Software & Data Engineering
The pipelines and infrastructure underneath.
A retrieval system is only as good as the text underneath it. Extraction that silently drops a table, or chunking that separates a requirement from its exception, produces answers that are confidently and precisely wrong — and very hard to trace, because the passage genuinely appears to support them.
What we build
Pipelines that move sensitive data across environments without losing its structure or its provenance. Extraction is verified against the original, chunking follows the document’s own sections and clauses, and every record carries the metadata that says which version governs.
Built for scrutiny
In compartmented environments, access control is applied at retrieval time rather than as a filter afterwards. A layer that ranks across everything and filters later has already leaked: the ranking itself is a signal about content the asker is not cleared to see.
What this covers
- Ingestion and extraction
- Including scanned pages and tables, verified against the source document rather than assumed to have worked.
- Transformation pipelines
- Idempotent, observable, and re-runnable, so a bad batch is corrected rather than worked around.
- Indexing and storage
- Search and vector infrastructure sized to the corpus, with provenance carried as structured metadata.
- Secure environments
- Data residency and access control scoped per engagement, including on-premises and air-gapped deployments.