Rackspace Technology
An Overview
Managed Public Cloud
TODAY
From edge to core to cloud, Rackspace is the definitive force in hybrid cloud transformation, orchestrating the full-stack lifecycle across AWS, Azure, Google Cloud, and secure private environments. We accelerate business outcomes through AI-infused delivery and outcome-based FinOps, ensuring your entire architecture is lean, secure, and hyper-optimized.
We extend your platform teams via an elite ecosystem—integrating Palantir, Uniphore, Rubrik, and Tessell—to move enterprise AI and data from experimentation to global production at scale.
Corporate Overview & Updates
Rackspace Technology is a $3B industry recognized leading end-to-end, hybrid/sovereign, Multi-cloud and AI solution provider.
The company is controlled by investment funds affiliated with Apollo Global Management.
We are one of the largest AWS and Microsoft partners driving 1.6B+ in ARR, over 2,000 Multi-cloud specialists across Cloud, Data and AI.
Leader in Private cloud with 35 DC’s across the globe with readiness to support AI and Sovereign workloads.
Recognized as leader in multiple areas by ISG in 2025 – Microsoft AI & Cloud Ecosystem, Google Cloud Partner Ecosystem, AWS Ecosystem, Cyber Security & Solutions.
Executive leadership team includes industry stalwarts: Gajen Kandiah, CEO (previously at Hitachi & Cognizant) and DK Sinha, President (previously at Cognizant).
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Americas
EMEA
APJ
3,700+
employees
1,000+ employees
1,200+ employees
5,900+ Employees globally | 20+ k Customers | 120 Countries | 168 PB Storage managed | 30+ Datacenters |
OUR DIFFERENTIATED EXPERTISE
OUR GLOBAL REACH
12M+
Hours of development
$1B+
Invested
8
Years of unique IP
Intelligent Infrastructure
Self-healing deployed to customers
AIOps
AI-driven management for multi-tenants
Intent-Driven Automation
Highly efficient customer support
2,600+ Certified technical �experts | 9,500+ Total technical certifications | Created OpenStack�jointly with NASA |
Managed Hosting
products, services and support
Hosting
Product-oriented portfolio of offerings; very technical
OpenStack
Multicloud Infra Solution
NextGen Multicloud Solutions Provider
Infrastructure, apps, data, security & AI
OUR JOURNEY
Private Cloud
We have a rich legacy of innovation with a passion for fanatical customer support over the past 25 years
About Rackspace
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WHY: Customer outcomes we deliver
Improve customer experience, time-to-market and developer productivity
Enable revenue growth and profitability through a nimble and agile IT operation
Reduce strategic and operational risk to prevent vendor lock-in and obsolescence
Reduce the overall TCO over the lifetime of the workload
Improve Security, Compliance, and Operational Resilience of business operations
HOW: We help
Manage
Transform
Advise
Optimize & Innovate
WHAT: Featured solutions
Cloud Migration & Modernization
Workload-aware migration & modernization | Database migrations
Industrialize AI
Enterprise Insights | Intelligent Automation | MLOps | GPUaaS
Enable The Future of Work
M365 | Copilot | Low code-No code
Intelligent applications
Application modernization �& development
Cloud-native Data
Advanced analytics | �Data platforms | Self-service BI
Security
Zero trust | Edge security | �Cyber Defense Center
Cloud Managed Services
Modern Operations | �Rackspace Managed Cloud
Scale Your Workforce
Rackspace Elastic Engineering
Cloud Platform & Optimization
Cloud foundations | Pvt/Sovereign |FinOps
Hybrid Cloud infrastructure
Private/Sovereign Cloud
Our services focus on the emerging, AI-enabled and �hybrid-cloud needs of our customers
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We serve clients across industry verticals and regions
Technology, Media, Entertainment & Gaming
Healthcare & Lifesciences
Products & Resources
Banking, Finance & Insurance
Public Sector
Private Equity
USA
LATAM
UK
DACH
KSA
AUS
Achilles Ltd
Audi
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Analyst Rankings
Awards and Recognitions
Competencies
IP and Accelerators
Our Service Offerings
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Simplifying complex technology with rapid business value realization
Cloud-Native
The Reframe
AI-First Model
Strategic Pillars
The Method
Forward Deployed Engineering (FDE)
Shared or dedicated teams that scale on demand, moving beyond rigid SOWs and rapid value realization
The Accelerators
Strategic Platforms
Leveraging Palantir, Uniphore and our purpose-built IP as force multipliers
The Foundation
Core Capabilities
A secure, multi-cloud core built on 25+ years of production experience.
