Document assistance agents - the extraction & Q&A stage of your document workflow
A document workflow has 5 stages: ingestion, extraction, validation, generation, audit. Document assistance agents are the extraction and Q&A stage - the part that reads your 500-page lease, your blurry mobile photo of a customs invoice or your 200 sub-contractor compliance packs and returns structured fields with citations. They are not chatbots. They are workflow components that hand their output to validation logic, Paperless generation and an audit dashboard.
Where document assistance agents pay back fastest
- Lease and contract clause lookup - 'find every break clause that triggers in 2026'.
- Blurry mobile photos and tilted scans of customs invoices, packing lists or BoLs that general AI misreads or hallucinates around.
- Audit-prep search across SharePoint, Google Drive, email and old PDFs.
- Multilingual policy Q&A - one source of truth, EN/LT/PL/RU answers with citations.
- Sub-contractor compliance Q&A - 'is this RAMS up to date for site X' from a 1,000-doc archive.
How they fit into your document workflow
- Visual grounding - the agent only extracts a value if it can prove the page and pixel coordinates it came from. No hallucinated numbers.
- Output flows straight into the next workflow stage: 3-way matches, policy checks, expiry validation, then Paperless generation.
- Plug into your CRM, TMS (e.g. CargoWise), ERP or SQL database during extraction - so cross-checks happen in real time.
- Permissioned per team, EU hosting, zero-retention model providers, full audit log per question and per extraction.
- Multi-language by default - extracts and answers in EN, LT, PL, RU, DE.
Common questions
How does this fit into the rest of the document workflow?
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Document assistance agents are stage 2 of 5: Ingestion -> Extraction (this) -> Validation / Logic -> Paperless Generation -> Audit & Governance. The agent reads the document, returns structured fields, and hands them to validation - which then triggers Paperless to generate the final compliant output. We rarely ship the agent on its own; we ship it as part of the full workflow.
What happens with blurry scans, tilted mobile photos or low-quality PDFs?
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This is exactly what we built our extractor pipeline for. Our visual grounding pipeline normalises orientation, locates fields, and only emits a value when it can prove the pixel location. Where confidence is low, the field is flagged for human review rather than guessed.
Will the agent hallucinate values?
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No - because it isn't allowed to. Extractions require a verifiable source citation (page + bounding box). If the agent can't prove the value, it returns 'not found' and escalates. The audit dashboard shows exactly where every value came from.
Can the agent call our CRM, TMS or SQL database during extraction?
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Yes - tool use is core. Common patterns: match an incoming invoice to a job in CargoWise, look up a sub-contractor's insurance status in your compliance DB, validate an HS code against your product catalogue.
Where is the data stored and which models do you use?
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EU hosting by default. We're model-agnostic (OpenAI, Anthropic Claude, Google Gemini, self-hosted open-source) and configure providers for zero retention. We sign DPAs as a matter of course.
How is this different from ChatGPT or a generic AI assistant?
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ChatGPT is a generic chat window. A Zeroflowers document assistance agent is one stage of an automated document workflow - it's permissioned, it cites its sources, it integrates with your CRM/TMS, and its output flows into validation and Paperless generation, not back to a human to copy-paste.
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