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Data & AI

MLOps & AI Infrastructure Consulting

Infrastructure, pipelines and evaluation for AI systems — including telling you when you do not need one.

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Overview

Retrieval-augmented generation is the most useful pattern in business AI at present, and also the most over-applied. It suits problems where the same knowledge is looked up repeatedly, the source material is written down, and being occasionally wrong is recoverable. It does not suit precise calculations, live transactional queries, or knowledge that exists only in someone’s head.

Where there is a genuine case, we build the surrounding infrastructure properly: retrieval that returns the right passages, permissions applied at query time, an evaluation set so quality is measured rather than assumed, and cost modelling that includes re-indexing and inference.

What you get

Retrieval done properly

Structure-aware chunking, metadata filtering and source hygiene — where these systems usually succeed or fail.

Permissions at query time

The assistant applies the same access rules as the underlying systems, rather than a cached copy of them.

Measured, not assumed

An evaluation set of real questions with known answers, scored on every change, including correct refusals.

Operating cost modelled

Embedding, re-indexing, inference and human review time budgeted before you commit, not after.

How we work

  1. 01

    Qualify

    We assess whether the problem genuinely suits AI, and say plainly when a search box or a report would do.

  2. 02

    Prepare

    Source material identified, authoritative versions agreed, and superseded documents excluded from the index.

  3. 03

    Build

    Retrieval, serving, access control and monitoring implemented, with evaluation running from the start.

  4. 04

    Evaluate

    A pilot with one team, with every question and every negative rating logged and reviewed.

Common questions

Should we fine-tune a model?

Rarely as a first step. Retrieval solves most business problems more cheaply and stays current as documents change. Fine-tuning suits format and tone, not knowledge.

How do we know it is working?

Write thirty to fifty real questions with known answers before launch and score every change against them. Correct refusals matter most — an assistant that says "I cannot find this" is more valuable than one that always answers.

Where does AI not belong?

Precise calculations, live transactional lookups, undocumented knowledge, and decisions with legal or safety consequences where no human is accountable for the outcome.

Often paired with

Ready to talk about mlops & ai infrastructure consulting?

We will tell you what we would do, roughly what it costs, and whether it is worth doing yet.

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