Why Document AI?
Back-office teams lose meaningful time to reading and re-typing the same kinds of documents — invoices, contracts, claims, leases, forms. Document AI uses modern AI to extract the right fields, classify the right things, summarise the right sections, and route the result through the right approvals — with humans firmly in the loop where it matters.
What Drag AI Builds
Invoice processing pipelines with line-level extraction and validation
Contract review support: clause extraction, risk flags, and structured summaries
Form digitisation and structured extraction from inconsistent templates
PDF and report summarisation with citation back to the source
Compliance-style review workflows with reviewer queues and audit logs
Outputs in clean JSON, CSV, or direct writes into your existing systems
Common Use Cases
Invoice processing
Extract supplier, line items, totals, dates; route to approvers; write to your accounting system.
Contract review support
Surface obligations, dates, parties, and risk-flagged clauses for legal/ops review.
Form & application extraction
Turn paper / scanned forms into structured records with validation rules.
Lease abstraction
Extract rent, term, escalation, options, and key clauses across portfolios.
Claims intake
Structure claims documents, attachments, and notes into reviewable cases.
Report summarisation
Convert long reports into structured exec summaries with traceable evidence.
Tools & Integrations
- •ERPs and accounting: NetSuite, QuickBooks, Xero, Sage
- •CRMs and ticketing: HubSpot, Salesforce, Zendesk, Freshdesk
- •Storage: SharePoint, Google Drive, Dropbox, S3, on-prem file shares
- •Databases: Postgres, MySQL, SQL Server, Snowflake, BigQuery
- •Workflow tools: Slack, Microsoft Teams, email triggers, internal portals
- •Custom APIs / webhooks for downstream systems
Security & Human Approvals
- •Document-level access controls and audit logging
- •Reviewer queues for low-confidence extractions instead of blind autofill
- •PII-aware handling and redaction options
- •Human approval steps before any irreversible action (payment, status change)
- •Versioning of extracted records with original document references
- •Per-tenant data isolation when serving multiple business units
How We Build It
01
Document workflow audit
Catalogue current documents, volumes, exceptions, and downstream systems.
02
Schema design
Define fields, validation rules, and reviewer queues with the team that does the work today.
03
Prototype on real samples
Run extraction on representative documents and measure accuracy honestly.
04
Reviewer experience
Design the reviewer UI so exceptions are fast, not painful.
05
Production rollout
Connect to downstream systems with retries, logging, and SLA monitoring.
06
Continuous tuning
Use reviewer corrections as labelled data to keep accuracy improving over time.
Frequently Asked Questions
How accurate is Document AI?
It depends on document quality and the field type. We measure precision, recall, and reviewer effort against your real documents — and design reviewer queues for anything below your accuracy threshold.
Can it handle scanned and low-quality PDFs?
Yes. We combine modern OCR with layout-aware models and validation rules. Truly bad scans still need human review — but the system flags them rather than guessing.
Will it replace our team?
No. It removes the most repetitive parts so your team can focus on judgement, exceptions, and customer-facing work.
Where does the data live?
Wherever you need it to. We design for data residency, tenant isolation, and your security review process.
How do we measure ROI?
Cycle time, cost per document, reviewer effort, and exception rate — measured before and after, against real volumes.
How do we get started?
Book a free AI strategy call and share a few sample documents. We will tell you honestly what is achievable and where the limits are.
Explore Related