Generic LLMs weren’t trained on your data, don’t understand your workflows, and often fail in high-stakes environments. We build models that are fine-tuned on proprietary content, aligned with operational logic, and predictable under pressure.
Custom LLMs deployment options—including on-prem and private cloud—mean your data stays protected, your compliance posture stays intact, and your system behavior stays governed. You control how it runs and what it returns.
We develop integrated LLM systems that solve specific problems—from summarising contracts to automating internal support. Our work includes fine-tuning open models, designing retrieval pipelines (RAG), enforcing output structure, and aligning models via RLHF.
More than model training, we build operational systems: versioned APIs, monitored inference, fallback logic, and safety layers. Every component is selected to meet your requirements for accuracy, latency, and accountability.
Miniml start with intent—what the model needs to solve and how success will be measured. From there, we assess your data, infrastructure, and integration points, then design a model pipeline that fits your environment and constraints.
Training involves curating a task-aligned corpus and applying the right fine-tuning and optimization methods. Deployment includes system integration, governance hooks, and telemetry—so your model is observable, testable, and adaptable over time.
Miniml‘s models are designed to operate reliably in real-world environments—with robust APIs, access controls, monitoring, and performance safeguards built in from the start.
We minimise hallucinations through structured prompting, output constraints, fallback logic, and fine-tuning aligned to your domain. Control interfaces give your team the ability to govern, audit, and iterate safely as requirements evolve.
