From reactive to intelligent
Products that reason over user context — planning across data sources, understanding intent, and taking action — create experiences that static products cannot replicate.
Give your product the ability
to reason, plan, and act.
goBIGai designs and deploys agentic workflow systems that power intelligent product experiences — where your product thinks across your user's data, orchestrates decisions, and delivers outcomes on their behalf.
Built for funded startups, scale-ups, and data-rich teams ready to embed AI into the core of their product.
Planner Agents · Agentic RAG · Multi-agent Orchestration · Tool-use Pipelines · Production LLM Systems
Bring your product use case. We'll design the agentic system around it.
The Opportunity
Your product has data.
Your users have goals.
An intelligent system connects them.
That connection — the reasoning layer between your data and your user's intent — is what separates products that compound from products that plateau.
Orchestration is the architecture
A real agentic system has a planner, memory, tool-use, retrieval, and execution layers working in concert. That architecture is what makes AI a product capability, not a feature.
AI that acts, not just responds
The highest-leverage AI systems don't wait for perfect inputs. They move across your product's data, make decisions, and deliver outcomes on behalf of your user.
What this looks like in practice
You've already felt what this is.
When Cursor launched, developers didn't say “nice autocomplete.”
They said: this thing understands my entire codebase.
Because Cursor isn't a code editor with AI added. It's a planner agent — it reads your repo, understands intent, reasons across files, and executes changes end-to-end. One instruction. Multi-step action.
That architecture — planner, context retrieval, multi-step reasoning, execution — is what separates products that feel intelligent from products that have AI features.
We build that architecture.
For your product. In production.
User Goal↓
Planner Agent↓
Agentic RAG↓
Tool Use↓
Memory / Context↓
Execution Layer↓
Product Output
Who this is for
Built for teams serious about
embedding AI into their product.
Funded startups building AI-native products where intelligence is a core product capability
Scale-ups integrating AI into product workflows to improve user experience and retention
Data-rich, ops-heavy teams with the data assets to power meaningful agentic systems
Teams that have prototyped AI features and are ready to build them into production systems
Operators who want AI embedded in their product — not added on top of it
If this describes your team — let's talk.
What we build
Production agentic systems —
designed to power your product.
Every engagement delivers a structured AI system with orchestration, memory, tools, and observability built in from day one.
Core system
Agentic Workflow Systems
End-to-end multi-step workflows combining planning, tool use, memory, and execution. Built for processes that need to run reliably — in your product or behind it.
→ Scalable intelligence with zero manual touchpoints
Intelligence layer
Agentic RAG Systems
Not just retrieval — reasoning over your product's data. Planner agents that decide what to retrieve, from where, in what sequence, and how to synthesise it into the action your user actually needs.
→ Your product reasons over context like a domain expert
Product layer
Embedded AI Agents
Agents built into your product's core flows. They plan, retrieve, decide, and act on behalf of your user — integrated into your existing product architecture.
→ Product experiences that feel like an expert working for every user
Operations layer
AI Automation Systems
Automate your highest-volume operational workflows — support triage, internal processing, lead qualification, document routing. Built to run continuously and reliably.
→ Meaningful reduction in manual ops load, with full observability
Infrastructure layer
LLM Application Architecture
Design and build the full LLM stack — prompt management, evaluation harnesses, cost monitoring, and production deployment pipelines.
→ Reliable, observable, cost-controlled AI infrastructure
Case Studies
Systems we've built.
Results that compound.
Live production system
AI News Intelligence Agent
goassistant.inContext: A media product tracking meaningful AI developments across 100+ sources daily.
Problem: Manual curation was a full-time job. Signal-to-noise ratio was unsustainable. Publishing was inconsistent.
System: Fully automated agentic pipeline — ingestion, semantic filtering, relevance ranking, and publishing. Planner agent orchestrating every step end-to-end.
Result: 100% automated. Zero manual curation time. Consistent daily output with measurable relevance scoring. Runs continuously in production.
