Jnanik connects your factory documents, existing systems, and operational knowledge into a single intelligent agent — so every person on your floor gets the precise answer they need, instantly.
10K+
Docs
< 5s
Response
3
User Roles
Multi-role, multi-geography deployment. Strictest data confidentiality requirements met by design.
Silicon India — April 2026. Recognised for engineering sovereign agentic intelligence for the industrial future.
Enterprise AI engineers with 18 years at Bosch and 3 years at AWS — who've deployed, not just consulted.
Meet the team →You've run the pilot.
You got the dashboards.
The floor didn't change.
Most enterprise AI projects produce reports, not results. The problem isn't the model — it's everything that came before it.
The gap between a pilot and a production system isn't the model — it's every decision made before the first line of code.
Not a pilot. Not a lab prototype. Deployed across sales staff, field technicians, and distributor networks simultaneously — solving real problems in production.
Your data never leaves your environment. SLM-first, model-agnostic, open-source deployment. Own your intelligence — don't rent it.
Designed for the shop floor, the field technician, the sales engineer between meetings. Not a horizontal tool bent to fit your world.
“Founded by people who watched enterprise AI fail at scale — from the inside. Every architectural decision is shaped by that experience.”
2025
Founded
3
Capability layers
2
Deployment modes
2+
Industries served
Every person in your organisation gets the right answer, in their language, on any device — without waiting for the expert.
Learn more →Responses that know who's asking — structured for the technician, precise for the sales engineer, decision-ready for the manager.
Learn more →AI that triggers workflows, routes decisions, and coordinates across functions without a human coordinator in the middle.
Learn more →A question enters. The orchestrator decomposes it, routes across every source simultaneously, and returns a single cited answer.
“What is the torque spec for the GX-450 pump seal, and has it been updated since the last QC review?”
SLM-first · sovereign deployment · model-agnostic
Plans
Decomposes query into sub-tasks
Routes
Selects tools and sources
Executes
Parallel tool calls via MCP
Synthesises
Cites, ranks, and responds
Anthropic Model Context Protocol
MCP provides a universal interface — the orchestrator calls any tool without bespoke integration code.
On-prem · encrypted at rest
Your data never leaves your environment. Retrieval is context-aware — the right chunk, not just the nearest.
Pre-built connectors · custom adapters
The orchestrator calls your systems like tools — reading live data, writing back decisions, closing the loop.
Role-specific
Structured for a technician — not a manager report
Cited sources
Linked to SOP v12 §4.3 and QC log #2891
Audit trail
Every decision logged with timestamp and actor
Sovereign deployment · Your data never leaves · No frontier LLM dependency · Full audit trail
The Problem
Quality holds overridden under delivery pressure. No accountability trail. OEM complaints land on Quality Head for decisions they didn't make.
What Changes
Every quality decision — hold, override, escalation — documented and traceable in real time. Decisions are defensible without slowing production.
The Problem
Trade deduction leakage running into crores annually. Distributor queries on Excel and phone. No connection between scheme performance, orders, and production signals.
What Changes
Commercial intelligence in the hands of every distributor and sales manager, in real time. Leakage visible. Disputes resolved with data, not calls.
Every Jnanik engagement follows the same four phases — because we've seen what happens when teams skip them.
2–4 weeks
Map the operational landscape. Define where AI delivers measurable ROI first. Define success metrics before any build begins.
1–2 weeks
Surface and remediate data quality issues before deployment. Fragmented industrial data is the #1 reason AI projects fail — this is the insurance policy.
4–12 weeks
Sovereign, modular deployment — in your environment, on your infrastructure, on your terms. Human-in-the-loop validation throughout.
Ongoing
Expand across roles, functions, and geographies — with an auditable trail of every decision and outcomes benchmarked against the ROI metrics defined in Phase 01.
See the full methodology →“A methodology shaped by seeing where AI projects break — and building the safeguards before the first line of code.”
We come to where you are — cloud or on-premises — and deploy AI that your data never has to leave the building for.
Your AI runs in the cloud — we handle everything. We set up, manage, and scale your AI platform on your preferred cloud provider.
Your AI stays inside your building — your data never leaves. We deploy the same platform inside your own data centre.
“Slashed sales cycle times. Freed senior engineers from repetitive support queries. Solved the knowledge bottleneck across a global distributor network.”
Office-based sales staff · Distributor partners · Mobile field service technicians — simultaneously. Different access tiers per role. One deployment.
The customer CEO presented this at their own channel partner summit. That's not a customer tolerating a deployment — that's a customer proud of what it became.
A 30-minute conversation with a senior Jnanik engineer. We assess your data, workflows, and constraints — then tell you honestly what AI can and can't do for your operation.
No pitch. No commitment.