Case Studies

Companies That Replaced Their Traditional SI With Prime

Real stories of organizations that fired their 10-20 person consulting pods and hired Prime instead. See the before/after: traditional SI engagement vs. 1 expert lead + AI agents.

Case Study 01

Financial Services Firm Cuts Cloud Migration Cost by 87% — Delivered in 8 Weeks Instead of 9 Months

A leading financial services firm needed to migrate 40+ applications from on-premise data centers to Azure. Their incumbent SI — a Big Four firm — scoped a 9-month engagement.

The Traditional SI Proposal

  • 15-person team: 1 engagement manager, 2 architects, 4 developers, 3 testers, 2 DevOps engineers, 2 analysts, 1 PM
  • 9-month timeline with waterfall-style delivery
  • $3.2M total engagement cost ($355K/month)
  • Senior architects in the sales process — junior consultants would do the actual work
  • 6-week ramp-up period before any actual migration began
$3.2M
quoted by the incumbent SI for a 9-month cloud migration engagement with a 15-person pod.

The Prime Engagement

Prime assigned 1 senior cloud architect with 20+ years of financial services experience. AI agents handled:

  • Automated infrastructure assessment and dependency mapping across all 40+ applications
  • Code generation for Terraform/IaC templates, container configurations, and CI/CD pipelines
  • Automated testing suites — integration, load, security, and regression
  • Comprehensive migration runbooks and documentation
  • Data migration scripts with validation and rollback procedures

The expert lead made all architectural decisions, handled edge cases, managed stakeholder communication, and ensured zero-downtime cutover.

The Result

Traditional SI
$3.2M / 9 months
→
Prime AI
$420K / 8 weeks

Measured Results

  • 87% cost savings ($3.2M → $420K)
  • Delivered in 8 weeks vs. 9-month SI timeline
  • Zero downtime during migration — all 40+ apps migrated successfully
  • Same senior architect from scoping through go-live (no bait-and-switch)
  • Complete documentation and runbooks generated by AI agents

Why It Worked

The traditional SI needed 15 people because most of the work — writing IaC templates, migration scripts, test suites, and documentation — is repetitive execution work. AI agents handle that at machine speed.

What you actually need is one senior architect who knows the domain, makes the right decisions, and ensures quality. That's exactly what Prime delivers.

Case Study 02

Telecom Provider Replaces $500K/Month Accenture Pod With Prime — Gets Better Results

A major telecom provider had been running a $500K/month application modernization engagement with Accenture for over a year. The results were disappointing.

The Problem With Their Traditional SI

  • 20-person team, but only 2 people understood the architecture deeply
  • Constant staff rotation — 3 different "leads" in 12 months
  • Junior developers producing code that required extensive rework
  • $6M spent over 12 months with only 30% of the roadmap completed
  • Documentation was sparse and always out of date
$6M
spent over 12 months with only 30% of the modernization roadmap completed. At that rate, the full engagement would cost $20M.

Switching to Prime

The client terminated the Accenture engagement and brought in Prime. We assigned 2 senior leads — one for backend modernization, one for data platform migration.

AI agents took over:

  • Code refactoring from monolith to microservices — AI-generated, expert-reviewed
  • Automated test generation across 200+ API endpoints
  • Database migration scripts with data validation and reconciliation
  • CI/CD pipeline setup and infrastructure-as-code for all environments
  • Comprehensive API documentation and architecture decision records
  • Security scanning and compliance automation

The Result

Accenture (12 months)
$6M / 30% done
→
Prime (16 weeks)
$640K / 100% done

Measured Results

  • Completed the remaining 70% of the roadmap in 16 weeks
  • Total Prime cost: $640K vs. projected $14M to finish with Accenture
  • Code quality scores improved — AI-generated code with senior review vs. junior-written code
  • Zero staff rotation — same 2 leads from day one to completion
  • Full documentation and test coverage generated automatically

The Bait-and-Switch Problem

Traditional SIs sell you their best talent. Then they staff with juniors and offshore contractors. You're paying partner rates for analyst-level work.

Prime eliminates this entirely. The senior expert who scopes your project is the same person who delivers it. AI agents replace the juniors — and they're faster, cheaper, and never rotate off your project.

Case Study 03

Energy Company Builds Enterprise AI Platform in 6 Weeks — SI Quoted 8 Months

A Fortune 500 energy company wanted to build an internal AI platform — enabling business units to deploy AI agents with proper governance, guardrails, and monitoring.

They received proposals from three traditional SIs.

What the Traditional SIs Proposed

  • Deloitte: 12-person team, 8 months, $4.8M
  • TCS: 18-person team, 10 months, $2.1M (offshore-heavy)
  • Infosys: 14-person team, 7 months, $2.8M

The Common Pattern

All three proposals included large teams, long timelines, and the inevitable "discovery phase" that adds 6-8 weeks before any real work begins.

The Prime Engagement

Prime assigned 1 senior AI platform architect — someone who had built AI platforms at two Fortune 100 companies. AI agents handled:

  • Platform infrastructure code (Kubernetes, Terraform, CI/CD) generated and tested by AI agents
  • AI governance framework implementation — model registry, guardrails, monitoring dashboards
  • Agent deployment pipelines with built-in compliance checks
  • Integration with existing SSO, data catalog, and cloud infrastructure
  • Comprehensive platform documentation, runbooks, and training materials

The lead designed the architecture, integrated with existing enterprise systems, and ran weekly demos with stakeholders.

