AI Systems Consulting
We work with technical founders and CTOs to design and build production-grade agentic systems — the ones that hold up when real users hit them.
Planner agents · Agentic RAG · Multi-agent systems · Production LLM pipelines
Where agentic systems break
The chunking strategy that scored well in evaluation falls apart when real users ask questions in their own language — not the language of your documents. You debug the embedding model. The problem is the retrieval design.
A LangChain flow that handled three tools cleanly now has retry logic, fallback prompts, and conditional branches scattered across eight files. Every new capability touches something that was already working.
Working memory fills up. Session state is lost between calls. The agent that impressed in the demo starts fresh with every interaction in production. Users notice immediately — even if they can't name why.
These aren't model problems. They're system design problems.
Work
agentic · news intelligence
End-to-end agentic pipeline ingesting, filtering, and publishing AI signal across 100+ sources. Zero manual steps.
Running in production
goassistant.in ↗multi-agent · semantic filtering · automated publishing
agentic · sales automation
Lead qualification and personalised follow-up agent integrated into CRM. Response time: hours → seconds.
60%
increase in walk-in conversions
conversational agent · CRM integration · lead scoring
agentic RAG · document intelligence
Agentic RAG pipeline extracting structured insights from complex documents at scale. Human-review triggers on low confidence.
90%
improvement in extraction accuracy
agentic RAG · intelligent parsing · confidence scoring
Anirudh operates at a level of clarity and execution that is rare, even among top engineering talent. He has the ability to think end-to-end — from first principles to production — while maintaining both speed and technical rigor. In environments where both correctness and execution matter, he is someone I would trust to build and deliver critical AI systems.
Dr. V. Ramgopal Rao
Former Director, IIT Delhi
Writing
Technical writing on the problems that come up when you try to ship agentic systems in production.
You tuned retrieval for two weeks. The demo impressed the team. You shipped it. Here is what actually happened when real users started asking questions.
It starts clean. A planner, three tools, a simple loop. Six months later you can't explain what it does without opening four files simultaneously.
Your agent forgets things it should remember. The fix is not a bigger context window — it is designing memory intentionally across four distinct layers.
Work together
A small number of custom engagements per quarter. Each one starts with a conversation.
AI System Sprint
2–4 weeks
Design and ship one production-ready agentic workflow end-to-end.
AI Strategy + Architecture
1–2 weeks
Map your highest-leverage AI opportunities and design the system.
AI Systems Partner
Ongoing
Embedded collaboration across your AI stack, long-term.
No pitch deck needed. Bring your architecture problem — we'll work through it.
Anirudh Voruganti
Founder, goBIGai
AI Systems Consulting
Anirudh is an AI engineer with deep experience across software engineering, data systems, and applied AI, shaped by work at Amazon and advanced research at IIT Bombay. Through goBIGai, he helps companies operationalize AI — designing robust, secure, and scalable agentic workflows that integrate cleanly into their products and operations.