We Replaced Our Entire SaaS Stack with AI We Built Ourselves — in 20 Days
Sphere’s team used its own AI Foundry and Precision-Driven Engineering methodology to replace CRM, marketing automation, chatbot, customer support, and lead generation — eliminating $110,000 in annual SaaS and subscription fees and going live in production in under three weeks.
A Growing SaaS Bill. Fragmented Systems. No Single Source of Truth.
Like most modern businesses, Sphere was running on a stack of best-of-breed SaaS tools — each one solving a narrow problem, none of them fully integrated, and collectively costing tens of thousands of dollars every year. We decided to practice what we preach.
🔴 The Problem
- Five separate platforms — CRM, marketing automation, chatbot, customer support, lead gen — with no unified data layer
- $110,000 per year in recurring SaaS and subscription fees with annual price escalation
- Manual handoffs between systems causing lead leakage and slow response times
- Generic AI features bolted onto legacy SaaS tools — not purpose-built for our workflows
- No proprietary data advantage; every conversation, lead, and interaction owned by a vendor
- Integration tax: constant maintenance, API limits, and sync failures across platforms
🟢 The Opportunity
- Replace all five systems with a single AI-native platform built on our own infrastructure
- Eliminate recurring SaaS fees — convert CapEx investment into long-term operational savings
- Build proprietary AI that knows our business, our voice, and our buyer journey
- Create a unified data layer: every lead, conversation, and outcome in one governed system
- Demonstrate Sphere’s AI Foundry and Precision-Driven Engineering methodology on ourselves
- Produce a working reference implementation our clients can evaluate firsthand
Five Systems. One AI-Native Platform.
Each tool we retired was replaced by a purpose-built AI module — trained on our data, connected to our workflows, and owned entirely by Sphere.
CRM
Contact management, pipeline tracking, and deal intelligence — now AI-powered and integrated with every other module.
Marketing Automation
AI-drafted email sequences, dynamic segmentation, and campaign orchestration grounded in actual buyer behavior.
Website Chatbot
RAG-powered AI agent trained on Sphere’s full service catalog, case studies, and FAQs — not a scripted decision tree.
Customer Support System
AI triage, response drafting, and resolution tracking — with human-in-the-loop escalation paths built in from day one.
Lead Generation Automation
AI-scored inbound leads, automated qualification sequences, and intent-based outreach — all connected to the pipeline layer.
See the Platform Running in Production
The screenshots below show the live AI-native platform Sphere built and deployed — a single, unified interface replacing five separate SaaS tools.
Precision-Driven Engineering: The 20-Day Build
Sphere’s AI Foundry methodology doesn’t start with technology — it starts with workflow mapping and success metrics. Here’s exactly how we went from problem statement to production in 20 days.
1–3
Workflow Mapping & System Architecture
Documented current-state workflows across all five platforms. Identified data flows, integration points, and human decision gates. Defined the unified data model, AI tool access scopes, and governance controls. Finalized system architecture and approved the build plan.
4–7
Core Platform & Data Layer
Stood up the unified data layer and core CRM module. Built the AI pipeline intelligence model on historical deal and contact data. Deployed the first working agent workflow with defined approval gates and audit logging. Internal demo completed on Day 7.
8–12
AI Concierge, Lead Engine & Marketing Automation
Built and grounded the RAG-powered website chatbot using Sphere’s full content library. Deployed the lead qualification and scoring engine. Built AI-drafted campaign sequences with human review workflows. All three modules passing quality tests by Day 12.
13–16
Customer Support Hub & System Integration
Deployed the AI support triage and response drafting module with HITL escalation. Connected all five modules through a unified event bus. Built monitoring, alerting, and performance dashboards across the full platform. Integration testing completed.
17–18
Quality Evaluation & Production Hardening
Ran automated eval harnesses across every AI module against real workflow paths. Measured accuracy, latency, and failure modes. Addressed regressions. Conducted final security review and access permissions audit.
19–20
Production Launch & SaaS Cancellation
Platform went live on Day 19 with full monitoring in place. SaaS subscriptions cancelled on Day 20. Zero downtime migration. The team began working in the new system with no disruption to active deals or support queues.
Why 20 Days — Not 6 Months
Traditional software projects take months because they plan for everything before building anything. Sphere’s Precision-Driven Engineering methodology inverts this: start with production-grade pilots, validate in real workflows, then scale.
Map Before You Build
Workflow mapping, tool access definition, human control points, and success metrics before a single line of code is written. This eliminates the rework that kills traditional projects.
Ship the First Deployable Increment
Three focused sprints, each ending with working software in the team’s hands. AI Foundry pods own delivery — no handoffs, no coordination overhead, no dependency on third parties.
Evaluate, Harden, Go Live
Automated test harnesses prove quality before release. Monitoring and governance controls are built in — not bolted on. Production on Day 19. Vendor cancellation on Day 20.
The Numbers After 20 Days
You’re a Candidate If…
Sphere’s Platform Reboot™ and AI Foundry services are built for organizations that are tired of paying for tools that don’t integrate, don’t learn, and don’t compound.
You’re paying recurring SaaS fees for…
- 🔴 CRM tools you’ve outgrown or under-customized
- 🔴 Marketing automation with generic AI features
- 🔴 Chatbots that run decision trees, not intelligence
- 🔴 Support tools that require manual triage
- 🔴 Lead gen tools that don’t connect to your pipeline
What you get with Sphere instead:
- ✓ AI purpose-built for your actual workflows
- ✓ A single platform — not five disconnected tools
- ✓ Your data, in your environment, under your governance
- ✓ Production-grade delivery in weeks, not months
- ✓ Elimination of recurring SaaS fees — permanent savings
The Services Behind This Case Study
Two Sphere service lines made this possible. Both are available to enterprise clients today.
Legacy Modernization & Platform Replacement
Sphere inventories your existing systems, scores risk vs. business value, and delivers a sequenced modernization plan with ROI attached — then executes it without service interruption.
→ Explore Platform Reboot™Production AI Systems, Built & Deployed by Elite Engineering Pods
Cross-functional AI engineering pods embed into your organization to build, train, and deploy AI systems that are accurate, secure, governed, and production-ready — starting in days, not months.
→ Explore Sphere AI Foundry™Let’s Map What You Could Replace — and What It Would Save
Sphere offers a no-commitment Platform Reboot Assessment: a structured audit of your current SaaS stack, with an ROI analysis and a sequenced modernization roadmap. Most clients identify $40K–$120K in annual savings opportunities in the first session.