AI sales automation
AI Sales Agent — Fitness Studio
Context: Local operator with strong ad spend but low inbound-to-walk-in conversion. High lead volume, slow manual follow-up.
Problem: Leads were falling through the cracks. Follow-up was inconsistent and hours behind the moment of user intent.
System: AI sales agent handling lead qualification, personalised follow-up sequencing, and appointment nudges — integrated into existing CRM workflow.
Result: 60% increase in walk-in conversions. Response time dropped from hours to seconds. Sales team refocused on in-studio conversion.
RAG + document intelligence
Document Intelligence System
Context: Professional services team processing dense, inconsistently formatted documents at scale.
Problem: Extracting structured insights manually was slow, error-prone, and bottlenecking downstream decisions.
System: Agentic RAG pipeline with intelligent parsing, structured extraction, confidence scoring, and human-review triggers for edge cases.
Result: 90% improvement in relevant content extraction accuracy. Review cycles cut significantly. Analysts redirected to higher-value work.
Every system is designed for one outcome: measurable impact in a real operating environment.
Our methodology
How we go from use case to production.
Step 1
System-first architecture
We start with workflow design — not prompts. Every decision point, tool call, memory requirement, and failure mode is mapped before a line of code is written.
Step 2
Agentic workflow construction
We build multi-step reasoning chains with tool use, memory layers, and planner logic — designed for the edge cases your real data will surface.
Step 3
Production hardening
Monitoring, fallback logic, cost controls, and observability are instrumented before go-live — as first-class requirements, not afterthoughts.
Step 4
Iterative deployment
We ship to real environments fast and improve with live feedback. Working software in days, not months.
The difference between a production AI system and a prototype is in how it was designed from the start.
Trusted by
What a pioneer of Indian deep tech
says about our work.
“Anirudh operates at a level of clarity and execution that is rare, even among top engineering talent. He brings a strong foundation in machine learning, but more importantly, he has the ability to design and deliver systems that solve real-world problems.
What distinguishes him is his 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
Deep Tech Founder & Researcher
🏆 Shanti Swarup Bhatnagar Prize🏆 Infosys Prize🎓 Former Director, IIT Delhi
Ways to work together
Three ways to engage — depending on where you are.
AI Strategy + Architecture
1–2 weeks
For: Teams mapping where agentic AI creates the most leverage in their product
Deliverable: Complete AI opportunity map, system architecture blueprint, and prioritised build roadmap. A plan you can execute — with or without us.
Book a Strategy Call →AI System Sprint
2–4 weeks
For: Teams with a defined use case ready to build
Deliverable: One production-ready agentic workflow or embedded AI agent — fully deployed, monitored, and documented. From scoping to go-live.
Start with a Strategy Call →AI Systems Partner
Ongoing
For: Teams building AI as a core product infrastructure layer over time
Deliverable: Embedded collaboration on architecture, build, deployment, and iteration across your AI stack. Your AI systems team — without the full-time hire.
Let's talk scope →Currently accepting a limited number of new engagements. Strategy calls are the first step.
About
Built by an engineer who has spent years
closing the gap between AI prototypes
and production systems.
I'm Anirudh Voruganti — AI systems engineer and founder of goBIGai.
My focus has always been the same problem: the gap between a promising AI prototype and a system that actually powers a product in the real world.
That gap — edge cases, cost overruns, lack of observability, no fallback, no compounding value — is where most AI investments stall.
I build agentic systems designed from day one for production. Planner architectures, agentic RAG, tool-use pipelines, memory layers, and real-world execution loops built to run and improve over time.
Through goBIGai, I work with funded startups and scale-ups who are ready to embed AI as a core product capability — and want it built right.
Ready to build the intelligence layer
of your product?
One 30-minute call. Bring your use case. We'll tell you exactly what's possible, what it takes, and whether we're the right fit.
Book a 30-min Strategy Call →✓ No pitch deck required✓ Specific, actionable advice in the first call✓ We'll tell you honestly if it's not a fit