Research Platforms
Internal platforms that turn scattered documents and data into searchable, citable, compounding knowledge.
Research teams lose their edge in lost context: findings buried in decks, data trapped in exports, sources unciteable. We build platforms that ingest heterogeneous material, resolve entities across sources, and make everything retrievable by meaning as well as keyword, with provenance preserved, because uncited knowledge is rumor.
Who this is for
Investment firms, research groups, and strategy teams whose knowledge currently lives in inboxes.
How the work is done
Model the domain
Information architecture first: the entities (companies, people, markets, documents), their relationships, and the questions the platform must answer. Schema design here determines usefulness for years.
Build the pipelines
Ingestion from documents, APIs, and databases with entity resolution, record-linkage and fuzzy-matching logic that knows “Acme Corp” and “ACME Corporation” are one company, with review queues for ambiguous merges.
Make it retrievable
Hybrid search: full-text indexing plus vector embeddings for semantic retrieval, filtered by structure (entity, date, source type). Provenance and citation tracking on every fact surfaced, answers link to sources.
Ship the workflows
Not just search: monitoring views, comparison tables, export-to-memo paths, and access control that respects information walls. Adoption is instrumented so we know what is used, not just what was built.
How the Research Platforms engagement runs
We begin with the decision, use the evidence that can genuinely change it, and make the reasoning reviewable from first input to final handover.
What we need to begin
- The sources to ingest, with access credentials and terms of use for each.
- Sample documents that cover the messy cases, not the clean ones.
- The entities that must resolve (companies, people, products) and the rules that constitute a match.
- The queries the platform must answer well on day one.
If an input is unavailable, we state the gap, its effect on confidence, and the agreed workaround. It is never quietly ignored.
Your four-phase engagement map
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Phase 1
Model the domain
Information architecture first: entities, relationships, and the questions the platform must answer.
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Phase 2
Build the pipelines
Ingestion from documents, APIs, and databases with entity resolution and review queues for ambiguous merges.
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Phase 3
Make it retrievable
Hybrid full-text plus vector search filtered by structure, with provenance and citation on every fact surfaced.
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Phase 4
Ship the workflows
Monitoring views, comparison tables, export-to-memo paths, and access control, with adoption instrumented.
Methods and models we draw on
- Information architecture & domain modeling
- ETL/ELT pipeline design
- Entity resolution & record linkage
- Hybrid full-text + vector search
- Provenance & citation tracking
- Access-control design
- Usage instrumentation
Methods are chosen for the problem, not the brochure, expect a subset of these, applied properly, plus whatever the evidence demands.
The decision this enables
Institutional memory that compounds: every past project makes the next one faster.