Data & Intelligence Engineering

Make enterprise data usable by AI.

Data & Intelligence Engineering

Make enterprise data usable by AI.

An AI system is only as good as the information it is given. Most enterprise data was never organised for software to read: attributes recorded inconsistently, specifications buried inside documents, categories that made sense to whoever built them a decade ago. We put that right, so search systems, models and assistants have something reliable underneath them. It is the part of an AI project most often underestimated, and most often responsible for disappointing results.

Where to start: A data readiness review, a product data audit, or an assessment of the documents you want AI to be able to answer from.

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Business Challenges We Solve

What We Analyze

Product & Catalogue Data Intelligence

Turn inconsistent product data into structured, usable information.

Enterprise catalogues collect years of inconsistency. The same attribute is recorded five different ways, specifications sit buried inside free text, and categories no longer match how customers search or how dealers look things up.

We extract, standardise and enrich that data into a consistent structure, and design category systems around real search behaviour rather than internal convention. This is ordinary, careful work, and it usually decides whether search, recommendations and assistants function at all.

Knowledge Base & Retrieval Engineering

Build the retrieval layer that lets AI answer from your documents, with sources.

Policies, standard operating procedures, product manuals, service records and technical documentation sit across disconnected systems, in formats never intended to be read by software.

We build the ingestion, indexing and retrieval layer that makes this content searchable by meaning, with citations so every answer can be traced to the document it came from, and with testing so retrieval quality is measured rather than assumed. This layer is what turns a general-purpose AI assistant into one that can be trusted on your own material.

Data Readiness & AI Pipelines

Check the data can support the plan, then keep it flowing.

Before model work starts, we establish whether the available data can actually support it: how much there is, how consistent it is, how it is labelled, and where it came from. Gaps that would otherwise appear late and expensively get dealt with first.

We then build the pipelines that keep information moving into live AI systems, so what the system knows reflects the business today rather than an export taken months ago.