Ingestion
Parsers for the messy reality of PDFs, scans, spreadsheets, and legacy exports.
Intake001
Service / Data & Retrieval
Make Your Data Answerable
Pipelines, indexes, and retrieval systems that turn a warehouse full of documents into something a model can reason over reliably.
- Search strategy
- Hybrid
- Every answer
- Cited
- Every index
- Versioned
- Retrieval quality
- Measured
What is included
Inside Data & Retrieval
The concrete pieces of work an engagement covers.
Chunking
Segmentation tuned to your documents, because default chunk sizes rarely survive contact.
Preparation002
Hybrid Search
Dense and sparse retrieval combined, then reranked, because neither wins alone.
Retrieval003
Citations
Every generated claim traceable to the passage and page it came from.
Trust004
Freshness
Incremental reindexing so answers reflect the document that landed this morning.
Currency005
Retrieval Evals
Recall and precision measured per query class, tracked over time like any other metric.
Quality006
Unique approach
Retrieval That Holds Up
A retrieval demo works on ten documents. Production starts at ten thousand.
With DarCode
- Hybrid retrieval with a reranking stage
- Chunking tuned against your documents
- Recall measured per query class
- Answers cited to page and passage
- Incremental reindex as documents change
- Access control enforced at retrieval
Typical alternative
- Pure vector search and default settings
- Fixed chunk size copied from a tutorial
- Quality judged by trying a few questions
- Confident answers with no provenance
- Full rebuild, run manually, when remembered
- Permissions checked only in the interface
How it runs
From Kickoff To Handover
The same sequence every time, compressed or extended to fit the engagement.
- 01
Inventory
Understand what documents exist, who may see them, and how they change.
- 02
Parse
Get text out of the awkward formats first. This is most of the work.
- 03
Index
Chunk, embed, and index with a strategy chosen by measurement.
- 04
Tune
Rerank, adjust, and re-measure against a labelled query set.
- 05
Serve
Expose retrieval behind an API with permissions enforced at query time.
Tooling
What We Reach For
Defaults, not dogma. The stack follows the problem.
- PostgreSQL
- Qdrant
- Milvus
- Elastic
- Snowflake
- Databricks
- BigQuery
- LangChain
FAQ
Frequently Asked Questions
Questions we get asked about data & retrieval.
Often not. Postgres with pgvector handles a surprising share of production workloads. We add a dedicated vector store when scale or feature requirements justify it.
Access control is enforced at retrieval time, so a user can never receive a passage from a document they are not entitled to read.
We run OCR with layout awareness, then validate extraction quality on a sample before the corpus is indexed.
A labelled query set with expected passages, scored for recall and precision per query class, tracked on every change.
Get started
Let's Build It, Together
Tell us what you are trying to ship. We will tell you the three shortest paths to it, and which one we would actually take.