Service
RAG and document intelligence
Most document AI fails because it treats retrieval as plumbing. Good RAG is a product surface: source selection, chunking, evaluation, citations, refusal behaviour, and operator trust all matter.
iftodo builds document systems for policies, contracts, technical notes, research collections, support knowledge, and internal process material. The aim is not a magic library. It is a reliable path from question to cited evidence.
Where this fits
- Important answers live across PDFs, docs, tickets, notes, and internal pages.
- Users need citations, uncertainty, and escalation when the source material does not support an answer.
- You need evaluation fixtures before expanding to more collections or higher-stakes use.
What gets delivered
- 01
Corpus analysis, ingestion design, metadata model, retrieval strategy, and answer policy.
- 02
Working RAG or document-intelligence prototype with citations, source previews, and refusal paths.
- 03
Evaluation set covering answer quality, source grounding, stale material, missing context, and edge cases.
What I need from you
- Representative documents and the questions people actually ask.
- Known bad answers, ambiguity, stale policies, and source-of-truth rules.
- Access constraints for who may see which document or answer.
How the work runs
- 01
Audit the corpus before choosing retrieval mechanics, because document shape decides system shape.
- 02
Build ingestion, metadata, and retrieval with visible intermediate results.
- 03
Evaluate answers against source support, not just fluency.
Acceptance and evaluation
- Answers include useful citations and avoid statements unsupported by retrieved sources.
- The system refuses, asks for clarification, or escalates when evidence is missing.
- Regression fixtures catch drift when documents, prompts, or models change.
Evidence and work
Common questions
- Can you work with messy documents?
- Yes, but messy source material is treated as a design constraint, not hidden. The first pass identifies what can be answered reliably and what needs cleanup or policy decisions.
- Do you use vector search only?
- Not by default. Keyword search, metadata filters, reranking, structured extraction, and smaller deterministic steps are often just as important.
- Will it answer every question?
- A trustworthy system knows when the corpus does not support an answer and says so with a useful next step.