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 Decision Systems
Manual decision-making is the bottleneck in many business processes, loan approvals, lead scoring, content moderation, fraud detection. We build AI decision systems that automate high-volume decisions accurately and auditably.
// Key benefits
What makes this service valuable
ML model integration
Classification models, regression models, and recommendation systems, trained on your historical decision data and integrated into your decision workflow.
Explainable decisions
Automated decisions require explainability, especially in regulated industries. We build decision systems with feature importance tracking and human-readable explanations.
Rules + ML hybrid systems
Pure ML can be a black box; pure rules are brittle. Hybrid systems use rules for clear cases and ML for ambiguous ones, combining reliability and adaptability.
// Details
Automating decisions without losing control
AI decision systems work best for high-volume, repetitive decisions where the decision criteria can be learned from historical data. The right architecture combines ML prediction with business rules, confidence thresholds, and human review queues for edge cases.
We build decision systems with full audit trails, every automated decision is logged with the inputs, model version, confidence score, and outcome, enabling retrospective analysis and model improvement.
// What this includes
- Historical decision data analysis
- ML model training and evaluation
- Rules engine integration
- Confidence threshold configuration
- Human review queue for low-confidence decisions
- Decision audit trail and logging
- Model performance monitoring
// Deliverables
What you receive
Every engagement produces clear, documented deliverables. Here is exactly what is included in our ai decision systems service.
- 01Decision system architecture and design
- 02Trained and evaluated ML model
- 03Decision API with confidence scoring
- 04Rules engine integration
- 05Human review interface
- 06Decision audit dashboard
// In practice
How ai decision systems engagements run
Decision engines pair rules with ML scores — e.g. fraud flags from Isolation Forest plus hard blocks on sanctioned geographies — with every outcome stored for audit. We expose override queues in your ops console and track precision/recall weekly against labelled samples. Shadow mode runs in parallel with human decisions for two weeks before auto-approval thresholds go live.
// 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
B2B infrastructure software vendor
- Challenge
- Editorial team needed governed AI without off-brand drafts.
- Simplileap solution
- Constrained prompts, block patterns, and review-before-publish workflow.
- Outcome
- Draft cycle 4.2 days → 2.1 days.
// 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 decision systems
What historical data do I need for AI decision systems?+
The more the better, typically 10,000+ past decisions with known outcomes. Less data requires more conservative automation (higher human review rate). We assess your data volume and quality before designing the system.
How do you handle bias in AI decision systems?+
We test for demographic and feature bias in training data, use fairness metrics during model evaluation, and implement monitoring to detect bias drift in production. For regulated use cases (credit, hiring), we follow applicable fairness guidelines.
Ready to get started with ai decision systems?
Share your requirements with our team. We respond within one business day with a clear plan from discovery to delivery.