Contract analysis · 50k contracts/month
Structured clause extraction with field-level audit. Each clause has an extracted value, a confidence score and the original passage for human validation when needed.
Processes that think.
When what your team does involves judgment, classification or interpretation — generative AI changes the game.
What we do
Automation that resolves, not just executes.
We combine LLM + tools + feedback loops. Nothing reaches production without clear metrics.
LLM-centered workflows
Classification, extraction and decisions in flows with clear human-fallback rules.
Document processing
Contracts, invoices, records, proposals. Semantic structuring with field-level audit.
RPA on steroids
Where traditional RPA broke on variations, generative AI closes the loop with reasoning.
Operational metrics
Latency, rework rate, % auto-resolution, cost per document. Visible in real time.
By the numbers
What volume and quality say at quarter end.
0×throughput in cognitive backoffice
−0%average time per document processed
0%auto-resolution with field-level audit
R$ 0average cost per document
Anatomy
Cognitive workflow, end to end.
Automation that doesn't break on semantic variation — generative AI handles different documents, rephrased sentences, unforeseen exceptions, while staying auditable.
- F1
Capture
Multi-source ingestion: email, upload, API, scan OCR.
Output Input pipeline with format validation.
- F2
Structure
LLM extraction + declared schema per document type.
Output Normalized JSON with per-field confidence.
- F3
Decision
Rules + model decide auto-approval vs. human review.
Output Confidence threshold calibrated per type.
- F4
Audit
Every decision has a trail: input, prompt, model, confidence, output. Digitally signed.
Output Audit-ready logs (internal/external).
Under the hood
Tools that ship workflow without breaking.
Document AI
- Claude VisionOCR + semantic structuring.
- Azure Form RecognizerFixed-layout documents.
- AWS TextractBulk OCR.
- PaddleOCRSelf-hosted for sensitive data.
Orchestration
- TemporalDurable workflows.
- n8n / ZapierLightweight integrations.
- Apache AirflowBatch pipelines.
ERP/CRM integration
- SAP, Oracle, TotvsNative API where available.
- SalesforceApex + REST.
- RPA bridgeFor legacy systems without API.
In production
Automation that thinks.
Healthcare invoice review
Automatic cross-checking between invoice, EHR and contractual table. Flags technical denials with rationale, letting the human auditor focus only on edge cases.
Commercial proposal structuring
Heterogeneous vendor PDFs converted into normalized structured tables. Procurement team stopped copying and pasting and started comparing.
Vs. alternatives
When generative AI beats traditional RPA.
LA AI · generativeCognitive workflow
Traditional RPAUiPath, Automation Anywhere
Off-the-shelf SaaSGeneric tool
Handles format/language variation
Learns from human corrections
Per-field audit with confidence
Deployment in 4-8 weeks
Custom for regulated process
Predictable cost per document
Frequently asked
About intelligent automation.
RPA vs. generative-AI automation?
Traditional RPA runs deterministic scripts — when input varies, it breaks. Generative AI handles semantic variation: different documents of the same type, rephrased sentences, unforeseen exceptions. RPA + AI is the common pattern today.
How do we measure quality?
On every deployment we define a gold-set (samples reviewed by domain humans) and run continuous evaluation. Above the agreed threshold, output goes automatic; below, it goes to human review.
Does it integrate with our ERP/CRM?
Yes. Native APIs (SAP, Oracle, Salesforce, etc.) or webhook for custom systems. Legacy without API: RPA bridge.
What if the model is wrong?
We define confidence gates per error type. Expensive errors (e.g., approving an invoice) always pass through a human. Cheap errors (e.g., ticket urgency classification) can go automatic with sample-based audit.