Cloud First Model
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“Cloud First” at the core and “AI First” in every decision
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AI-First in Our Solutions: Translating to Customer Outcomes
IP-enabled Labor Minus Model with AI-native tools and automation embedded across every engagement — compressing timelines, elevating quality, reducing cost
Infra and Cloud
Data and AI
Apps and Products
Cyber Security
AI-Driven Migrations
AIOps and Autonomous Management
AI-Enhanced Development
AI-Native Security
Powered by AI-First Delivery Platform
IMPACT FOR RACKSPACE
BUSINESS IMPACT FOR CUSTOMERS
BY THE NUMBERS
AI-Enabled Accelerators and IP in Our Services
Automation and Platform
Assessments
Cloud Management Platform
Assessments
Data Modernization
Rackspace AIRE
Outcome Enabled
12 months → 20 weeks
Migration timeline compression
Outcome Enabled
Compressing SQL migration from weeks to hours
Outcome Enabled
3-5x - Developer productivity multiplier
Outcome Enabled
95% Reduction in security vulnerabilities
Outcome Enabled
Reduced time to recommendations hours to minutes
“Proactive prevention of INC Ransomware group attacks against 2 customers using RAISE in Dec 2025”
“2000 CAR customer scans of their AWS environments in Dec 2025, largest by any partner”
“Replaced 60 developers from a traditional GSI to 7 for CBA to modernize their applications”
“Migrated 450 dashboards and visualizations from Tableau to PowerBI in 2 weeks for Lifepoint Health”
"Validations and testing of 1000s of firewall rules during a migration in under 1 hour that normally takes 2 weeks"
** Estimated
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AI, Partnerships & Capabilities
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Customer First. Cloud First. AI First
We bridge the gap between AI ambition and execution
THE CHALLENGE
Assess Design Build Operate Optimize
Rackspace AI Services
Advisory, Professional & Managed Services
“Organizations struggle to harness the power of AI and Cloud due to complexity, skill gaps, and ballooning costs.”
THE RESULT
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INDUSTRY AND DOMAIN AI EXPERTISE
Contact center
Software dev
Customer care
Sales & Marketing
Knowledge worker productivity
Specialized use cases
Streamline operations
AI strategy & use case prioritization
PARTNERS
Our Strengths
Solutions
Logos
100+
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Our Differentiators
AI that is Architected to Evolve
Designed to Sustain
Engineered to Operate
Technical Excellence
5 Industry Agents
10+ Agentic solutions on Marketplace
Build Custom SLMs
20+ multi-agent reference architectures
Analysts
Leader in the US for ”Generative AI Services for Microsoft Cloud”, 2024
Contender in Advanced Analytics and AI Services, 2025
Product Challenger in AI Services 2024
Leader’s quadrant of the 2024 Generative AI Service Providers PeMa Quadrant
Competencies
Generative AI Innovation Launch Partner
AI & Machine Learning Specialization
Generative AI Competency
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We meet enterprises where they are in the journey
Rackspace’s methodology aligned to customer AI maturity and business outcomes.
