Turn enterprise AI ambition into realized enterprise performance
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Turn enterprise AI ambition into realized enterprise performance
Book a discovery call
AI experimentation is accelerating. Enterprise value is not.
AI experimentation is accelerating. Enterprise value is not.
Why AI pilots fail to become enterprise platforms
Why AI pilots fail to become enterprise platforms
1
Pilot proliferation
Hundreds of experiments. Few production-grade outcomes.
1
Pilot proliferation
Hundreds of experiments. Few production-grade outcomes.
2
Data is not AI-ready
Fragmented, weakly governed data limits model quality and business trust.
2
Data is not AI-ready
Fragmented, weakly governed data limits model quality and business trust.
3
Missing enterprise context
Without an enterprise context, AI is not reliable.
3
Missing enterprise context
Without an enterprise context, AI is not reliable.
4
Governance and security after deployment
Trust must be built in, not bolted on.
4
Governance and security after deployment
Trust must be built in, not bolted on.
5
Duplicated engineering
Teams repeatedly rebuild data pipelines, prompts, agents, and integrations because the enterprise lacks shared infrastructure, standards, and expertise.
5
Duplicated engineering
Teams repeatedly rebuild data pipelines, prompts, agents, and integrations because the enterprise lacks shared infrastructure, standards, and expertise.
6
Rising AI costs and slow time to value
Without reusable building blocks and shared operations, engineering and compute costs grow faster than the business value AI creates.
6
Rising AI costs and slow time to value
Without reusable building blocks and shared operations, engineering and compute costs grow faster than the business value AI creates.
Fractal AI Foundation
Context-driven secure foundation
Scale AI with AI Foundations, Fractal’s modular enterprise backbone connecting data, business context, models, agents, governance, and operations within your existing cloud, data, and enterprise technology ecosystem.
What it enables
Enterprise ready architecture
Agentic foundation
Data and Enterprise context
Trusted governed ecosystem
Secure AI adoption
What it enables
Platform engineering and operations
Accelerated value realization
Six-layer foundation architecture
Six-layer foundation architecture
Data foundation
Data foundation
Governed multimodal data products: structured, unstructured
Enterprise context
Enterprise context
Semantics, ontologies, knowledge graphs, policies and decision context.
Models and intelligence
Models and intelligence
Multi-model access, routing, contextualization, lifecycle management and guardrails.
AI reasoning and agents
AI reasoning and agents
Agent runtime, orchestration, tools, memory, retrieval, and human-in-the-loop workflows.
Experience and action
Experience and action
Agentic processes: Human to agents, Agent to agent.
Control and operations
Control and operations
Security, governance, evaluation, observability, reliability, and FinOps.
Enterprise-scale AI capabilities
Enterprise-scale AI capabilities
1
Data Foundation
AI-ready data with quality and freshness monitoring
AI-ready data with quality and freshness monitoring
Semantic and ontology-driven reasoning across enterprise data
Unified metadata management and lineage tracking
2
Intelligence and action
Intelligence layer to define the right model for right task with optimal cost
Multi-agent orchestration with autonomous decision-making
Decision intelligence and agentic process automation
3
Trust and operations
Policy-as-code governance framework for regulatory compliance
Policy-as-code governance framework for regulatory compliance
Full audit trails and provenance for every AI decision
Real-time observability, monitoring, and cost optimization
Modular by design.
Enterprise-grade by standard.
Modular by design.
Enterprise-grade by standard.
Platform building blocks
Platform building blocks
Modular, composable platform components that work together or independently
Modular, composable platform components that work together or independently
AI development process lifecycle
AI development process lifecycle
Standardized, repeatable process from concept to production
Standardized, repeatable process from concept to production
Define business problem, success metrics, and AI use case scope.
How much measurable value are we extracting from the
AI and data investments we already own?
How much measurable value are we extracting from the
AI and data investments we already own?
1
Agentifying data integration and management
Agentifying data integration and management
Agentified engineering processes from requirements to trusted, production-ready data products with a reusable governed composition of agents from a standardized agent catalogue. Apply AI agents across the lifecycle, from data product design and semantic modeling to migration, code generation, testing, data quality, and deployment.
