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Collections from 100,000 to multiple millions of documents combine scans, emails, office files, duplicates, versions and matter-specific access controls. The engineering challenge is not merely semantic search: every result needs defensible provenance, controlled access and measurable retrieval behavior.
DEMAND LANGUAGE / REAL-WORLD PROBLEM
“It works in the demo — but will it work in daily operations?”
“How do we measure whether the problem is actually solved?”
WHAT CAUSES THIS?
OCR and parsing errors silently remove searchable evidence.
architecture_for LEGAL DOCUMENT INTELLIGENCE
We start with corpus forensics: file types, OCR quality, duplicates, document families, matter boundaries, access policy and review workflows. The platform then preserves immutable source identity while derived representations remain versioned and rebuildable. Retrieval combines lexical precision, semantic recall, metadata filtering and reranking; generation is optional and remains downstream of evidence retrieval.
Matter isolation and ACLs should constrain retrieval before content is exposed to ranking or generation. Depending on policy, execution can use EU-hosted controlled services or on-premise/private model infrastructure. Keys, indexes, caches and evaluation data need the same matter-aware security model as source documents.
At multi-million scale, measure ingestion throughput, OCR queue depth, index size, filter selectivity, candidate latency, reranker depth, p95/p99 search latency and cost per reviewed/retrieved document. Architecture choices should be benchmarked on the actual corpus distribution.
legal document AI · hybrid retrieval · OCR · reranking · ACL · provenance
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measure_kicker
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CTO / CIO FAQ
Yes, if ingestion, indexes, ACL filters and reranking are designed for the corpus and measured under representative load. The exact architecture depends on document distribution, update rate and isolation requirements.
No. Controlled EU-hosted or on-premise/private execution patterns are possible where policy or client requirements demand them.
Keep immutable source identifiers and hashes, version derived artifacts, and make every citation resolve back to a stable source location such as document plus page/section.
No. This is technical platform and engineering work for document retrieval, review support, evidence handling and controlled AI execution.
Evaluate them independently and as a fused system against known-relevant sets. Exact identifiers and citations often favor lexical retrieval; conceptual queries can benefit from semantic candidates and reranking.
Measure each stage separately: OCR, parsing, embedding, indexing, reranking and generation. Cache/reuse derived artifacts and reserve expensive model calls for stages where evaluation shows measurable value.
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