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 external service 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 such as Llama, Mistral, and custom fine-tuned models on your hardware or private server — with no external service calls during operation. HIPAA and confidentiality requirements are met by design.
Domain-Specific Accuracy
Public language models hallucinate in specialized fields. We fine-tune and adapt models on your domain content — 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 delivered, you run it with no payments to us — ever.
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
Language Model 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.
Document-Grounded AI Search
Vector database design, document ingestion pipelines, semantic search, and answer generation that grounds the model's responses in your own knowledge base.
Custom Fine-Tuning
Dataset curation, prompt engineering, supervised fine-tuning, and alignment on your domain-specific content to produce a model that answers like your best subject matter expert.
Machine Learning 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.
Where our AI systems are deployed
Private company chat and automation
Internal chatbots grounded in your company's procedures, HR policies, product documentation, and domain knowledge — integrated with your existing intranet or desktop software.
Clinical Document Intelligence
Private language model deployment for querying clinical notes, pathology reports, and health record data — without sharing protected health information with any external vendor. HIPAA-compliant by architecture.
Scientific literature search
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
Machine learning 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.
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.
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 model size, accuracy needs, and hardware budget. |
| Inference Engines | llama.cpp, Ollama, vLLM, ONNX Runtime, TensorRT | Optimized engines make private AI faster and less expensive to run on your own hardware. |
| Fine-Tuning | Hugging Face Transformers, LoRA adapters, Axolotl, Unsloth | Focused tuning helps the model perform better for your domain without requiring a massive training budget. |
| Document Search & Grounding | LangChain, LlamaIndex, Chroma, Qdrant, pgvector, FAISS | Grounding answers in your documents reduces hallucinations and makes responses traceable to source material. |
| Machine Learning Frameworks | PyTorch, scikit-learn, XGBoost, LightGBM, TensorFlow | We match the framework to the problem: deep learning for unstructured data, gradient boosting for tables, and simpler models when explainability matters most. |
| Model Deployment | MLflow, DVC, FastAPI, BentoML, Docker, Kubernetes | Versioning, testing, and containerized deployment make the system easier to reproduce, operate, and update. |
| Computer Vision | YOLOv10, SAM2, OpenCV, torchvision, Detectron2 | Detection and segmentation models are tuned on your imagery for high-accuracy inspection, lab, or field workflows. |
| Data & Embeddings | SentenceTransformers, spaCy, Pandas, DuckDB | High-quality embeddings power semantic search; we select embedding models for domain fit, not just benchmark scores. |
Common questions about our AI services
Ready to deploy AI on your terms?
Start with a free consultation to scope your use case and find the right approach for your data, infrastructure, and budget.