Results That Actually Matter
We don't measure success in presentations delivered or training sessions completed. We measure it in revenue generated, cost saved, and velocity gained — verified, documented, and independently audited.
Fractal LegacyLift Results
Enterprise AI transformation case studies
Global SaaS Platform Reduces Cloud Costs 87% While Scaling 4X
Challenge: $8B enterprise SaaS provider was spending $2.4M/month on AWS to serve 50K daily active users. Architecture was lift-and-shift from on-prem. Every new customer required manual infrastructure scaling. Leadership wanted to 4X user base without 4X-ing cloud spend.
Solution: Fractal LegacyLift full transformation. We rearchitected their monolith into event-driven microservices, implemented auto-scaling with predictive demand modeling, migrated 80% of workloads to serverless, and built AI-powered cost optimization that dynamically rightsizes resources.
Timeline: 9-month engagement across 3 phases (Proof of Value → Core Migration → Production Hardening)
VERIFIED OUTCOMES
87% reduction in AWS spend
4X growth without scaling costs
Down from 3 weeks
Healthcare Analytics Provider Builds AI-Powered Patient Risk Scoring in 12 Weeks
Challenge: $250M healthcare analytics company had 15 years of patient data sitting in siloed databases. Clinical teams manually reviewed charts to identify high-risk patients — a process taking 40 hours per hospital per week. They wanted AI-powered risk scoring but had zero ML infrastructure.
Solution: Proof of Value engagement via Fractal LegacyLift. We built a real-time patient risk scoring engine using their historical data, deployed it on a serverless ML pipeline, integrated with their existing EHR systems, and trained their team to iterate on models independently.
Timeline: 12-week Proof of Value (data pipeline → model training → production deployment → team training)
VERIFIED OUTCOMES
Predicting 30-day readmissions
From reduced readmissions
Per hospital in chart review
Fintech Startup Cuts Fraud Losses 94% With Real-Time ML Detection
Challenge: Fast-growing payment processor was losing $1.2M/month to fraud. Their rule-based system caught obvious fraud but missed sophisticated attacks. Fraud analysts spent 60+ hours/week reviewing flagged transactions manually. High false-positive rate was blocking legitimate customers.
Solution: Fractal LegacyLift Proof of Value. Built real-time fraud detection using graph neural networks to identify suspicious transaction patterns, deployed ensemble models that learn from analyst feedback, integrated with their payment gateway for sub-100ms decisions.
Timeline: 8-week Proof of Value → 6-month full production rollout
VERIFIED OUTCOMES
94% reduction in fraud losses
Down from 8% with rules
Real-time at scale
Fractal Spark Results
Dev consultancy partnership case studies
$28M Dev Shop Adds $6.2M ARR in AI Services Without Hiring ML Engineers
Challenge: 85-person dev consultancy was losing deals to AI-native competitors. Clients wanted RAG systems, LLM integrations, and ML-powered features. Agency had zero ML expertise and couldn't hire fast enough. Tried to staff AI projects with web devs — results were poor.
Solution: Fractal Spark partnership. We white-labeled AI delivery for their first 8 client projects, trained their senior devs on AI patterns via pair programming, built reusable AI components they could deploy independently, and transitioned them to 85% self-sufficient in 9 months.
Timeline: 9-month partnership (4 months co-delivery → 3 months oversight → 2 months transition)
VERIFIED OUTCOMES
From AI service line
Delivered in first year
Now delivering independently
Boutique Firm Wins $4.8M Enterprise AI Deal With Spark Credibility
Challenge: 12-person boutique consultancy had a relationship with a Fortune 500 prospect wanting to build an AI-powered procurement platform. Client loved the boutique's domain expertise but doubted their AI capabilities. Boutique couldn't staff the project and risked losing the deal.
Solution: Fractal Spark co-delivery. We provided AI engineering capacity under their brand, presented jointly in client meetings as their "AI team," handled all ML infrastructure and model work, while boutique led product strategy and client relationships.
Timeline: 14-month engagement (2 months pre-sales support → 12 months delivery)
VERIFIED OUTCOMES
Would've lost without AI cred
At 50/50 split (15% ongoing)
Using Spark as AI partner
Fractal Forge Results
Enterprise knowledge graph & insight exploration case studies
Data Team Cuts Dashboard Build Time From 3 Weeks to 4 Hours With BI2AI
Challenge: 40-person data team at a $500M e-commerce company was drowning in dashboard requests. Building a new Tableau dashboard took 2–3 weeks (requirements gathering → SQL → viz → QA → deploy). Backlog was 8 months. Business teams resorted to building their own Excel reports with stale data.
Solution: Deployed Birdseye (Fractal Forge) with BI2AI module. Business users now ask questions in Slack ("show me top 10 products by revenue last quarter, broken down by region"). Birdseye generates SQL, queries warehouse, creates interactive viz, and responds in Slack — all in under 30 seconds.
Timeline: 6-week deployment (2 weeks setup → 2 weeks data integration → 2 weeks user rollout)
VERIFIED OUTCOMES
Down from 3 weeks
Up from 12/month manually
Cleared 8-month queue
More Case Studies Coming
Fractal Forge is currently in early access. Birdseye, our enterprise knowledge graph & insight exploration tool, is available to select data-driven organizations. We'll publish additional case studies as they achieve verified outcomes.
What Our Clients Say
"FractalShift cut our AWS bill by 87% while we scaled 4X. They didn't just optimize — they completely rearchitected our platform. Every claim they made in the proposal, they delivered. Best investment we've made."
Sarah Chen
VP Engineering, Global SaaS Platform
"Spark partnership let us add $6.2M in AI services without hiring a single ML engineer. They white-labeled delivery for our first 8 projects, trained our team, and got us to 85% self-sufficient in 9 months. Game changer."
Marcus Williams
CEO, Mid-Market Dev Consultancy
"We went from 3 weeks per dashboard to 4 hours. Birdseye cleared our 8-month backlog in 6 weeks. Business teams now get answers instantly instead of filing tickets. This is how data teams should work."
Dr. Amanda Foster
Head of Data, E-Commerce Company
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