Corporate law boutique
- Challenge
- 200-page dataroom first-pass review took 11 hours per matter.
- Simplileap solution
- Private RAG with citation anchors and human sign-off gates.
- Outcome
- Review time 11h → 3.5h; zero unverified citations in pilot.
// Automate
AI Data Processing Workflows
Unstructured data is expensive to process manually. We build AI-powered data processing workflows that extract, classify, transform, and route data at scale, from documents, emails, images, and audio.
// Key benefits
What makes this service valuable
Document intelligence
Invoice extraction, contract analysis, form processing, and document classification using Azure Document Intelligence, AWS Textract, or GPT-4 Vision for complex layouts.
Batch and stream processing
High-volume batch document processing with Celery/Kafka, or real-time stream processing with AWS Lambda triggers, matching throughput to your data volume and latency requirements.
Data quality validation
AI extraction outputs are validated against business rules, confidence thresholds, and cross-validation checks before writing to your data store.
// Details
Processing data that used to require humans
Document processing, data classification, and entity extraction have traditionally required human review teams. AI now handles these tasks accurately enough for automated processing, with human review reserved for low-confidence edge cases.
We build processing pipelines with appropriate AI services for each data type: Document Intelligence for structured documents, LLMs for unstructured text, computer vision for images.
// What this includes
- Document extraction (invoices, contracts, forms)
- Named entity recognition and extraction
- Data classification and categorisation
- Image analysis and description
- Audio transcription and analysis
- Validation and quality scoring
- Integration with downstream systems
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our ai data processing workflows service.
- 01AI data processing pipeline
- 02Extraction accuracy baseline and monitoring
- 03Validation and exception handling rules
- 04Human review interface for exceptions
- 05Data quality dashboard
- 06Integration to downstream data store
// In practice
How ai data processing workflows engagements run
We typically anchor the first sprint on document intelligence. Unstructured data is expensive to process manually. We build AI-powered data processing workflows that extract, classify, transform, and route data at scale, from documents, emails, images, and audio. On Residency Road engagements, discovery maps dependencies and success metrics before sprint one. Every automation ships with exception queues, audit logs, and a baseline metric so ROI is measurable within 30 days.
// Stack & frameworks
Stack we use for this
AI & LLM
- OpenAI / Anthropic APIs
- LangChain pipelines
- RAG architectures
- Confidence thresholds
Integration
- Salesforce / HubSpot
- Zapier / Make
- Custom webhooks
- ERP connectors
Governance
- LangSmith observability
- PII handling
- Audit logs
- Rollback procedures
// Delivery
Simplileap execution framework
01
Architecture mapping
Dependencies, API contracts, compliance constraints, and performance budgets documented before sprint one.
02
Secure sprints
Two-week increments with GitHub access, demo recordings, and QA checkpoints, client visibility at every stage.
03
QA & handover
Automated tests on critical paths, security review, runbooks, and knowledge transfer to your team.
// Proof
Real deployments from Bangalore
// Engagement models
How teams engage us
Currency
| Package | Ideal for | Investment | Includes |
|---|---|---|---|
| Workflow automation | Ops teams | ₹3L – ₹10L |
|
| AI / LLM integration | Product teams | ₹4L – ₹12L |
|
| RPA implementation | Back-office | Scoped per process |
|
// Company and service positioning
Company and Service positioning is reviewed for production delivery standards by Harsha Parthasarathy (Co-Founder, Strategy & Operations 24+ years IT veteran, IBM, Global Delivery, Program Management) and Keshav Sharma (Co-Founder, Engineering and Lead Architect, Full-stack engineering, product delivery and technical standards).
CIN
AAU-8582
Startup India
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Common questions about ai data processing workflows
What accuracy rates can I expect from document extraction?+
For well-structured documents (invoices, purchase orders), modern AI extraction achieves 95–99% field-level accuracy. For unstructured or variable documents, accuracy ranges from 80–95% depending on variation. We establish accuracy baselines and monitor drift.
Can you process documents in multiple languages?+
Yes, modern document AI models support 50+ languages. Accuracy varies by language; Latin-script languages generally perform better than non-Latin scripts.
Ready to get started with ai data processing workflows?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.