Repeated document entry
Extract fields and route exceptions for review.
SENXOS / AI ENGINEERING
AI understands.
Results stand up to scrutiny.
Connect documents, knowledge and workflows with source evidence, business rules and human review.
Start with work worth improving.
Answers are hard to find
Search accessible policies with source references.
Work spans many tools
Prepare actions with approval for important steps.
Inconsistent AI results
Test representative data and track errors.
From understanding to useful work.
Document intelligence
Organize invoices, contracts and business documents with source evidence, required fields and amount checks for review.
Enterprise knowledge retrieval
Search permitted policies and manuals, cite sources and indicate when evidence is insufficient.
Controlled workflows & agents
AI prepares intent and parameters; rules, permissions and approval govern execution.
Evaluation & improvement
Test quality, exceptions, latency and cost on representative data and compare versions.
AI-generated application concepts, not customer or product screenshots.
From document to usable information.
Predefined examples and local rule checks. Fields are prepared in advance. No OCR, model inference, upload, storage or payment approval.
Source example
- Document reference
- SX-SAMPLE
- Line one
- 1250.00
- Line two
- 750.00
- Subtotal
- 2000.00
- Tax
- 120.00
- Total
- 2120.00
Prepared fields
{
"reference": "SX-SAMPLE",
"lines": [
"1250.00",
"750.00"
],
"subtotal": "2000.00",
"tax": "120.00",
"total": "2120.00"
}Rule checks
Choose an example and run checks.
Models interpret. Code verifies.
Data intake
- Processing
- Access permitted documents and data.
- Stop or review conditions
- Stop without authorization or known sources.
Define boundaries before extending capability.
Data use
Confirm what may reach models and what must remain private or masked.
Access
Define document, data and tool scope.
Action approval
Distinguish drafts from authorized changes.
Records & evaluation
Agree sources, versions, records and review methods.
Bring intelligence into everyday operations.
AI interprets and suggests. Business systems govern data, permissions, state and execution. Evaluate cloud APIs, private or hybrid environments against requirements.
Questions, answered
Will data reach an external AI model?
It depends on the model, deployment and data. Agree what may be shared, masked or kept internal, then check the provider’s actual processing arrangements.
How do you handle incorrect AI answers?
Retain sources, use representative tests and rules, and route missing evidence, exceptions and important actions for human confirmation.
Must we replace existing systems?
Not necessarily. Review the software, data and interfaces first. Integration or targeted improvements may be enough.
Why validate before development?
Prototypes and representative data help agree assumptions, usable results and scope before committing to the next stage.
Choose one task worth improving.
Tell us the data source, current process and what makes a result usable. Assess evidence and validation before defining scope.