AI Systems Consulting
We work with technical founders and CTOs to build agentic systems that hold up when real users hit them, not just in the demo.
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.
Anirudh is unusually clear in how he thinks and how he ships. He can work from first principles to production without trading speed for rigor, or rigor for speed. When both correctness and execution matter, I would trust him to build and deliver critical AI systems.
Dr. V. Ramgopal Rao
Former Director, IIT Delhi
Work
agentic · ecommerce assistant
Conversational shopping assistant that understands how customers describe products, then recommends the right items directly on store pages.
Founding 15 onboarding
goassistant.in ↗RAG · vector search · multilingual · Shopify/WooCommerce
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
Writing
Technical writing on the problems that come up when you try to ship agentic systems in production.
Why FluidCloud rebuilt its multi-cloud migration engine without AI — deleting four months of LLM-based mapping for deterministic rules. A field guide to where LLMs belong in infrastructure.
How Signalbase runs a production signal pipeline: waterfall verification, confidence gates, feedback loops, and where human judgment still bottlenecks GTM execution.
You tuned retrieval for two weeks. The demo impressed the team. You shipped it. Three weeks later, users were getting answers that had nothing to do with their questions.
It starts clean: a planner, three tools, a simple loop. Six months later you need four files open to explain what it does.
Your agent forgets what it should remember. The fix is not a bigger context window. You need four memory layers, each doing a different job.
Why FluidCloud rebuilt its multi-cloud migration engine without AI — deleting four months of LLM-based mapping for deterministic rules. A field guide to where LLMs belong in infrastructure.
How Signalbase runs a production signal pipeline: waterfall verification, confidence gates, feedback loops, and where human judgment still bottlenecks GTM execution.
You tuned retrieval for two weeks. The demo impressed the team. You shipped it. Three weeks later, users were getting answers that had nothing to do with their questions.
It starts clean: a planner, three tools, a simple loop. Six months later you need four files open to explain what it does.
Your agent forgets what it should remember. The fix is not a bigger context window. You need four memory layers, each doing a different job.
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 builds agentic systems for production. His background is applied AI, software engineering, and data systems. Work at Amazon and advanced research at IIT Bombay shaped how he thinks about systems at scale. Through goBIGai he helps teams ship agentic workflows inside their products and operations.