ModelXpress — AI-ready model packs
Delivers:
- Risk ↓ 5–10% (credit / behaviour / collections)
- Manual review ↓ 20–40% (fraud / claims)
- Forecast error ↓ 10%+, promo ROI ↑ 2–5%
How it works:
- Base configuration: Map your schema to standard features, train / calibrate with your data.
- Feature expansion (optional): Client-specific signals for extra lift
- xAI built-in: global & per-prediction reasons; bias/drift checks
- Extra Features – Data Sandbox, Feature Store, Periodic Optimization and Model Monitoring, Deployment & Integration.
Where it fits:
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DataStory AI — From raw data to decisions
Delivers:
- Minutes-to-insight with KPI trees & role-based dashboards
- 5–10× faster ad-hoc via governed Text-to-SQL
- Customized narrative – briefs with “what changed?” & drivers
- 30–50% BI backlog reduction
How it works:
- Data Engineering Backbone: ingest → transform → semantic layer (secure, reliable)
- Dashboards & KPI Trees: drill, filter, period compare; CXO/Ops/Finance views
- Text-to-SQL: schema-aware, reviewable queries with guardrails (RLS, PII masking, sandbox)
- Narrative Panel: auto executive summaries (EN + Indian languages) with root-cause hints
Where it fits:
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DocZen AI — From documents to clean data
Delivers:
- STP ↑ 20–30 pp; TAT ↓ 45%; exception-only operations
- Audit-ready extractions with confidence & lineage
How it works:
- Document understanding: layout-aware OCR & auto-classification
- Extraction & validation: rules + ML/LLM + HITL for low-confidence cases
- Workflow: API/webhook routing to CRM/LOS/DMS; dashboards for accuracy/STP/TAT
Where it fits:
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DecisionForge AI — Agents that execute
Delivers:
- Cure@30 ↑ / DPD ↓
- Insurance Risk Bucketing
- Optimal Credit Cutoff / Line Assignments
How it works:
- Playbooks & policies: when/what to act on; throttle, fairness, simulations
- Agents & channels: system actions via APIs + Nudges (WhatsApp/SMS/Email/IVR)
- Guardrails & monitoring: approvals, replay/rollback, audit logs, live KPIs
Where it fits:
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AI Strategy & Enablement
AI adoption is enabled for small and medium businesses through opportunity assessment, a defined roadmap, appropriate tools, and strong governance. A structured approach is followed to ensure alignment with business goals, driving innovation and measurable impact.
AI Opportunity Assessment
Identify high-impact use cases aligned with your business goals through a structured evaluation of data assets, operations, and readiness.
AI Toolchain Selection
Recommend the optimal stack of tools, platforms, and technologies tailored to your use cases, scalability needs, and team capabilities.
AI Roadmap Design
Develop a phased and actionable AI adoption roadmap that aligns strategic priorities with technical feasibility and change management.
Data & Model Governance
Establish robust frameworks to ensure transparency, compliance, and ethical use of data and AI models across the enterprise.
