVC, FastAPI, BentoML, Docker, Kubernetes | Experiment tracking, model versioning, and containerized serving create reproducible, production-ready ML systems. |
| Computer Vision | YOLOv10, SAM2, OpenCV, torchvision, Detectron2 | State-of-the-art detection and segmentation models, fine-tuned on domain-specific imagery for high-accuracy outputs. |
| Data & Embeddings | SentenceTransformers, spaCy, Pandas, DuckDB | High-quality embeddings are the foundation of semantic search and RAG — we select embedding models for domain fit, not just benchmark scores. |
Where our AI systems are deployed
Clinical Document Intelligence
Private LLM deployment for querying clinical notes, pathology reports, and EHR data — without sharing protected health information with any external vendor. HIPAA-compliant by architecture.
Scientific Literature RAG
A private internal knowledge base that ingests research papers, lab protocols, and technical reports — allowing scientists to query thousands of documents in natural language and trace answers to source pages.
Predictive Maintenance & Anomaly Detection
ML pipelines trained on sensor, equipment, and operational log data to flag failure patterns before downtime occurs — deployed on-premise with zero cloud dependency.
Satellite & Imagery Analysis
Computer vision models trained on aerial and satellite imagery to classify land cover, monitor crop health, or detect environmental changes — replacing manual inspection at scale.
Enterprise AI Chat & Automation
Internal chatbots grounded in your company's SOPs, HR policies, product documentation, and domain knowledge — integrated with your existing intranet or desktop software.
Visual Quality Inspection
Real-time computer vision pipelines for product defect detection, microscopy image classification, or laboratory sample analysis — embedded directly into existing inspection workflows.