AI-Aware Curiosity
AI-Enabled Experimentation
AI-Operational Production
AI-Scaled Expansion
AI-First Transformation
Capabilities & Offerings |
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Our Approach | Demystification & Alignment | Safe, Governed Exploration | Engineering Rigor | Standardization & Economics | Trusted Innovation Partner |
Customer Success | $2B insurer partnered with Rackspace for AI strategy and secured $4M funding | Multi-state health system used Rackspace’s HIPAA-compliant AI sandbox | J.Crew - Deployment achieved 60% reduction | Atlas Air — achieving 20% reduction in total AI infrastructure costs | Built proprietary AI models & scalable MLOps/LLMOps for a leading Tech company |
Outcomes |
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| Hyperscaler-neutral assessment; Industry-focused business workshops | Cloud Agnostic Agent Architecture Rapid Value Realization Multi-Cloud Flexibility | Sovereign AI Options Rackspace AI Fabric Managed AI Operations | Hyperscaler Cost Arbitrage Multi-Cloud AI Platform FinOps for AI | Forward Deployed Engineering Rackspace Innovation Labs Outcome-based model |
Persistent Foundations (Maturing Across All Stages)
Governance & Security
Data Strategy
People & Culture
Responsible AI
Cloud Foundation
Our Differentiation
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To deliver the outcomes, we need to shrink/bridge the intelligence gap
Cloud infrastructure is solved — but advanced AI and agentic systems require entirely new skillsets*
From infrastructure to intelligence | Real-time data architecture complexity | AI-specific governance & observability | Quality data for Agents | Cross-functional AI product thinking |
Teams mastered cloud ops but lacked expertise in feature engineering, model serving, vector databases and production machine learning patterns. | Building streaming architectures with event-driven systems, change data capture and sub-second latency at scale. | Traditional monitoring doesn’t capture model drift, bias detection, hallucination rates or embedded quality degradation. | Agents require vetted data to action on meaningfully and deliver the right outcomes | Building AI products requires blending machine learning engineering, product sense, domain expertise and user experience design. |
Impact�Machine learning models fail in production due to data drift, latency issues and poor feature engineering. | Impact�Batch-trained models struggle with eventual consistency, backpressure and stateful stream management. | Impact�Silent failures in production — models degrade without visibility into why. | Impact�Agents operating on unreliable data can compound errors exponentially, amplifying the impact of poor data across the system. | Impact�Technical sound models fail to solve actual business problems or gain user adoption. |
All workloads require 10x more specialized knowledge than cloud migration. | Real-time systems have 5-10x higher architectural complexity. | 68% lack proper machine learning observability, detecting issues only after business impact. | As agent-based systems scale and operate autonomously in real time, data quality issues can spread faster than teams can detect or intervene. | Only 20% of data science teams operate with product mindset |
* Beyond cloud: The AI and data intelligence gap
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Rackspace Enterprise AI Solutions
Cloud Engineering
Decision Intelligence and Enterprise AI Platforms
Data & Knowledge Layer
Enterprise Data Platform
Knowledge Graph
Vector Stores
Enterprise Ontology
Model Layer
Fine Tuning and Pre-training
Training LLMs with enterprise data
Evaluations
Predictive Model Optimization
Model fitting and Hyperparameters
Secure, Workload-Aware AI Infrastructure and AI Platform
Agentic and AI Solutions
Amazon Bedrock
Azure AI
Foundry
Vertex AI
Semantic Agents
MEKA (Multimodal)
RAG+
Guardrails
Industry SLMs
Model Orchestration
Advisory and Consulting Services
Data Engineering
Forward Deployed Engineering
Managed Services
Rackspace AI Reliability Engineering (AIRE)
AI Advisory & �Professional Services
AI Managed Services
AI Platforms
Rackspace IP
AI Managed Services and Operations
AI Advisory, Strategy and Governance
Rackspace Agent Factory
CareFlow
LegalMind
HealthBridge
RITA
REX
Agentic Workflows
MCP & A2A Integrations
Multi-Agent Systems
Rackspace AI Fabric
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Rackspace Enterprise AI Launchpad (REAL)�
AWS AI platform and Data services provide strategic and flexible options
Advisory & Engineering
Business-led workshops
AI Strategy | Use Case design | Roadmaps
Bootcamps
AI Bootcamps (ABCsTM) | Rackspace Rapid Bootcamp
Rackspace AI Fabric
AI Governance | Agentic Foundations |
Data Governance
Forward Deployed Engineering
Data Foundations | Applied AI Studio | Cognitive Applications
Secure AI Landing Zones
Security Policies | AI Guardrails
AI & ML Migrations
OpenAI to Nova | Cuda to Neuron
Secure Enterprise AI Platform
Architecture | Design | Security
AWS Neuron
AI INFRASTRUCTURE
AI Agents
Fine-tuning | Model Development & Evaluations | Industry Agents
Data Foundations
Ontology | Data Mesh | Enterprise Integrations | Knowledge Graphs | RAG+
Forward Deployed Engineering
Rapid Bootcamps |
Discovery to Production
Data Platform Migrations
Lakehouses | Data Mesh | GenBI
Data Foundry for AI
Foundations | Governance | Ontology
Data Engineering
Streaming | Transformations
Amazon
SageMaker
DATA FOUNDATIONS
ISV AI Partners
AI-Driven Business Applications
Rapid Prototyping | AI-DLC
Model Development
Feature Engineering | Data Science
Model Operations
MLOps | FAIRTM LLMOps | FMOps
Model Training
Model Evaluation | Fine Tuning
Amazon
SageMaker AI
MACHINE LEARNING
Generative AI Industrialization
Evaluations | Fine Tuning | Distillation | Guardrails
