New York equity hedge fund · $1–5bn AUM
The research was there. The answers were hard to find.
The firm's knowledge lived across email, SharePoint, files and a research management system. We mapped the estate, tested the technology and designed one cited query layer across it.
Decision reached
Full build approved
The tested discovery became the specification for the client's Azure research data lake.
- Discovery and technical testing
- 2–3 months
- Source figures retained by selected extraction paths
- 99.9%
- Requested research report ranked first
- 28 / 28
- Evaluated answers returned with citations
- 22 / 22
An internal note or important attachment might sit only in one person's mailbox or SharePoint folder. When an analyst needed it, there was no central place to ask. People emailed around the firm to learn who had seen a report, saved an attachment or written a prior view.
The firm wanted an easy way to query that distributed knowledge as one research corpus, extract useful insights and move from every material claim back to its evidence.
Seven discovery sessions mapped how the team screened ideas, prepared for earnings, reviewed prior conclusions and shared research. The work connected each source to a real investment question before making any platform choice.
Inside the query layer
One question. Several forms of evidence.
The simple experience depended on a layered system that prepared the corpus once, then routed each question through the evidence paths it actually required.
Research ingestion and query flow
Distributed knowledge · prepared once · routed by question
End-to-end pipeline
INGEST → ENRICH → ROUTE → RETRIEVE → CITE
Distributed knowledge
Start where the firm's research already lives.
SharePoint
Research system
Shared files
Prepare the corpus
Extract
Preserve tables and figures
Clean
Remove boilerplate and noise
Enrich
Generate metadata and entities
QUERY
ROUTER
Classify intent, apply permissions and prioritize current information.
Select the evidence paths
Metadata
Hybrid retrieval
Knowledge graph
Deterministic tools
Cited answer
Combine the relevant paths into one response while retaining the route to every supporting passage.
Measured quality gates
99.9%
Source figures retained
28 / 28
Requested report ranked first
44 / 44
Evaluated claims grounded
115
Evidence-backed relationships
How a query was resolved
More than a search box over a folder.
Each layer addressed a different reason that a plausible research answer can still be incomplete, stale or wrong.
- Layer 01
Clean and preserve
Convert research into structured content while preserving tables and figures, then remove repeated disclaimers before they can dilute retrieval.
- Layer 02
Generate metadata
Enrich each item with company, ticker, author, date, document type, themes and an internal or external source flag.
- Layer 03
Retrieve evidence
Combine structured filters with semantic and keyword retrieval, keeping the supporting passages attached to the answer.
- Layer 04
Traverse relationships
Use a knowledge graph to surface suppliers, customers, competitors and partners that direct document search may miss.
- Layer 05
Route the question
Apply metadata, recency, retrieval, the graph and deterministic calculations according to what the analyst is actually asking.
Test before choosing
Access was useful only if the answer could be trusted.
Twelve document-extraction approaches were evaluated against a 17-document reference set containing dense financial tables. Some produced readable text while dropping or introducing figures. The selected paths retained 99.9% of source figures with no fabricated figures in the test set.
Three candidate search platforms then faced the same documents and 28 questions. The selected configuration returned the requested research provider's report first on all 28 questions, without naming or relying on vendor demonstrations.
A separate 22-question evaluation returned citations on every answer. All 44 claims assessed for groundedness were supported by retrieved evidence. A weak case involving internal notes was documented and carried into the build plan rather than being hidden in an average.
A smaller data problem
Store what becomes more valuable together. Fetch the rest when needed.
Hard-to-retrieve research, internal notes, email and the firm's research history belonged in a governed, searchable layer. Prices, filings, estimates and other accessible structured feeds could remain live, avoiding a second stale copy.
Sources with unresolved access or content rights remained conditional until tested and cleared. The architecture did not assume that a visible connector or existing licence permitted long-term retention.
Testing also exposed a capacity constraint before historical backfill began. The pilot tier supported the proof but would constrain the intended archive. The production recommendation corrected the sizing before that decision became expensive to reverse.
What changed
The client moved from a broad ambition to a tested full-build specification.
The client saw that relevant answers could be returned quickly from a broad research estate while noise was removed, current information was prioritized and supporting passages remained available for review. The tested proof sat within a wider estate of thousands of documents, messages and notes.
The fund decided to proceed with the full Azure research data lake. The discovery findings became the architecture, quality gates and implementation sequence for the next engagement.
The published measurements describe the evaluated proof-of-concept corpus. Investment use still requires source review.
The service behind this work
Data & AI Foundation
Governed, portable data, retrieval and firm memory that make research usable by people and approved agents.
See how the Data & AI Foundation works →Read next
Azure data-lake build
An Azure research data lake under firm control
More than 10,000 research records moved through governed Azure ingestion, extraction, search, identity and audit into one cited query layer.
Data & AI foundation
An inbox of research turned into an indexed history
More than 500 daily research items became one queryable record across time, topics and tickers, with the original source one click away.
Is valuable research trapped by person or system?
Start with the questions your team repeatedly asks, the places the evidence currently lives and the quality threshold an answer must meet. The first step is to prove the smallest research foundation worth building.