RAG & Document Intelligence
Retrieval, chunking, document processing and evaluation for systems that have to answer from your documents, consistently.
Sounds familiar?
- 01The same question returns different quality answers depending on phrasing.
- 02“It can't find it” is a daily complaint from users or support.
- 03Table, layout or multi-document questions fail unpredictably.
- 04Nobody knows which retrieval change actually improved anything.
What it costs to leave it
- Users stop trusting the feature and route around it.
- Document processing costs scale without improving answers.
- High-stakes answers remain unauditable and uncorrectable.
What the engagement covers.
Document processing pipelines for real-world messiness: layouts, tables, scans, versioning.
Retrieval architecture: chunking strategy, indexing, hybrid search, reranking.
Query understanding and multi-step retrieval for complex questions.
Citation and grounding behavior so answers are auditable.
Retrieval evaluation: known-answer sets, precision/recall targets, regression suites.
How the engagement runs.
Profile the document estate: formats, quality, churn, sensitivity.
Build a retrieval evaluation set and measure current quality.
Fix processing, chunking and retrieval architecture against the baseline.
Regression suites and monitoring for ongoing corpus change.
Deliverables
Document processing pipeline design and implementation
Retrieval architecture with chunking/indexing strategy
Known-answer evaluation set and retrieval metrics
Grounding/citation behavior specification
Regression suite wired to corpus and code changes
A good fit when
- Teams whose product must answer from documents users rely on.
- Organizations with large, changing, imperfect document estates.
- Regulated or high-stakes domains where answers must be auditable.
Probably not, when
- Marketing-site chatbots answering from a handful of PDFs.
- Teams unwilling to label a ground-truth evaluation set.
Common questions.
Can you take a prototype into production?
Yes. Prototypes usually prove relevance but not reliability; productionization is exactly the processing, evaluation and monitoring work this service adds.
Do you handle the full-stack integration?
Yes. From document ingestion through retrieval to the product surface, including your existing storage and permission systems.
Can you work with sensitive data?
Yes. Document processing can run inside your infrastructure, and data flows are designed around your constraints rather than around our convenience.
Which vector database do you use?
Whichever fits: existing Postgres with pgvector, an established vector store, or hybrid search over your existing engine. The database is rarely the bottleneck.