Amazon Quick Suite
Flows | Enterprise Knowledge & Search | Business Automation
Amazon Bedrock
GENERATIVE AI
Agent Accelerators
MCP Orchestration | Multi-agent Steering | Evaluations | Observability
Rackspace Industry Agents
LegalMind | Careflow | HealthBridge | RFX
Contact Center AI
Amazon Connect AI Agents | Native CX Applications
Amazon Bedrock
AgentCore
AGENTIC AI
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Delivering unique value in the market and applying AI-First in our delivery
AI Infrastructure
Business AI & Decision Intelligence
AI-First Delivery and Operations
Forward Deployed Engineering
Accelerating time to value
Healthcare
BFSI
Manufacturing
50%+
Reduced ramp-up time being AI-First
3x
Faster code deployment
with Rackspace Harness
4x
Faster time to market
66%
Reduction in cloud spend
50%
Improved security posture
20-40%
Savings for customers
Education
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Rackspace Value Driven Approach / Customer Journey
AIRE/ End to End – Strategy to Execution to Sustainable Value Management
IDEATE
INCUBATE
INDUSTRIALIZE
AI Bootcamp
& Use Case Workshops
AI Maturity & Targeting Performance
Roadmap, Design and Strategy
Foundational Capabilities + Organization Alignment
AI Enablement�and Literacy
Infrastructure for AI
Data Foundations�for AI
Enterprise Systems Integrations for AI
Governance + AI Operating Model
Prioritizing
Prototyping
Scaling
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Key Case Studies
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SOLUTION |
We partnered with Alvee to create a modular, agent-driven architecture using AWS Bedrock Gen AI to automate care planning, patient engagement and benefits enrollment with over 90% reliability. |
Outcome |
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Healthcare |
Alvee turns SDOH data into actionable workflows for equitable care. |
CHALLENGE |
Alvee Health faced the challenge of navigating widely varied and complex enrollment processes across multiple healthcare and social service platforms to implement personalized SDOH care plans. |
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SOLUTION |
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Outcome |
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Energy |
Global leader in clean energy accelerates complex, consultative selling with AI-driven, production-grade O2C. |
CHALLENGE |
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Revolutionizing Retail Support Through AI Agents
J.Crew Group is an internationally recognized omnichannel retailer operating iconic American brands including J.Crew, J.Crew Factory and Madewell. With over 580 retail stores across the United States and a robust ecommerce presence, the company serves millions of customers seeking timeless, classic and high-quality fashion. Operating with a best-in-breed technology philosophy, J.Crew Group continuously seeks innovative solutions to enhance operational efficiency across their retail, supply chain and customer service operations.
80% IMPROVEMENT
Customer Satisfaction Scores
70% REDUCTION
IT help desk call volume
1M+ SAVINGS
Labor costs and training new Support Staff
Solution
Challenges
Developed JCG Buddy (IT support), JCI Connect (vendor support) and JCG Ally (customer service) using LangGraph and Amazon Bedrock
Three custom AI agents
Overwhelmed support systems
With IT help desk and customer service teams spending excessive time on routine inquiries, impacting productivity
Limited AI capabilities
Prevented the company from building custom AI solutions independently
Multi-language vendor communications
Created inefficiencies for supply chain teams working with global partners
Fragmented knowledge sources
Across SharePoint, documentation and web resources made information retrieval time-consuming
Business Impact
Outcome
Freeing technical staff for strategic initiatives
70% reduction in IT help desk calls
Built on Amazon Bedrock, Lambda, OpenSearch and S3 with Google Vertex AI for advanced domain search capabilities for the agents
AWS-native architecture
Seamlessly integrated Microsoft Azure Entra for SSO and Google Vertex AI for controlled domain search access to the agents
Multicloud integration
Enabled multi-format support (PDFs, images, PowerPoint, Excel, web pages) with 7-day automated refresh cycles
Intelligent document processing
Implemented department-specific responses and multilingual capabilities for global vendor communications
Role-based personalization
Per supply chain employee through automated vendor query resolution
16+ hours daily savings
Timeline using reusable AWS CDK infrastructure templates
Weeks vs. months deployment
With instant access to accurate, role-scoped information
Enhanced employee satisfaction
To management through AI-powered first-level support
Reduced escalations
Established with scalable AI architecture supporting future agent development
Foundation for AI innovation
Escalation bottlenecks
With routine questions consuming developer and leadership time that could be automated
Maintained through controlled data sources and approved domain restrictions
100% security compliance
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Accelerating Banking Innovation Through Enterprise AI Automation
A leading Australian Bank operating across retail, commercial, and institutional banking sectors with complex regulatory requirements and legacy systems. The Core Banking division manages critical financial products and services while navigating stringent compliance frameworks across Privacy, Model Risk, and Cyber domains. With ambitious digital transformation goals, the bank sought to scale AI capabilities while maintaining operational excellence and regulatory compliance in a highly regulated financial services environment.
