Sample
Collect representative clean, messy and adversarial examples without retaining unnecessary personal data.
Service
AI Document & Data Workflow
Turn unstructured emails, PDFs, images and notes into validated operational records.
Best fit
Document-heavy operations where people repeatedly interpret text and then enter predictable fields into a system.
Buyer outcome
A schema-first extraction or classification pipeline with deterministic validation, confidence routing and a review queue.
Deliverables
What leaves the engagement.
Concrete artifacts, a clear operating boundary and enough documentation for the team to own the result.
01
Document/input taxonomy
02
Pre-processing and OCR path
03
Structured extraction schema
04
Model and prompt evaluation set
05
Business-rule validation
06
Confidence and abstention policy
07
Human exception queue
08
Destination-system integration and audit trail
How the work moves
Four deliberate stages.
Schema
Define required, optional and forbidden fields before selecting a model.
Evaluate
Measure field accuracy, schema validity, misses, invented values, latency and cost.
Control
Route uncertain, inconsistent or high-impact records to a human before any consequential write.
Reference architecture
AI proposes. The workflow validates.
systemInput
systemPre-process
aiAI extraction
logicSchema validation
logicBusiness rules
humanHuman review
systemDestination write
System / APIDeterministic logicAI stepHuman decision
Consequence boundary
Uncertain, inconsistent or high-impact records cannot progress until the defined reviewer resolves them.
Engagement boundaries
Clarity before commitment.
- A confident model response is not the same as a correct record.
- Arithmetic, allowed values, known vendors and duplicates are checked deterministically.
- Payment approval and similar consequential decisions remain outside AI authority.
“Use deterministic automation wherever possible. Insert AI only where language, extraction or judgment makes it earn its cost.”
Bahman’s engineering principleNext step
Bring one process, not an AI shopping list.
Describe what starts it, how often it runs, which systems it touches and where it currently breaks.