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 Process Integration
AI does not replace processes, it enhances them. We identify where AI genuinely improves your workflows, classification, extraction, routing, summarisation, and integrate it cleanly into your existing systems without disruption.
// Key benefits
What makes this service valuable
High-impact process identification
Not every process benefits from AI. We analyse your workflows to identify where AI adds measurable value, typically document processing, classification, and decision support, before committing to implementation.
Existing system integration
AI capabilities are integrated into your existing tools and workflows, not standalone add-ons. They appear where your team already works.
Human-in-the-loop design
AI-enhanced processes are designed with appropriate human oversight, confidence thresholds, exception queues, and audit trails that maintain accountability.
// Details
AI where it earns its place
AI process integration starts with identifying the right use cases, not building impressive demos. The highest-value applications are typically document understanding, intelligent classification, entity extraction, and decision support.
We integrate via OpenAI, Anthropic, or open-source model APIs, build confidence scoring into every AI decision, and design exception handling for cases where AI confidence is too low.
// What this includes
- Use case analysis and ROI assessment
- AI model selection and API integration
- Document understanding and extraction
- Intelligent classification and routing
- Confidence scoring and exception handling
- Human-in-the-loop workflow design
- Performance monitoring and model evaluation
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our ai process integration service.
- 01AI use case assessment and ROI analysis
- 02Integrated AI capability in your existing workflow
- 03Confidence scoring and exception queue
- 04Human oversight interface
- 05Performance monitoring dashboard
- 06Model evaluation and accuracy reporting
// In practice
How ai process integration engagements run
We map one high-volume workflow end-to-end — often email-to-CRM or ticket classification — and embed LLM steps only where regex fails. Integrations use your existing APIs (Salesforce, HubSpot, Jira) with idempotency keys and dead-letter queues on failure. Prompt versions are pinned in config; rollback does not require redeploying the whole service.
// 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
DIPP83124
Founded
November 2020
Office
Residency Rd, Bengaluru, India
// FAQ
Common questions about ai process integration
How do you measure ROI on AI process integration?+
We establish baseline metrics before integration, processing time, error rate, headcount, then measure improvement post-integration. Common metrics include: hours saved per week, error rate reduction, and processing speed improvement.
Do you fine-tune models or use general-purpose LLMs?+
For most business process integrations, prompt engineering on general-purpose models (GPT-4, Claude) is sufficient and faster to iterate. Fine-tuning is recommended when you have large volumes of domain-specific training data and need cost optimisation at scale.
Ready to get started with ai process integration?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.