SOLUTION
CHALLENGES
Deployed Amazon Bedrock multi-agent system for automated document analysis, product interpretation, and scenario generation with iterative refinement workflows
Agentic AI Requirements Framework
Manual requirements gathering
Taking up to 4 months through stakeholder interviews and spreadsheet modeling, creating significant product development bottlenecks
Fragmented risk frameworks
Across Privacy, Model Risk, and Cyber domains causing 6+ week approval delays and compliance gaps
Reactive operational monitoring
With disconnected log management across Autosys, OTC, and Greenfield pipelines impacting system reliability
Prolonged incident resolution
Due to manual diagnostic processes and lack of unified visibility across technology stack
BUSINESS IMPACT
OUTCOME
From 4 months to <4 weeks while maintaining quality
Requirements generation accelerated 10x
From 6 weeks to <3 weeks through automated compliance checking
Compliance model validation reduced by 50%
With automated risk assessment and document generation
70% reduction in manual compliance effort
With AI-powered root cause analysis and automated incident resolution
Proactive monitoring established
For Cloud engineers, improving diagnostic efficiency
Natural language operations enabled
Platforms and shared knowledge bases
Cross-functional collab-enhanced by unified AI
Scaling limitations
Preventing rapid AI adoption due to compliance complexity and operational constraints
With reusable frameworks extending to test scripts and API development
Foundation for AI scaling created
10x acceleration in requirements documentation
70% reduction in compliance effort
Implemented Proactive monitoring
Automated risk navigation and incident resolution
Built multi-agent architecture for automated risk assessment, compliance document generation, and workflow automation integrated with RiskInSite and JIRA
Intelligent Risk Navigation Assistant
Implemented unified log ingestion with Bedrock-powered conversational AI interface and automated remediation through ServiceNow and AWS Lambda
Centralized AIOps Platform
Consolidated historical requirements, Group Reference Data (GRD), pipeline lineage, and incident runbooks into centralized AI-accessible repositories
Knowledge Base Integration
Monitoring and comprehensive audit trails
Regulatory compliance with continuous
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TSK-13509
Thank you
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www.rackspace.com
United States: 1800 961 2888
Argentina: 0800 999 1438
Australia: 1800 722 577
Brazil: 0800 020 1543
Canada: 1800 741 0054
Chile: 1230 020 7920
Colombia: 01800 518 4577
EMEA: +44 1473 760 226
India: 000800 100 8796
Germany: 0800 723 8997
Hong Kong: 800 900 330
Malaysia: +1800 812620
Mexico: 800 099 0352
New Zealand: 0800 451 613
Peru: 0800 55413
Philippines: 1800 111 01 468
Singapore: +65 6428 6102
Venezuela: 800 136 2342
UAE: 1 716 559 9881
United Kingdom: 0800 988 0100
© Rackspace | Rackspace® Fanatical Support® and other Rackspace marks are either registered service marks or service marks of Rackspace US, Inc. in the United States and other countries. Features, benefits and pricing presented depend on system configuration and are subject to change without notice. Rackspace disclaims any representation, warranty or other legal commitment regarding its services except for those expressly stated in a Rackspace services agreement. All other trademarks, service marks, images, products and brands remain the sole property of their respective holders and do not imply endorsement or sponsorship.
Appendix
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20,000+
Customers
Global and broad
Customer base
5,900+
Dedicated Rackers globally
2,600+
Certified technically experts
9,500+
Total technical certifications
Trusted
Recognized
History of innovation
Rackspace at a glance
Industry Analysts
People & Expertise
Customer Base
Brand
Partner Ecosystem
We are a global leader in hybrid multicloud and a premier AI solutions expert, accelerating innovation for the modern enterprise across the entire technology stack—from edge to core to cloud.
