US long/short equity manager · $10bn–$25bn AUM · 50–100 staff
Choose the research architecture without closing off what comes next.
Independent technical research helped an investment manager separate the decisions it could make now from the complexity it should add only when the evidence supported it.
Engagement
Focused advisory
A technical working session, targeted research and a detailed written architecture response for the firm's technology and data team.
- Shared and private research
- 2 zones
- Technical questions researched
- 5 tracks
- Decisions ordered by evidence
- One sequence
- Future platform options preserved
- No lock-in
The firm was planning an AI-ready research estate at institutional scale. A common pool of external research needed to be searchable across the investment team. Internal notes, models and thesis work needed a separate private layer, isolated at analyst level.
The challenge went beyond choosing a database. The design had to account for extraction quality, retrieval across thousands of companies, source lineage, concurrent use, storage economics and the agents that might later sit above the platform.
The team wanted an independent view before making commitments that would become costly to reverse as the research estate grew.
The architecture sequence
Build the controllable foundation before adding expensive complexity.
The recommendation connected platform design to the research questions, permissions and future agent workloads the system would need to support.
Research architecture decision map
Preserve flexibility while protecting proprietary research
Two knowledge zones
Shared research
Sell-side reports · common corpus
Private analyst work
Notes · models · thesis material
Non-negotiable control
Shared access cannot expose one analyst's proprietary work to another.
Foundation before complexity
Portable raw layer
Keep original documents and source lineage.
Metadata + hybrid retrieval
Combine semantic, keyword and structured search.
Permissions at query time
Preserve analyst-level segregation in every answer.
Sequenced decisions
Choose now
Ingestion, metadata, lineage and access controls
Validate
Retrieval quality, cost, concurrency and traceability
Defer
Knowledge graph and heavier ML until evidence supports them
Resulting direction
A controlled first phase that tests the architecture in the firm's own environment before long-term commitments are made.
What Altitude assessed
Answer the tradeoffs together, not one platform at a time.
Data foundation
Keep source documents in a portable raw layer, with structured metadata and retrieval services above it.
Platform choice
Start with the platform that fits governed document retrieval while preserving options for heavier ML later.
Search quality
Combine semantic, keyword and metadata retrieval with re-ranking instead of relying on embeddings alone.
Knowledge graph
Prepare entities and source references, then add graph traversal only when a defined query requires it.
Private research
Maintain strict analyst-level segregation through storage, retrieval and every downstream answer.
Economics
Separate storage, indexing and serving so cost, capacity and latency can be observed independently.
A decision framework
Some choices were ready. Others needed to earn their place.
Altitude recommended reliable ingestion, structured metadata, source-linked hybrid retrieval and access controls as the first foundation. Those capabilities were useful under any later platform choice and made retrieval failures easier to diagnose.
A knowledge graph could help with supply-chain, causal and cross-company questions. Adding it immediately would also create an ontology and maintenance burden before the team had shown that ordinary retrieval was insufficient. The recommendation was to preserve the ingredients for a graph, then introduce it for a defined query class.
The same principle applied to the platform stack: use a bounded first phase to test quality, permissions, traceability, expected volume and concurrent cost in the firm's own environment before making a long-term architecture commitment.
What the engagement established
A practical sequence for moving forward without pretending every decision was settled.
The client received an independent, researched view of the architecture, the tradeoffs behind it and the evidence required for the next decision. This engagement covered technical advice rather than a platform build, so no implementation outcome is claimed.
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 →This engagement also touched Fractional AI Officer.
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Choosing an AI research architecture?
Bring the use cases, constraints and platforms already under consideration. We'll research the tradeoffs, challenge the sequence and identify the smallest architecture decision worth making next.