Work Assistance AI & GenAI On-Demand

AI & GenAI Work Assistance for Engineers

Your manager wants AI in the product by next quarter. You have built a prototype with the OpenAI API that works impressively in demos but falls apart with real user input. The LLM hallucinates facts, your RAG pipeline retrieves irrelevant documents, the API costs are projected to blow the budget at production scale, and your security team has concerns about sending customer data to third-party models. The gap between a working demo and a production-ready AI feature is wider than anyone on your team expected.

I help engineering teams cross that gap. As a software architect who has integrated AI capabilities into production systems, I understand both the AI side — model selection, prompt design, RAG architecture, fine-tuning decisions — and the engineering side — reliability, cost management, security, monitoring, and graceful degradation. I can help you make the right choices for your specific use case, avoid the expensive mistakes that come from treating AI as a black box, and build AI features that work reliably in production, not just in demos.

Get AI Guidance → View Pricing

📞 +91 83173 67002 💬 WhatsApp

🤯

LLM Responses Inconsistent or Hallucinating

The model gives a perfect answer 80% of the time and confidently makes up facts the other 20%. Users cannot tell the difference, and your team does not have a strategy for detecting or preventing hallucinations. Prompt engineering alone is not solving the problem.

🔍

RAG Pipeline Returning Irrelevant Results

You built a RAG system but it retrieves the wrong documents. The embeddings do not capture the semantic meaning your users expect, the chunking strategy splits context across chunks, and the retrieval ranking feels random. The LLM's answers are only as good as the context it receives.

💰

AI Integration Costs Exceeding Budget

Your prototype used GPT-4 for everything and now you are looking at a five-figure monthly API bill at production scale. You need to figure out which calls actually need the most capable model, which can use cheaper alternatives, and how to implement caching without serving stale responses.

🤔

Unclear Which AI Model to Use for Your Use Case

OpenAI, Anthropic, Google, open-source models — the options are overwhelming and changing monthly. Your use case needs specific capabilities around context length, structured output, or domain knowledge, and picking the wrong model means rebuilding your integration later.

🎯

Prompt Engineering Producing Unreliable Outputs

Your prompts work for the examples you tested but break with edge cases. The output format is inconsistent, the model ignores instructions when the context is long, and every prompt change improves one scenario while breaking another. You need a systematic approach, not trial and error.

🔒

AI Security and Data Privacy Concerns

Your legal team wants to know what data is sent to the model provider. Your security team is worried about prompt injection attacks. Your compliance team needs to understand data retention policies. The AI works, but it cannot go to production until these questions are answered.

Architecture & Design

We design the AI integration architecture — where the LLM fits in your system, how to structure the RAG pipeline, when to use embeddings vs fine-tuning, and how to handle failures gracefully so a slow or unavailable AI service does not break your entire application.

Model Selection & Evaluation

I help you evaluate models against your specific requirements — accuracy, latency, cost, context window, structured output support, and deployment options. We run targeted evaluations on your actual data, not benchmarks, to find the right model for your use case.

RAG Pipeline Optimization

Retrieval quality makes or breaks a RAG system. I help you improve chunking strategies, embedding model selection, retrieval ranking, re-ranking, and context window management to get relevant results consistently instead of hoping the right documents surface.

Production Readiness

Moving from prototype to production requires cost controls, monitoring, guardrails, and fallback strategies. I help you implement response validation, content filtering, cost tracking, latency budgets, and the operational infrastructure needed to run AI features reliably.

Build a Production-Ready RAG Pipeline for Internal Knowledge Base

Your company has thousands of documents — wikis, PDFs, Confluence pages, Slack threads — and you want employees to query them with natural language. We build a RAG pipeline that actually returns relevant answers, handles document updates, and scales with your knowledge base.

Design document ingestion and chunking strategy
Select and configure embedding model
Implement retrieval with re-ranking and filtering
Build answer generation with source citations

Evaluate and Select the Right LLM for Your Product

You need to choose between GPT-4, Claude, Gemini, Llama, and other models for your product feature. We define your evaluation criteria, run tests on your actual data, compare costs and latency, and make a recommendation grounded in your specific requirements.

