ADI connects your private markets data into one governed set of records, so the workflows built on top of it stop depending on manual entry. Ingest, integrate and report. Integrate, ingest, and report. One platform for all of it.
Most vendors sell these as separate products. Here they share one data model, so a correction made in extraction shows up in reporting without a reload.
Email, cloud drives, SFTP, document portals and data-warehouse connectors, all flowing into a versioned store.
OCR, a classification taxonomy, schema-driven extraction into runtime-typed models, a self-service extraction wizard and zero-shot named-entity recognition with a feedback loop.
A versioned repository for every document: classified, searchable and archived with a complete audit trail. Every document version is tracked, time-stamped, and archived for full traceability across reporting periods.
Ensemble matching with adaptive confidence, graph clustering, and golden records tied to your source of truth, enriched from commercial reference universes like S&P Capital IQ, Preqin, Cherre and ICE, with public identifiers (LEI, FIGI) layered on where they exist.
Document extraction, data quality, reconciliation and reporting agents on one governed layer, plain-language questions compiled to SQL, dashboards on demand, and an MCP server for external AI clients.
Per-tenant vector collections, dual chunking strategies and a semantic catalog, five collections per vertical across assets, documents, summaries, entities and parties.
Maker-checker task queues with field-level edits, approve and reject, and a full audit trail, on an orchestrator with a scheduler, event router and autoscaler.
NL to SQL, drag-drop dashboards, charting and PDF export, holdings and ownership graphs, valuation and a query lab, plus RAG-driven branded Word and PDF report synthesis.
A distribution layer with governed reader accounts, table sharing and reporting-client provisioning, white-label data delivery on top of verified data.
Per-tenant isolation, role-based access by organization and department, secret management, configuration services and platform-wide observability.
Email, GP portals, SFTP, cloud drives and custodian feeds, with background loops that pull the moment a document posts.
Schema-driven extraction maps each statement into defined, validated data, not a wall of text.
Any entity: clients, portfolios, assets and people, resolved to one master record and tied to your source of truth.
A maker-checker queue surfaces only the fields the model is unsure of. Humans spend minutes, not hours.
Validated transactions posted into the systems you already run. Every field traceable to source.
Branded client reporting and governed data delivery, built on data you already trust.
A NAV figure is unusable until you know which fund, vehicle and investor it belongs to. Aggregation tools hand you the figure and leave the attribution to you.
Four stages sit between an extracted field and a posted one: validation, cross-document checks, human review and writeback QA.
Every agent runs on the same rails: typed schemas, identifier grounding, a human review gate and writeback QA. Nothing reaches your system of record unreviewed.
Once every field is extracted, mastered and checked, two things come for free: a portfolio you can query in plain language, and reporting that builds itself from numbers you already trust. Questions compile to SQL and run against governed tables: the model writes the query, the database does the math, so answers are deterministic, auditable and cheap to run at volume.
Models change every quarter, so the platform stays portable across them and keeps the grounding, schemas and review gates in its own layer.
Aggregation platforms are strong at the top of the stack: portals, categorization, extraction. The differences start below that, in accuracy, entity mastering, where processing runs and what it costs to operate.
A working session on your real documents and systems, across the capabilities your team needs most.