What’s driving AI adoption?
Why enterprises need AI-powered solutions
1
AI adoption struggles to scale beyond pilots
2
Decision-making needs to be faster, smarter, and automated
3
Data silos limit real-time customer and operational insights
4
Businesses must balance growth, efficiency, and compliance
1
AI-driven decisioning: AIDE
Automating enterprise decision-making with real-time intelligence
Real-time decision orchestration across business functions
Machine learning-powered adaptive decision-making
Cloud-native architecture with close to complete uptime
Case Study: Eliminating cognitive dissonance, unlocking significant CLV
Challenge: Low conversion, high drop-offs
Solution: AI-driven digital journey optimization
Result:
Bulk daily digital visits analyzed
Significant incremental CLV unlocked
Faster UX improvement cycles
4
AI-Powered simulation: Trial Run
De-risking business decisions through AI-driven scenario modeling
Load testing and performance monitoring
Digital twin simulations for real-world test cases
Automated resource allocation and cost optimization
Case Study: Optimizing retail store sales with data-driven remodeling
Challenge: Uncertain remodeling impact on sales
Solution: AI-driven experimentation
Result:
Substantial sales lift in remodeled stores
Break-even in short period for large stores
6
Fractal LLM Studio
Plug-and-play platform for fast, scalable deployment of custom language models
Auto LLM tailored for domain and task adoption
Life-cycle management and deployment support
Improves response quality and consistency
Cuts custom LM development from months to days
Simplifies LM ops with built-in governance AND cloud pipelines
7
Fractal SDLC
Agentic AI platform for scalable app development with governance and integration
Agent composition, orchestration, and multi-LLM support
Prompt management, versioning, and catalog
RAG integration with unified metadata
Integrated operations and governance
Shortens onboarding time for a Fortune Alco-Bev firm
Decreases documentation effort for a med-tech leader
8
Fractal Data Foundation
Intelligent multi-modal data platform
Eliminates silos for faster, cleaner insights
Boosts data workflow efficiency by up to 70%
Elevates data accuracy by 85% via unified processing
Unifies data quality, observability, and harmonization
Simplifies data ops with an integrated GenAI platform
Faster, trusted data access for an energy major
Quicker delivery with less manual effort for a CPG firm
Actionable insights from complex data for a transport firm
9
Fractal Data Science
Cloud agnostic, no vendor lock-in
Agile agentic framework
Highly scalable model
7
Fractal Document Processing
Cuts effort and cost, boosting accuracy
Enables faster decisions with seamless data integration
8
Fractal Enterprise Store
Centralized platform for easy, trusted data sharing across teams
Semantic search for easy data discovery
Smart data product recommendations
End-to-end workflow management
Data profiling with quality scores
Usage tracking and analysis
Secure access control
Cuts time-to-data significantly, enabling near-instant access
Doubles to quintuples self-service users, reducing IT dependency
Enhances compliance with better metadata, lineage, and access controls
9
Fractal Migration
Agentic AI workbench streamlining secure, large-scale migrations
Agentic AI for code analysis, conversion, and duplication
Multiple conversion patterns with automated unit testing
Cloud-agnostic, scalable migration workbench
Speeds up assessment and migration
Productivity boost for APAC telco on Azure
Optimizes SAS-to-Python migration for a finance firm
Phase 1
AI strategy and readiness
What we do
Assess data readiness and compliance requirements
Identify business challenges and AI opportunities
Define success metrics and ROI modeling
Build a custom AI roadmap
Outcome: AI solutions tailored to business needs, ensuring a clear path to impact
Phase 2
AI solution design and development
What we do
Design intuitive dashboards and user experiences
Develop AI models and automation workflows
Ensure scalability, security, and governance
Integrate AI into enterprise data systems
Outcome: AI-powered solutions built for enterprise scalability and efficiency
Phase 3
Deployment and optimization
What we do
Deploy AI solutions across multiple business functions
Implement real-time monitoring and model retraining
Provide ongoing AI governance and enhancements
Ensure compliance with industry regulations
Outcome: A future-ready AI ecosystem with continuous learning and impact measurement
Thought leadership
