Large Language Models: AI engineering

Overview

A general-purpose model knows the public internet. It does not know your policies, contracts or operating procedures, or who in the organisation is allowed to see which of them. Our language model work closes that gap without sending sensitive data somewhere it should not go.

We build private language model systems grounded in company knowledge through retrieval, with access control applied per team or business unit, so a model only answers from documents the person asking is allowed to read. Where retrieval is not enough we fine-tune, and every system is measured against an evaluation harness built from real questions before it reaches users.

Deployment follows the data. Models run in a private cloud, on premise or in a specific region where residency rules apply, behind one routing layer that picks a suitable model for each task and logs every call. That keeps the organisation free to change models as better ones arrive, without rebuilding what sits around them.

Capabilities

Private LLM deployment

Models deployed in a private cloud, on premise or in-region, so prompts and documents stay inside the boundary your policies set.

Retrieval over company knowledge

Answers grounded in your documents, procedures and systems of record, with sources shown and each user's permissions respected.

Fine-tuning

Models adapted to your vocabulary, formats and tasks where retrieval alone does not reach the quality the work needs.

Evaluation

A test harness built from real questions and expected answers, run before launch and after every change to the model, prompts or data.

Guardrails and usage policy

Written rules on what a model may answer and do, enforced with input and output checks, redaction of sensitive data and a log of every call.

Model routing

One layer that sends each task to a suitable model, so models can be swapped as they improve without rebuilding the tools and knowledge around them.

Technical detail

Architecture, integrations, deployment and documentation for Large Language Models are published by Opsian Technologies, the engineering arm of Opsian.opsiantechnologies.com

Industries

Questions

Does our data leave our environment?: Not unless you decide it should. Models can be deployed privately, on premise or in-region, and retrieval respects the permissions set in your source systems.
Should we train our own model?: Rarely. Most of the value sits in the grounding, permissions, evaluation and audit around a model rather than in its weights. We fine-tune when evaluation shows it is needed, and say so when it is not.
How do you know the model is accurate enough?: Every system is measured against an evaluation set built from real questions before launch, and measured again after every change to the model, prompts or data.