Rackspace Public Cloud Portfolio 2026 that is AI first
Engineer high-performance digital products using Gen AI for faster delivery
40% reduction in development, 60% decrease in testing time
Applications & �Products Engineering
1.5X faster time to market
Drive smarter decisions with modern data capabilities and AI-driven insights
Data & AI �Engineering
70% events automated to resolution
Unify secure multi-cloud infrastructure with AIOps for frictionless performance
Infrastructure & Cloud Engineering
80% automation across complex business processes
Automate processes and improve experiences with AI-powered decisioning
Platform & Automation Engineering
Practice Araes
AI Consulting & Advisory With Service As Software1
Develop an AI Transformation Journey that aligns strategy with AI insights, accelerates delivery, and strengthens organizational resilience
Professional Services
Design & build solutions for migration, modernization, optimization, analytics, AI and security (project-based fees)
Resell
Resale of public cloud platforms, such as AWS, Google Cloud and Microsoft Azure, to customers across industries and geographies
Managed Services
Operate clients’ cloud environments and provide 24x7 platform support (recurring fees based on capacity)
Contracting Model
6.6 Bn events /month correlated to 670 actionable alerts
Reduce risk with Zero Trust, AI-driven threat protection, and smart compliance
Cyber Security
Engineering
Rackspace Public Cloud Portfolio consists of 5 practice areas and the offerings are delivered across 4 contracting models
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Rackspace Palantir Bootcamp
Zero to Proof of Value in five days*
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1 | Hands on keyboards |
2 | Build intuition to move beyond chatbot |
3 | Solve a real problem |
4 | Operate securely |
5 | Exit with Proof of Value |
* Bootcamp may be longer due to complexity, technical requirements, and customer environment access provisioning
Palantir AI FDE
SOLUTION OVERVIEW
AI Foundry Development Environment (AI FDE) — an AI-native agentic engineer embedded inside Palantir Foundry that builds, evaluates, debugs, and self-improves platform solutions autonomously, shifting human work into AI work so teams can focus on what matters most
What it does
Builds & edits ontology objects, performs data integrations, fixes pipeline issues, and develops full applications — operating the platform autonomously on behalf of human builders
Understands full platform lineage — traverses objects, links, backing datasets, code repositories, and documentation to provide deep contextual intelligence for any task
Builds complete AIP Logic functions from scratch — decomposes problems, explores ontology data, reads regulatory and business documentation, and generates production-grade agent logic with detailed prompts
Introduces evals-driven development: auto-generates diverse test cases from historical data, runs eval suites, diagnoses failures, and iterates logic in autonomous loops until accuracy targets are met
Creates and edits Workshop applications and Automates — building full front-end interfaces on ontology primitives and connecting agents to real-world event triggers for proactive execution
Interoperates with no-code tools (Pipeline Builder, Logic) alongside code repositories — letting builders use their preferred tools within AI FDE with automatic mode switching across the platform
Core Capabilities
Explore & Govern
Understand complex models, lineage, and business context in hours — auto-document ontologies, decode business acronyms, enrich metadata, and enable enterprise-wide discoverability at scale
Debug & Validate
Diagnose bottlenecks, build test harnesses, probe edge cases, and run evals-driven loops — went from 90% failing to 90% passing in a single iteration cycle
Create & Ship
Generate usable frameworks, migrate codebases, build Workshop apps and Automates, and ship production features — with minimal human intervention and parallel session execution
Learn & Compound
Ingests user feedback from the ontology, synthesises root causes, writes new test cases, self-corrects logic, and builds meta-agents — systems that get better with every human interaction
Bottom Line
AI FDE is a paradigm shift from co-pilot to agentic engineer. It collapses onboarding from months to hours, turns non-engineers into production-ready builders, governs and scales enterprise ontologies without slowing teams down, builds eval-validated agents that learn from production failures, and architects compounding multi-agent systems — fundamentally rewriting the build-vs-buy calculus for platform development.
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From Traditional SDLC to AI FDE-Native Development
AI FDE doesn’t just accelerate individual SDLC phases — it collapses the entire lifecycle into autonomous, evals-driven loops where exploration, build, test, deploy, and feedback happen in a single continuous session.