1
2
Agent quality assurance
Agent quality assurance
Create an agent quality harness for testing enterprise suite of applications across digital, data and AI estates . Keep human reviewers in control while delivering faster test coverage, earlier feedback, and near-real-time reporting targeting 70% reduction in the current spent towards quality.
2
3
AI governance
AI governance
Move faster with AI controls embedded from use-case design through production monitoring. Connect automated intake, cataloging, risk assessment, regulatory mapping, human approvals, and post-deployment observability in one traceable governance workflow.
3
4
AI for BI
AI for BI
Conversational assistance for business personas to business insights in natural language through Microsoft Teams or existing BI tools. Built a semantic and a multi-agent GenAI framework to interpret each request, coordinates specialized domain agents, and validates data, code, and outputs before responding to users. Tested across markets, multiple datasets and use cases, the solution achieved high accuracy across the evaluated prompts, with a quick response time..
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5
From fragmented data to a reusable global growth engine
From fragmented data to a reusable global growth engine
Turn fragmented data into a scalable foundation for commercial growth with harmonized, governed, reusable data products that work across markets and use cases. Enabled 4 markets with 70+ reusable data products and 100+ harmonized data assets, cutting data readiness time from weeks to days.
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6
Migration and modernization
Migration and modernization
AI agents reverse-engineer legacy estates, map applications and dependencies, define the target architecture, sequence migration waves, convert and refactor code, automate testing, and support deployment there by accelerating the move from monolithic systems to scalable, cloud-ready microservices. Enabled SAS to Databrick, Java application modernization to microservices based architecture, Synapse to databricks and more .
6
Hyperscaler native/multi-cloud composable solutions. Translating AI capabilities into enterprise outcomes.
Hyperscaler native/multi-cloud composable solutions. Translating AI capabilities into enterprise outcomes.
Compose, govern and operate reusable agent workflows across engineering and business processes reducing repetitive development effort and accelerating productionization.
Compose, govern and operate reusable agent workflows across engineering and business processes reducing repetitive development effort and accelerating productionization.
Product components
A plug-and-play capability for language model contextualization & life-cycle management to democratize the adoption of domain, function and task-specific intelligence at enterprise-scale
A plug-and-play capability for language model contextualization & life-cycle management to democratize the adoption of domain, function and task-specific intelligence at enterprise-scale
Product components
Your industrialized AI journey
From isolated pilots to production-grade, reusable AI platforms
From isolated pilots to production-grade, reusable AI platforms
Assess maturity and AI readiness
Define AI engineering blueprint aligned to business goals
Build data and context backbone for enterprise scale
Engineer AI platform capabilities with governance embedded
Embed assurance, compliance, and security controls
Launch decision apps and AI agents into production
Operate with observability, SRE, and FinOps discipline
Assess maturity and AI readiness
Define AI engineering blueprint aligned to business goals
Build data and context backbone for enterprise scale
Engineer AI platform capabilities with governance embedded
Embed assurance, compliance, and security controls
Launch decision apps and AI agents into production
Operate with observability, SRE, and FinOps discipline
Your next AI use case should strengthen the foundation does not create another silo
Your next AI use case should strengthen the foundation does not create another silo
Your next AI use case should strengthen the foundation does not create another silo
Turn existing data, cloud and AI investments into a secure, governed and reusable engine for enterprise intelligence.
Turn existing data, cloud and AI investments into a secure, governed and reusable engine for enterprise intelligence.
Fractal AI Foundation
Fractal AI Foundation
Context-driven. Secure. Governed. Reusable data, intelligence, and action.
Context-driven. Secure. Governed. Reusable data, intelligence, and action.
Scale AI with AI Foundations, Fractal’s modular enterprise backbone connecting data, business context, models, agents, governance, and operations within your existing cloud, data, and enterprise technology ecosystem.
What it enables
Enterprise ready architecture
Agentic foundation
Data & Enterprise context
Trusted governed ecosystem
Secure AI adoption
What it enables
Platform Engineering & operations
Accelerated value realization
