Generative AI · Production-Grade

Generative AI That
Works in Production

Enterprise AI implementations are failing at a 75% rate globally (McKinsey, 2025). The gap isn't the AI — it's the integration. Win Infosoft builds generative AI systems that are connected to real data, tested against real workflows, and measured by real business outcomes.

15+
AI Implementations
GPT-4
Gemini · LLaMA Support
72 hrs
Proof of Concept
ISO 9001
Certified Delivery

What We Build

Generative AI Services

Six practice areas, each delivered with full source code ownership and no dependency on any single model vendor.

Custom LLM Development

Fine-tune or build LLMs on proprietary enterprise data. Models stay on your infrastructure, not a third-party cloud. Your data doesn't leave your environment at any point during training or inference.

RAG Pipeline Architecture

Retrieval-augmented generation that pulls from your documents, databases, and knowledge bases in real time. Answers grounded in your actual content — not a model's training data from two years ago.

AI Agent Development

Multi-step AI agents that operate across CRM, ERP, and ticketing systems without human handholding. Agents that can read a support ticket, look up order history, draft a response, and route for approval — automatically.

AI Integration Services

Drop generative AI capabilities into existing applications via APIs. No full rebuild required. Teams can add summarization, classification, or generation features to current tools in weeks, not quarters.

Prompt Engineering & Optimization

Systematic prompt design that increases output quality and reduces token waste by up to 40%. This isn't trial-and-error — it's a structured process with evaluation sets, regression testing, and version control.

AI Audit & Governance

Review existing AI deployments for hallucination risk, data leakage, and compliance gaps. Delivered as a written report with remediation steps — not just a list of findings with no path forward.

The Process

From Discovery to Production

A fixed process that removes ambiguity and gives teams a clear picture of timeline and deliverables before any code is written.

1
Day 1–2

Discovery Call

Map current workflows and identify the highest-ROI AI entry points. Output: a prioritized shortlist of use cases with effort estimates.

2
Day 3–5

Proof of Concept

Build a working demo against your actual data. Validate the use case before committing to a full build — 72 hours, not 12 weeks.

3
Week 2–8

Production Build

Full integration with testing, monitoring, and rollback plans. CMMI Level 3 process discipline at every stage. No surprises at delivery.

4
Ongoing

Ongoing Support

Model retraining, performance monitoring, and quarterly reviews. Models drift over time — this keeps them accurate.

Why Win Infosoft

What Makes the Difference

Most AI vendors sell capability. What matters is whether a system holds up six months after launch. These are the structural reasons Win Infosoft's implementations do.

Based in Noida, India

Delivery teams that understand Indian enterprise data structures, compliance requirements, and the operational realities of working within existing IT stacks.

Multi-Cloud Capability

Certified across AWS Bedrock, Azure OpenAI, and Google Vertex AI. No vendor lock-in — the right platform is chosen based on your existing infrastructure and cost model.

CMMI Level 3 Process Discipline

Applied to every AI project — not just the traditional development work. Documentation, change management, and testing standards that enterprise procurement teams expect.

Full Source Code Ownership

Source code is transferred to the client on delivery. No ongoing licence fees for the software itself, and no dependency on Win Infosoft to keep the system running.

Model Support

Works With Every Major Platform

GPT-4 / GPT-4o
Claude (Anthropic)
Google Gemini
LLaMA 3 / Meta AI
Mistral / Mixtral
Microsoft Phi-3
AWS Bedrock
Azure OpenAI

Model selection is based on cost, data sensitivity, and task performance — not vendor preference.

Common Questions

Generative AI — FAQ

How long does a generative AI implementation take?

Most production-ready implementations take 6–12 weeks, including integration testing. A proof of concept can be ready in 72 hours — enough to validate the use case and see real output quality before committing to a full build.

Can you work with our existing data and systems?

Win Infosoft's team builds RAG pipelines that connect directly to your databases, SharePoint, CRM, and document stores. No data migration required. The system reads from where your data already lives and returns answers grounded in that content.

What does a generative AI project cost?

Project cost depends on complexity, data volume, and integration depth. Discovery engagements start at ₹2 lakhs. Contact the team for a scoped estimate based on your specific requirements — the discovery call itself is free.

Do you support open-source LLMs or only commercial models?

Both. Win Infosoft has experience with GPT-4, Claude, Gemini, and open-source models including LLaMA, Mistral, and Phi-3. The model is chosen based on cost, data sensitivity, and performance requirements — not a preferred vendor relationship.

Get Started

Ready to put AI into your
actual workflow?

Schedule a free 45-minute discovery call. No pitch deck — just a direct conversation about what's feasible, what it costs, and what you'll get.

Book Discovery Call

ISO 9001 certified · CMMI Level 3 · Noida, India