TRADITIONAL SDLC — HUMAN-DRIVEN, SEQUENTIAL, SLOW
BEFORE
Requirements
Weeks of stakeholder interviews, manual spec writing, tribal knowledge transfer
Design
Architects schemas, data models, and patterns manually
Build
Dedicated engineering teams code for months; SI dependency for complex builds
Test
Manual QA, evaluating outputs, ad-hoc validation — no systematic evals
Deploy
Long release cycles, central team bottleneck for approvals and governance
Feedback
Unstructured complaints, lost context, manual root-cause analysis by devs
AI FDE TRANSFORMS EVERY PHASE
AI FDE-NATIVE SDLC — AGENTIC, AUTONOMOUS, CONTINUOUS
AFTER
Explore
AI FDE auto-explores ontology, traverses lineage, reads docs & regulations, decodes acronyms — hours not weeks
Architect
AI FDE designs solution plans, decomposes problems, recommends existing objects to reduce duplication
Build
AI FDE generates Logic, functions, Workshop apps, Automations, and code — non-engineers can ship production features
Eval
Auto-generates diverse test cases, runs eval suites, diagnoses failures, iterates until accuracy targets are met
Govern
Auto-documents ontology, enriches metadata, branches & merges — governance at the speed of development
Learn
Ingests structured user feedback, synthesizes root causes, writes new evals, self-corrects — agents that compound
KEY PARADIGM SHIFTS
Sequential → Continuous
Traditional SDLC phases run in isolation. AI FDE collapses explore → build → eval → fix into a single autonomous loop that runs in minutes.
Human-Only → Human + Agent
Every phase now has AI FDE as an active participant — from requirements discovery to production debugging to writing better feedback prompts.
Static → Self-Improving
Traditional deploys are frozen until the next release. AI FDE agents learn from every rejection, rewrite evals, and improve logic continuously in production.
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Lear Corporation
Largest US-based automotive supplier | 170,000+ employees | 250+ factories across 37 countries | Supplies seating & electrical components to 480+ vehicle programs globally
Challenge
100 → 16,000
Platform users in ~12 months
Solution Highlights
Outcomes
4 → 280+
Approved use cases (dev + production)
90–95%
Business acronym decode accuracy — zero context provided
1,000+
Active builders self-serving on the platform
New paradigm
Build-vs-buy calculus fundamentally shifted — single builders replacing months of SI work
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Trinity Industries
Founded 1933 | Premier North American railcar manufacturer & lessor | Moves 900+ commodities across the continent | Planning, estimating, procurement & engineering all operating in Foundry
Challenge
Hours vs. Months
Engineer onboarding to complex MRP logic
Solution Highlights
Outcomes
Sub-second
Pivot table loads — previously timing out at 10K rows
2 hrs vs. 2 months
New quoting framework coded by a non-engineer using AI FDE
Parallel execution
Multiple AI FDE sessions running concurrently per user — async productivity
Democratised dev
Data engineers, directors & non-coders now shipping production features
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Scaling Engineering Velocity: AI FDE Technical Use Cases for AEO (1/2)
Use case | The problem | AI FDE Implementation | Engineering/AI Team Value |
Supply Chain "Mesh Network" Logic Optimization | AEO’s logistics arm, Quiet Platforms, integrates 40+ carriers and 13+ fulfillment centers. Engineering teams struggle with manually updating routing logic as carrier rates, fuel surcharges, or "circularity" (returns) requirements shift. | AI FDE can be tasked via natural language to auto-generate Python transforms that ingest real-time carrier API data into the Foundry Ontology. It identifies bottlenecks in the "Control Tower" by traversing the supply chain graph and proposing code-based optimizations to re-route parcels based on "lowest carbon footprint" or "fastest last-mile" constraints. | Reduces the cycle time for deploying new logistics business rules from weeks to minutes via a branch-and-merge code workflow. |
Automated Multi-Tenant Data Normalization | Integrating third-party brands (e.g., Steve Madden, Kohl’s) into AEO’s logistics infrastructure requires mapping disparate schemas (SAP, Oracle, bespoke ERPs) into a unified retail ontology. | The AI FDE acts as a Schema Mapping Agent. It analyzes raw source data from Snowflake/Google Cloud, identifies semantic relationships (e.g., mapping "SKU_ID" in one system to "Product_Ref" in another), and automatically writes the TypeScript functions required to normalize these into a "Single Source of Truth" product object. | Automates 80% of the "data janitor" work, allowing AI teams to focus on building demand forecasting models rather than debugging pipeline joins. |
Operationalizing "Behavioral Customer IDs" at Scale | AEO needs to stitch together first-party data from web, app, and in-store (Open-Sell model) to drive personalization, but data fragmentation across legacy silos prevents a real-time 360-view. | AI FDE can build and maintain a Dynamic Identity Graph. It writes the logic to link anonymous cookies to known loyalty IDs across the ontology, then exposes these as "Object Actions" for frontend developers. When a new data source (e.g., a new social platform API) is added, the AI FDE updates the ontology and updates all downstream documentation automatically. | Provides a clean, API-accessible Object Software Development Kit (OSDK) that frontend engineers can use to build personalized React-based shopping experiences without writing a single SQL query. |
