Most retrieval problems are data problems wearing a costume. The documents arrive in six formats, the same entity is spelled four ways, and nothing carries the metadata a filter would need. We build the ingestion and preparation layer underneath: connectors, normalization, deduplication, chunking that respects document structure, and an index that can be rebuilt without a migration.
01 / DATA
For data that is not retrieval-ready. Connectors, normalization, chunking and indexing: the work that decides whether retrieval finds anything worth citing.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
On DocVerse every extracted metric traces to the page it came from, which only works if the ingestion layer carries provenance from the first step. Bolted on later, it cannot be recovered.
Splitting a table across two chunks makes both useless. We chunk to structure, which is slower to build and the reason retrieval finds anything.
On FinSight, custodian feeds arrive continuously. A pipeline that requires a full rebuild to absorb one change is a pipeline that runs monthly and is always stale.
Week 1
Formats, inconsistencies and the metadata that is missing. Retrieval problems are usually data problems, and this is where that becomes obvious.
Week 1-3
Connectors to where content really lives, with normalization and deduplication across sources that disagree.
Week 2-4
Boundaries that respect sections, tables and clauses rather than character counts, so a retrieved passage is still intelligible.
Week 4-6
Reindexing a change rather than the corpus, so freshness does not mean a rebuild.
ESG & sustainabilityIn production
An ESG-intelligence pipeline that ingests dense, unstructured filings at scale, extracts framework-aligned metrics with full source lineage, connects them across reports and years, and serves them through a governed analytics layer analysts can actually trust.
Fund operationsIn production
An institutional fund and portfolio operations platform. It ingests statements and brokerage data from every custodian, reconciles them automatically, and presents one operational view with the audit and compliance trail regulated finance depends on built in.
Market intelligenceIn production
A reasoning-first prospect intelligence engine that turns a LinkedIn or company URL into a structured, intent-aware profile. Multi-source ingestion and hybrid RAG uncover ICP fit, strategy, and risks, with every claim locked to a real source rather than invented.
Sources arrive by batch, change data capture or stream, and a quality gate checks schema, nulls and duplicates before anything downstream sees them. Rows that fail are held for inspection rather than discarded, because silently dropping them is how a corpus quietly stops matching the business, with no way to explain the gap later. A classify pass marks what is sensitive before transformation, lineage records where every row came from, a backfill path reprocesses history when a rule changes, and staleness raises an alarm instead of passing as fresh.
Production-proven
Built by engineers who've already shipped this in production.
It is the normal starting condition and most of the work. Six formats and four spellings of the same entity is what the pipeline exists to absorb.
No. Connectors read from where content already lives; we would rather not add a migration to the project.
Near real time where the source supports it. Incremental reindexing is built in from the start, because retrofitting it means a rebuild.
Usually the same engagement. Splitting them is how teams end up with good retrieval over a corpus nothing can find anything in.
Tell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.