About Jnanik AI

Built from the shop floor up.

Jnanik is rooted in Jnana — the Sanskrit principle of knowledge through direct experience. Not theory. Not abstraction. Knowledge earned by understanding the problem from within.

We founded Jnanik AI because we watched a pattern repeat itself: intelligent teams, complex operations, and AI systems that simply couldn't understand the context in which they were being asked to work.

A factory floor is not the internet. A quality exception in an automotive line is not a customer service ticket. The knowledge that runs a manufacturing plant — the hard-won expertise of engineers who've spent years tuning, calibrating, and solving — doesn't live in a PDF. It lives in people.

We started Jnanik AI to build AI that understands where it's being deployed. Not generic tools configured for a use case, but systems designed from the ground up around the realities of Manufacturing, FMCG, and Automotive operations — where precision is non-negotiable and the cost of failure is real.

Jnanik AI is the knowledge that industry needs. Built by the engineers industry deserves.

2025

Founded

2+

Industries

2

Deployment Modes

Ex-AWS

Cloud Architecture

Distributed systems at hyperscaler scale — reliability and security built in from day one.

Ex-Bosch

Industrial Engineering

Production software for environments where failure is measured in downtime, not bugs.

Open-Source First

No Vendor Lock-in

Everything we build runs on open foundations. You own your models, your data, your infrastructure.

Our Vision

Every organisation — from a regional manufacturer to a global enterprise — has access to AI that works reliably, protects their data, and creates measurable business value.

Our Mission

Close the gap between AI potential and enterprise reality. We build production-grade systems that solve specific, high-value problems — not generic tools or proof-of-concept demos.

Our Approach

We start with constraints — your data, your team, your risk tolerance. We design the simplest system that solves the problem. We ship it, measure it, and improve it.

The Founders

PK

Pramod Kumar P

Business & Strategy | Founder

Most enterprise AI pilots fail before a single line of agent code is written. The root cause is almost always the same — the data was never ready to be reasoned over.

18 years at Bosch in IoT and telematics, then 3 years at AWS on cloud and GenAI strategy. Both taught me the same thing: the gap between a demo and a production system is an engineering discipline problem, not a model problem.

Background

  • Ex-Amazon Web Services · 3 yrs
  • Ex-Bosch · 18 yrs
  • AWS Certified Solutions Architect – Professional
1.
Enterprise domain fluencyTwo decades across Bosch and AWS — I understand how enterprise procurement, buy-in, and technical decisions actually work.
2.
Industrial operations depthFactory SOPs, IoT pipelines, automotive-grade telematics. I understand the environment our AI must operate inside.
3.
Phase 0 data readinessOur pre-build audit framework catches the failure pattern before we write a single line of agent code. Weeks added upfront — quarters saved downstream.
4.
Commercial architectureFrom first conversation to signed engagement to production system — I own the full path.
LinkedIn →
PR

Puneeth Reddy

Technology & Execution | AI Advisor — Technical Architecture

A great AI system is defined by what it does when the edge case arrives at 2 AM — not by how smoothly the demo ran in the boardroom.

15 years building production systems at Bosch — 8 as Senior Software Engineer in Bengaluru, 7 as Software Architect in Stuttgart. M.S. AI from Georgia Tech.

Background

  • Software Architect — Bosch Stuttgart · 7 yrs
  • M.S. AI — Georgia Institute of Technology
  • 15+ Years Production Systems Engineering
1.
Full-stack AI architectureFrom data ingestion to retrieval pipelines, agent orchestration, model serving, and the API surface that connects it to your existing systems.
2.
Production-grade defaults15 years at Bosch means reliability and zero tolerance for systems that pass QA but fail in field conditions.
3.
Georgia Tech AI depthFormal ML training on top of architectural experience — decisions that hold up theoretically and in production.
4.
Scalability and sovereigntyMulti-tenant deployments, on-prem constraints, data residency, compliance audit trails — enterprise-ready from day one.
LinkedIn →

Company Timeline

August 2025

Bootstrapped in Bengaluru

Started with one conviction: enterprise AI should be private, practical, and built to last — not a flashy demo that never ships.

January 2026

First Production Deployment

Shipped our first on-premise AI Knowledge Hub for a mid-market manufacturer. 50,000+ documents. Answers in seconds instead of hours.

March 2026

Agentic Systems Live

Launched our first multi-step agentic workflow — handling document routing, classification, and approval without human intervention.

May 2026

SLM-First Architecture

Moved fully to Small Language Models. Domain-specific models outperforming general LLMs at 80% lower cost.

July 2026

Expanding Across Industries

Now supporting enterprise AI programs across Manufacturing, Automotive, Publishing and FMCG.

Our Principles

01

We build, not just advise

Engineers who deliver working systems, not consultants who hand over slide decks and leave.

02

Outcomes over technology

Every project is measured by the business result, not the technical sophistication.

03

Honest about what works

We'll tell you when AI isn't the right answer. That's how we build trust.

04

Simple before complex

The best AI system is often the simplest one that solves the problem reliably.

05

Your team's success is ours

We work alongside your people, transfer knowledge, and make sure the system lives on after we leave.

06

Open and portable by default

Open-source foundations. No vendor lock-in. You own everything we build.

Talk to our engineers. Not our sales team.

60 minutes with a senior Jnanik engineer. We assess your data, workflows, and constraints — then tell you exactly what AI can and can't do for your business. No pitch. No commitment.