Fine-tuned models on your own infra.
Custom AI, fine-tuned models and own infrastructure
We design and build company-specific AI systems where performance, data security and accountability stay in your hands. Instead of generic chat wrappers over third-party cloud APIs, we build fine-tuned models and production agents directly inside your core workflows.
When a general-purpose model is too heavy, slow, expensive or inaccurate for your domain language and decision rules, we fine-tune an open-source language model on your own material. A task-specific model knows your terminology, follows the required data schema reliably and runs at a fraction of the latency and operating cost of general models. Accuracy is verified against your own evaluation dataset before anything enters production.
Business-critical contracts, quotes, customer records and financial vouchers do not belong in external model providers' clouds. We deploy the models, retrieval pipelines (RAG) and AI agents on your own servers (on-prem) or in an isolated private cloud, keeping the data, model weights and logs wholly under your control. Where a workflow carries operational or financial responsibility, the AI prepares, classifies and explains the proposal with source links, and a person approves the decision (human-in-the-loop).
Why your own model and infrastructure
Fine-tuning beats general models on accuracy, speed and cost
An open-source model fine-tuned on your company's data, terminology and rules performs the exact job it was trained for: classifying, extracting structured data or preparing decisions fast and at a predictable cost.
Full data ownership: on-prem or private cloud
Sensitive customer, contract, production and financial data is processed on your own servers or in your private cloud. Not a single prompt or document leaks to third-party APIs or external training runs.
Human-in-the-loop keeps accountability clear
AI never makes blind commitments or ledger entries. We build checkpoints into the workflow where the agent shows its proposal, source document and reasoning, and the responsible person approves, edits or rejects the action.
Internal knowledge search (RAG) and document automation with citations
We connect the language model to your databases, contracts and technical documentation while enforcing role-based access rights. Every answer and extracted field points back to its exact source.
What the delivery covers
- Specification of the use case, data, security boundaries and evaluation metrics
- Fine-tuning of an open-source language model and verification against your own test set
- Internal knowledge search (RAG), document automation and production agents inside your workflow
- Deployment on your own infrastructure (on-prem or private cloud), access control and audit trail
- Human-in-the-loop checkpoints, automated evaluation pipeline, documentation and handover
Where we start
We start with the operational task AI must improve: which data is involved, where the model must run, what accuracy threshold it has to pass, and where a person makes the final decision.
Where should you start?
Describe the situation briefly. You get a proposal for the first step and how it would be priced.
Opens in your email as a ready draft.