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Real-World Results

Projects That Delivered
Measurable Business Outcomes

Every case study follows the same format: what was broken, how it was fixed, and what the outcome was in numbers. Client names are anonymised unless permission is granted.

Case Study · 01 · Legacy Modernisation

ASP.NET WebForms Insurance Platform
Migrated to .NET 8 + Azure

Financial Services · Insurance · Pune, India
Legacy Modernisation Delivered on time
The Problem
A mid-size insurance company had a core policy management system built on ASP.NET WebForms 2.0 running on .NET Framework 4.5. The platform was processing ₹200Cr+ in policy renewals annually but had accumulated 14 years of technical debt. Deployment took 6 hours of downtime. Any developer change risked breaking core business logic scattered across code-behind files and stored procedures. The team had tried twice to rewrite it and failed both times.
The Approach
Instead of a third failed rewrite, I applied the Strangler Fig pattern — wrapping the legacy system in a new .NET 8 API shell and migrating one bounded context at a time over 16 weeks. Business logic was extracted from code-behind files and stored procedures into a clean domain layer. A CQRS architecture separated reads (handled by optimised SQL queries) from writes (handled by a domain-model-validated command pipeline). Zero downtime was maintained throughout, with feature flags controlling which system handled each request.
The Outcome
The platform was fully on .NET 8 + Azure App Service within 16 weeks with zero production incidents during migration. Deployment time dropped from 6 hours to 8 minutes via a GitHub Actions pipeline. The codebase went from zero tests to 84% unit test coverage. The client's in-house team extended the system independently within 3 months of handover — something that had been impossible before.
16wk
Full migration timeline
97%
Deployment time reduction
84%
Unit test coverage (from 0)
0
Production incidents during migration
ASP.NET → .NET 8C# 12CQRS + MediatREF Core 8Azure App ServiceAzure SQLGitHub ActionsxUnit
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Case Study · 02 · Cloud-Native Architecture

E-Commerce Platform Re-Architecture
for 10x Traffic Scale

E-Commerce · D2C Retail · 2M+ monthly users
Cloud-Native Microservices
The Problem
A fast-growing D2C brand had an ASP.NET MVC monolith that regularly crashed during sale events. A single database served the entire application — catalogue, inventory, orders, and payments. Horizontal scaling was impossible because of shared mutable state. During their last sale event, the system was down for 4 hours, losing an estimated ₹45L in revenue. They needed to handle 10x their current traffic reliably.
The Approach
I designed a domain-driven decomposition — not a full microservices rebuild, but a modular monolith first, with clear service boundaries enabling horizontal scaling of bottleneck services. Catalogue and product search were extracted to a dedicated read-model backed by Redis. Order processing was decoupled via Azure Service Bus, making it resilient to downstream failures. The database was vertically split by domain. Azure Kubernetes Service was introduced for the high-traffic services only. The entire infrastructure was codified in Terraform.
The Outcome
The next major sale event handled 2.1M concurrent users with 99.98% uptime. Product search response time dropped from 2.8s to 180ms through Redis caching. Order processing throughput increased 12x. Infrastructure cost per transaction dropped 34% due to right-sized autoscaling. The team went from one deployment per week (with weekend maintenance windows) to multiple daily deployments with zero-downtime rolling updates.
99.98%
Uptime during peak sale
12x
Order processing throughput
180ms
Product search (was 2.8s)
34%
Infrastructure cost reduction
.NET 8AngularAKSAzure Service BusRedisTerraformDockerGitHub ActionsPrometheus
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Case Study · 03 · AI & LLM Integration

AI-Powered Document Intelligence
for a Legal SaaS Platform

LegalTech · SaaS Platform · Remote Engagement
AI / LLM Production shipped
The Problem
A UK-based legal SaaS startup had lawyers manually reviewing 200+ page contracts to extract key clauses, obligations, and risk flags — taking 3–5 hours per document. They had experimented with GPT-4 via the OpenAI API in a Jupyter notebook, but couldn't figure out how to turn it into a production-grade feature with proper accuracy controls, audit trails, and the latency their UX required.
The Approach
I designed a RAG-based document intelligence pipeline using Azure OpenAI and Semantic Kernel, integrated into their existing .NET 8 backend. Documents are chunked, vectorised using Azure AI Search, and stored with metadata. Clause extraction uses a prompt chain with structured output validation — each extracted clause includes a source page reference and confidence score. A Cosmos DB audit log records every AI interaction for compliance. SignalR was used to stream extracted clauses in real-time so the UI felt instant even on 200-page documents.
The Outcome
Time per document review dropped from an average of 4.2 hours to 18 minutes — an 88% reduction. The feature shipped to production in 11 weeks. Clause extraction accuracy measured at 91.3% on the test corpus (validated by their senior legal team). Monthly AI infrastructure cost was budgeted and controlled at under $200 per 1,000 documents through aggressive caching and model selection optimisation.
88%
Review time reduction
91.3%
Clause extraction accuracy
11wk
Time to production
$200
AI cost per 1k docs / month
Azure OpenAISemantic KernelRAGAzure AI SearchCosmos DB.NET 8SignalRAzure Functions
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⚠ Replace these with your real project details before going live. Client names are anonymised above — add real names if you have permission.

What Clients Say

In Their Words

"

The architecture delivered wasn't just a solution — it was a foundation. Two years later our team is still extending it without any refactoring debt. That's what 20 years of experience looks like in code.

RK
Rajesh Kumar
CTO, FinTech Startup · Mumbai
"

We'd been told our legacy ASP.NET system couldn't be modernised without a full rewrite. Abstech proved otherwise — incremental migration, zero downtime, and we were on .NET 8 in four months.

SP
Sanjay Patel
VP Engineering · Pune
"

What stood out was the clarity of thinking. Every architectural decision came with a rationale, a trade-off analysis, and a documented alternative. Our board used the docs during investor due diligence.

AM
Anita Mehta
Founder & CEO · SaaS Platform

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