Define evaluation criteria from product requirements
Build evaluation dataset from real user scenarios
Run comparative tests across candidate models
Analyze cost, latency, and quality trade-offs

Implement AI Guardrails and Content Moderation

Your AI feature needs to handle user input safely — no prompt injection, no harmful content generation, no data leakage. We implement input validation, output filtering, content moderation, and audit logging to make your AI integration production-safe.

Design input sanitization and prompt injection defense
Implement output validation and content filtering
Set up PII detection and data masking
Build audit logging for AI interactions

Optimize LLM Costs with Caching and Model Selection

Your AI API costs are unsustainable at production scale. We implement semantic caching, route simple requests to cheaper models, batch similar requests, and optimize prompts for token efficiency — often reducing costs by 60-80% without degrading quality.

Analyze current usage patterns and cost drivers
Implement semantic caching for repeated queries
Design model routing (expensive model for complex, cheap for simple)
Optimize prompts for token efficiency

Product Engineers Adding AI Features

You have been tasked with adding AI capabilities to your product — chatbots, content generation, document analysis, or intelligent search. You need guidance on the architecture and implementation to do it right the first time.

Teams with AI Prototypes Ready for Production

Your demo impressed leadership but now you need to make it production-ready. That means handling edge cases, managing costs, implementing guardrails, and ensuring the AI feature does not degrade the reliability of your core product.

Engineering Leaders Evaluating AI Strategy

Your organization wants to adopt AI but you need a clear-eyed assessment of where AI adds genuine value versus where it adds complexity without benefit. You need an architect's perspective, not a vendor's sales pitch.

Developers Learning AI Integration

You are a strong engineer but AI is new territory. You need hands-on guidance from someone who understands both software engineering and AI — not a machine learning researcher, but an architect who builds AI-powered products.

View Pricing & Packages →

AI & GenAI Work Assistance for Engineers

GenAI problems that block production readiness

LLM Responses Inconsistent or Hallucinating

RAG Pipeline Returning Irrelevant Results

AI Integration Costs Exceeding Budget

Unclear Which AI Model to Use for Your Use Case

Prompt Engineering Producing Unreliable Outputs

AI Security and Data Privacy Concerns

AI guidance from an architect who builds production AI systems

Architecture & Design

Model Selection & Evaluation

RAG Pipeline Optimization

Production Readiness

AI challenges I help engineering teams solve

Build a Production-Ready RAG Pipeline for Internal Knowledge Base

Evaluate and Select the Right LLM for Your Product

Implement AI Guardrails and Content Moderation

Optimize LLM Costs with Caching and Model Selection

Engineers building AI features that need to work in production

Product Engineers Adding AI Features

Teams with AI Prototypes Ready for Production

Engineering Leaders Evaluating AI Strategy

Developers Learning AI Integration

Expert AI guidance, from quick questions to full integration support

Related Training & Services

Tell us about your AI challenge

AI & GenAI Work Assistance for Engineers

GenAI problems that block production readiness

LLM Responses Inconsistent or Hallucinating

RAG Pipeline Returning Irrelevant Results

AI Integration Costs Exceeding Budget

Unclear Which AI Model to Use for Your Use Case

Prompt Engineering Producing Unreliable Outputs

AI Security and Data Privacy Concerns

AI guidance from an architect who builds production AI systems

Architecture & Design

Model Selection & Evaluation

RAG Pipeline Optimization

Production Readiness

AI challenges I help engineering teams solve

Build a Production-Ready RAG Pipeline for Internal Knowledge Base

Evaluate and Select the Right LLM for Your Product

Implement AI Guardrails and Content Moderation

Optimize LLM Costs with Caching and Model Selection

Engineers building AI features that need to work in production

Product Engineers Adding AI Features

Teams with AI Prototypes Ready for Production

Engineering Leaders Evaluating AI Strategy

Developers Learning AI Integration

Expert AI guidance, from quick questions to full integration support

Related Training & Services

GenAI Engineering Training

AI Tools for Software Engineers

Python Full-Stack Training

System Design Guidance

Software Architecture Review

Tell us about your AI challenge