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Scaling Engineering Velocity: AI FDE Technical Use Cases for AEO (2/2)
Use case | The problem | AI FDE Implementation | Engineering/AI Team Value |
MLOps: Automated Model Retraining & Observability | AI teams at AEO deploy ML models for markdown optimization, but "concept drift" occurs as fashion trends shift seasonally. Manually monitoring and retraining these models is labor-intensive. | The AI FDE monitors model performance within the Foundry AIP Logic environment. When it detects a drop in accuracy below a technical threshold, it automatically triggers a retraining pipeline, generates a "Model Performance Report" in plain English, and opens a Pull Request with the updated model weights for the data scientist to review. | Establishes a "Closed-Loop" MLOps framework where models are self-healing, ensuring markdown strategies remain accurate during volatile peak seasons. |
Governance-as-Code for Retail Data Mesh | With over 1,000 internal users and high-velocity data, maintaining documentation and compliance (GDPR/CCPA) for retail data is an engineering nightmare. | AI FDE performs Automated Governance Audits. It can be asked to "Audit all objects containing PII and ensure they have the correct 'Restricted' markings." It then scans the entire platform, applies metadata tags, and generates technical documentation for every pipeline it touches, ensuring the codebase is always self-documenting. | Guarantees platform integrity and compliance "by design," preventing technical debt and ensuring that the data used for AI training is always governed and auditable. |
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Mapping Rackspace x Palantir AIP Foundry Capabilities to AEO's Engineering & Data Landscape (1/2)
Real Rewards (35M+ Members) → Foundry Customer Entity Resolution + Omnichannel View
AEO's Real Rewards spans AE, Aerie, and OFFLINE across in-store, web, and app — creating fragmented identity data across brands and channels. Foundry's entity resolution (fuzzy matching + ML) can unify customer records into a single customer DNA view, eliminating duplicates and improving targeting accuracy by ~40%. This directly strengthens AEO's AI-driven dynamic pricing and personalized offers that already drove 50 bps margin improvement.
500+ Weekly Content Pieces → Foundry Digital Twin + Personalized Marketing
AEO's AI Marketing Council and Writer partnership scale content, but the insight-to-action loop between customer behavior data and content targeting remains a gap. Foundry's HyperAuto tooling can build a dynamic customer digital twin in hours — feeding real-time segmentation and recommendations directly into AEO's marketing execution layer, closing the loop between what customers do and what content they see.
Four-Layer Supply Chain ML → Foundry Orchestration & Bi-Directional Sync
Brandon Friez's layered architecture (demand forecasting → inventory repositioning → carrier optimization → orchestration) currently operates as a stacked model. Foundry adds the missing bi-directional decision capture — feeding operational outcomes back into models for continuous recalibration. The network simulation capability AEO used for tariff mitigation becomes persistent and real-time rather than event-triggered, enabling always-on scenario planning.
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Mapping Rackspace x Palantir AIP Foundry Capabilities to AEO's Engineering & Data Landscape (2/2)
GCP Data Stack (BigQuery + Dataplex + Airflow) → Foundry as the Semantic & Operational Layer
AEO's data infrastructure is mature but analytics-oriented — built for warehousing and governance. Foundry doesn't replace BigQuery or Dataplex; it sits on top as an operational decision layer — connecting data teams, ML models, and business operators through a single semantic interface. This bridges the gap between Bk Vasan's data platform and the frontline operators making real-time merchandising, pricing, and supply chain decisions.
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Composable Commerce (Jumpmind + Microservices) → Foundry as the Decision Fabric
AEO's composable POS architecture across 7,000+ devices generates massive transaction-level decision exhaust that currently flows one way. Foundry captures this exhaust — store-level transaction patterns, device-type preferences, checkout behavior — and feeds it into clustering and affinity models that improve assortment, bundling, and promotion decisions at the individual store level.
Aerie Body-Positive Brand + AR/3D Commerce → Affinity Modeling at Scale
Aerie's authenticity-first positioning and AEO's Snapchat AR try-on / 3D visualization investments generate rich behavioral and engagement signals beyond purchase data. Foundry's customer-product affinity scoring can model these non-transactional interactions — linking AR engagement, social content interaction, and loyalty tier behavior to predict purchase intent and optimize product-customer matching without compromising brand values.
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