Your Expertise Is Scattered. We Connect It.

Structured data (CRM records, financial transactions, project allocations, timesheets) sits in relational databases and is queried with SQL. Letting a user ask questions in natural language and translating that into accurate SQL is a well-understood goal. In practice, it is one of the more difficult problems in applied AI.

The challenges are specific. Business databases have complex schemas, often hundreds of tables with non-obvious naming conventions and relationships. A question like “what is our revenue this quarter?” might require joins across invoice tables, payment tables, and client tables, with filters for date ranges, currencies, and accounting periods. The AI system needs to understand the schema, the business logic, and the user’s intent simultaneously.

Accuracy is non-negotiable. An incorrect SQL query does not produce an obvious error. It produces a plausible but wrong number. A join that misses a filter condition, an aggregation that double-counts, or a date boundary that is off by one day can produce a result that looks reasonable but misleads a decision. This is why text-to-SQL systems require careful schema documentation, query validation, and often a feedback loop where successful queries are captured and reused as few-shot examples to improve accuracy over time. We invest heavily in this layer because structured data is where the numbers live: revenue, pipeline, utilisation, capacity, aged debt. If the numbers are wrong, nothing built on top of them is trustworthy.

Unstructured data (documents, proposals, meeting transcripts, emails, internal policies) requires a fundamentally different retrieval approach. Semantic search uses vector embeddings to find content by meaning rather than keyword matching. A search for “data strategy” can surface a document titled “analytics roadmap” because the concepts are related, even though the words are different.

The promise is compelling. The engineering is demanding. Every stage of the pipeline affects retrieval quality.

Chunking determines how documents are split for indexing. Too large and the chunks contain too much irrelevant context. Too small and the meaning is fragmented. The optimal chunk size varies by document type: a legal contract, a meeting transcript, and a technical specification have different internal structures and require different strategies. Overlapping chunks, hierarchical chunking, and document-aware splitting all have trade-offs. Embedding models map text into vector space where similarity can be measured. The choice of embedding model, the dimensionality of the vectors, and how well the model handles domain-specific language all affect whether the right content surfaces. A model trained on general web text may not understand that “PE” means “Private Equity” rather than “Physical Education” in a consulting context.

Retrieval is only half the problem. Once relevant chunks are retrieved, they must be presented to a language model with a prompt that produces an accurate, grounded response. Poor prompting leads to hallucination (the model fabricates information not present in the retrieved content) or to shallow summarisation that strips the nuance from the source material. Effective retrieval-augmented generation requires prompt engineering, source attribution, and confidence scoring so that answers are traceable to the documents they draw from. We treat each of these stages as an engineering problem with measurable accuracy, not as a configuration exercise.