Every case study starts with a real problem and ends with a production outcome. Here is what that looks like in practice: quantified KPIs, real timelines, and the engineering decisions behind each one.
Filter by the kind of work that matches your situation. Each study walks through the client, the challenge, what we built, and the measured result.
Years of mergers and acquisitions left six critical VB6 components throttling automation, capping scalability, and putting precision hardware communication at risk. A phased, Gen AI-accelerated migration to .NET cleared the debt without breaking a single workflow.
Manual referral and prior-authorization work was burning 30 to 40 staff hours a week, with no audit trail and frequent errors. An embedded FDE squad shipped an AI agent wired into the EMR and CRM that runs the workflow end to end.
Tier-1 support was drowning in repetitive queries while cost-to-serve climbed despite added headcount. An FDE engagement stood up governed Agentforce service agents with escalation workflows and full observability, freeing humans for the complex work.
A 1,240-student institute was losing 15 to 20% of admin bandwidth rebuilding fee schedules each cycle and chasing parents one WhatsApp message at a time. Feezy onboarding (bulk import, Razorpay, automated cycles, and the AI reminder engine) turned that into a hands-off process.
No vague wins or vanity metrics. Every study follows the same six-part structure, so you can judge the work the way an engineer would.
Industry, company size, and geography, anonymized where confidentiality requires it.
The specific problem the client faced before the engagement, stated plainly.
The engagement model, team composition, and the key decisions we made.
The concrete deliverables: integrations, AI agents, modernized applications.
Quantified results: KPIs achieved, time saved, cost reduced, adoption rate.
The real duration of the engagement, start to production.