Messy source ingestion
PDFs, office documents and HTML that extract into soup: broken reading order, tables flattened, headings lost.
Use case · RAG and search teams
Clean, traceable documents and evaluation data for RAG
Retrieval quality is mostly an ingestion problem. Structure that survives extraction, chunks that keep their lineage, and an evaluation set with unanswerable questions in it.
01
Where teams usually are when they call
your situationEach one points at a different product — the decision helper below turns the symptom into a starting point.
No ground truth for evaluation
No way to tell whether a retrieval change helped, because there is no labelled set to measure against.
Chunks without lineage
An answer cites a chunk, and nobody can say which document or passage it came from.
02
The products that answer them
what we build for thisQ&A & Instruction Data
Grounded Q&A and RAG evaluation records with evidence spans, citation IDs and answerability labels.
- grounded
- answerable
Monolingual Corpora
Clean, structured source documents with the extraction quality retrieval depends on.
- structure
- lineage
Custom Dataset Development
Your chunking rules, your metadata, your delivery channel — as a repeatable pipeline.
- chunk rules
- refresh
03
Start from the symptom, not the catalogue
decision helper| If you need | Start here | Why |
|---|---|---|
| Better extraction from PDFs and office files | Monolingual corpora | Format-aware extraction, reading order and tables preserved |
| Chunks you can trace | Custom dataset development | chunk_id and parent_doc carried on every record |
| A retrieval evaluation set | Q&A & instruction data | Answerable and unanswerable questions with citation targets |
| Hard negatives | Q&A & instruction data | Adversarial and near-miss passages, labelled |
| Recurring refresh of the index source | Custom dataset development | Delta releases with a changelog |
04
What we measure on this kind of project
quality controls that matter hereExtraction quality
Reading order, tables, captions and formulas preserved as specified.
how measured · parser checks + human sample
Citation validity
Every evidence ID resolves to real text in the cited chunk.
how measured · automated, full pass
Answerability balance
Share of unanswerable questions in the evaluation set.
how measured · per release
Chunk lineage completeness
Chunks carrying a resolvable parent document and span.
how measured · manifest validation, full pass
05
Chunks that keep their parent
chunkingBring us a language, a domain or a schema.
We will help define a feasible pilot, quality criteria and a delivery format for your model.