Solution / Business use case
Documents in. Validated data out. Exceptions to people.
Classify, extract and validate document data, and route exceptions to the right people.
Document & Back-office AI
/ 01Challenge
What gets in the way today
Back-office teams re-key data from invoices, applications, claims and contracts. The work is slow, error-prone and hard to scale at peak periods.
Earlier template-based capture tools broke whenever a supplier changed a layout. Modern models are more flexible but still need validation and review.
/ 02AI workflow
How the work flows with AI in it
Steps marked human are decision points owned by your people.
- 01
Intake
Documents arrive from mailboxes, portals, scanners or storage.
- 02
Classify
Files are split and classified by type.
- 03
Extract
Fields are extracted with confidence scores and source locations.
- 04
Validate
Rules check totals, formats, reference data and cross-document consistency.
- 05human
Review exceptions
Low-confidence or failing items go to a reviewer with evidence highlighted.
- 06
Post
Approved data is written to ERP, case management or the warehouse.
/ 03Example architecture
Signals in, governed outcomes out
A typical reference design. Your version depends on your systems, data and risk appetite.
- Email and upload portals
- Scanned and digital documents
- Reference and master data
- Classification
- Layout-aware extraction
- Rules engine
- Routing
- Confidence thresholds
- Validation rules
- Reviewer approval
- Retention and deletion jobs
- Structured records
- Exception queue
- Accuracy and throughput reports
/ 04Benefits
What changes, and how you would measure it
Less manual entry
Clean documents flow through without re-keying.
Fewer downstream errors
Validation catches problems before they reach financial or customer systems.
Elastic capacity
Peaks are absorbed without proportional overtime.
Measures we would agree with you
- Straight-through processing rate
- Field-level accuracy
- Exception review time
- Cycle time from receipt to posting
We do not quote generic improvement figures. Targets are set from your measured baseline.
Limitations to plan for
- Accuracy must be measured on your own documents; poor scans and handwriting reduce it.
- Validation rules need business ownership and maintenance as processes change.
/ 05Product and service match
What we would use
Product concepts
Services
- Service / buildCustom AI SaaS & Multimodal AppsAI-native products and internal applications that work with text, documents, images and audio.
- Service / integrateAI Workflow & CRM/ERP IntegrationBring AI into the CRM, ERP and service platforms where work already happens.
- Service / adviseUse-case Discovery & ROITurn a long list of AI ideas into a few business cases you can defend, with baselines and success measures.
/ 07FAQ
Questions buyers ask
Which document types work best?
High-volume types with consistent purpose, such as invoices, purchase orders, onboarding forms and claims. Rare or highly variable documents may stay manual.
Can we keep documents in our own environment?
Yes. Processing can run entirely in your cloud account with models hosted under your control or via regional APIs.
Explore AI for document & back-office ai
Bring a sample of the work: tickets, documents, requests. We will show you what is realistic and how we would measure it.