The Result

Best SI Quote
$2.1M / 7-10 months
→
Prime AI
$280K / 6 weeks

Measured Results

  • 87-93% cost savings vs. the three SI proposals
  • Platform live in 6 weeks — business units deploying agents by week 8
  • AI governance and guardrails built in from day one (not an afterthought)
  • Platform now supports 50+ AI agents across 8 business units
  • No ongoing SI dependency — the client's team operates the platform independently

Why Traditional SIs Are Slow

Traditional SIs are structurally incentivized to be slow. They bill by the hour and staff by headcount. More people × more months = more revenue.

Prime's model is the opposite. We're incentivized to deliver fast because our expert leads are expensive — and AI agents make speed possible. Faster delivery = better economics for everyone.

Case Study 04

Retail Giant Saves $12M/Year by Replacing Wipro Managed Services With Prime

A global retail enterprise was spending $15M/year on a Wipro managed services contract covering application support, infrastructure management, and ongoing development across their e-commerce and supply chain platforms.

The Managed Services Problem

  • 60-person offshore team with high attrition (40% annual turnover)
  • Knowledge loss every quarter as team members rotated
  • Ticket resolution times averaging 5+ days for non-critical issues
  • New feature development taking 3-6 months per request
  • $15M/year with quality that was "acceptable but never excellent"
$15M
per year for managed services — with constant staff turnover, slow resolution times, and mediocre quality. The CTO called it "paying a lot for very little."

Transitioning to Prime

Prime replaced the 60-person Wipro team with 3 senior leads — one for e-commerce, one for supply chain, one for infrastructure — each backed by AI agent fleets.

  • AI agents handle incident triage, root cause analysis, and automated remediation
  • Code fixes and feature development generated by AI, reviewed by leads
  • Infrastructure monitoring and auto-scaling managed by AI agents 24/7
  • Documentation always current — AI agents update docs with every change
  • New features delivered in days/weeks instead of months

The Result

Wipro (annual)
$15M / 60 people
→
Prime (annual)
$3M / 3 leads + AI

Measured Results

  • $12M/year savings (80% reduction)
  • Ticket resolution time dropped from 5 days to 4 hours
  • Feature delivery accelerated from months to weeks
  • Zero knowledge loss — AI agents maintain institutional memory
  • Platform uptime improved from 99.5% to 99.99%

The Managed Services Trap

Traditional SI managed services contracts are designed to lock you in. Large teams create complexity, and complexity creates dependency.

Prime's model is the opposite. We aim to make your systems simpler, better documented, and more automated — so you're never locked in. Our value comes from outcomes, not headcount.

Case Study 05

Banking Institution Modernizes Core Platform in 12 Weeks — After 2 Failed SI Attempts

A large banking institution had tried twice to modernize their core lending platform. Both attempts — with two different traditional SIs — had failed.

Two Failed SI Engagements

Attempt 1 (Major SI, 18 months):

  • $8M spent over 18 months
  • Team grew to 25 people but productivity stayed flat
  • Architecture was over-engineered — designed for consultants to bill on, not for the bank to maintain
  • Project cancelled after 18 months with no production deployment

Attempt 2 (Mid-tier SI, 12 months):

  • $4.5M spent over 12 months
  • Better team, but still junior-heavy (2 seniors out of 16 people)
  • Delivered a partial solution that the bank's team couldn't maintain or extend
  • Vendor dependency meant ongoing costs of $200K/month for "support"
$12.5M
spent across two SI attempts over 30 months — with no successful production deployment.

Third Time's the Charm — With Prime

Prime assigned 2 senior leads — one banking domain expert with 25 years in lending systems, one platform architect specializing in modernization.

The approach was different from day one:

  • AI agents analyzed the existing codebase (800K+ lines) and mapped all dependencies in 3 days
  • Pragmatic architecture designed for the bank's team to maintain — not for consultant billing
  • Incremental migration strategy — not a risky big-bang rewrite
  • AI-generated microservices with comprehensive test suites (95%+ coverage)
  • Automated data migration with reconciliation checks at every step
  • Full documentation and knowledge transfer built into the process

The Result

Two SI Attempts
$12.5M / 30 months / failed
→
Prime AI
$520K / 12 weeks / live

Measured Results

  • Core lending platform modernized and live in production in 12 weeks
  • Total cost: $520K (vs. $12.5M across two failed SI attempts)
  • Bank's internal team fully capable of maintaining and extending the platform
  • Processing time reduced from 45 minutes to 3 minutes per loan application
  • Zero vendor lock-in — the bank owns everything

Why Traditional SIs Fail at Modernization

Large SI teams create complexity. More people means more coordination overhead, more meetings, more documents, and less actual building.

Prime's model is fundamentally different. One expert makes the decisions. AI agents execute. There's no coordination tax, no junior ramp-up time, no knowledge silos across a 20-person team.

Smaller teams with AI-powered execution deliver better results — not in spite of being small, but because of it.

Ready to Replace Your Traditional SI?

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