AI native engineering
Adopt AI in your operations, inside the systems you already run
Ajinkya Technologies is an AI native engineering company. We help manufacturers and enterprises find the AI use cases worth doing, build them on your own data, and integrate them into existing MES, ERP, maintenance and document systems, with human approval where it matters. Most projects start with one use case and one number to move.
Free scoping call. We start with one line, never change your machine logic, and you own the code and the data.
- material movements tracked a day, integrated steel plant
- 720,000+
- fewer dispatch errors with RFID and barcode dispatch
- 90%
- items an hour through RFID warehouse automation
- 16,800
Company reported project figures, not independently audited, not a promise for another plant. Samsung Business Insights: our RFID warehouse automation story (opens in a new tab). Forbes India Brand Connect (sponsored): founder profile (opens in a new tab).
Sound familiar?
- Leadership wants an AI strategy, and nobody can name the first use case.
- A chatbot pilot impressed in a demo and never reached the floor.
- Engineers spend hours searching manuals, SOPs and old reports for answers.
- IT is worried about data leaving the company through AI tools staff already use.
Every month without a working use case is spend on AI tools without a return.
What you get
A ranked use case list
By value and data readiness, agreed with operations and IT.
One AI use case live
Integrated into the real workflow, not a separate demo.
Guardrails
Permissions, approvals, logging and data handling your IT can sign off.
A foundation to reuse
Data connections and patterns the next use case builds on.
How we work
How an AI engagement starts
- 1Use case workshop. Two to three hours with operations and IT to rank use cases by value and data readiness.
- 2Data check. Confirm the data exists, is accessible and is good enough.
- 3Pilot. One use case, integrated into the real workflow, with a measure agreed up front.
- 4Scale. Harden, monitor and extend to the next use case on the same foundation.
How it works
How we integrate AI into existing infrastructure
- Connect to the data you already have: MES, ERP, historians, document stores, through their standard interfaces.
- Choose the model for the job: hosted models such as Claude, GPT or Gemini, or open models run on your infrastructure.
- Keep humans in the loop: suggestions first, automatic actions only after the team trusts them, with every action logged.
- Secure by design: role based access, no training on your data without consent, and deployment where your policy requires.
AI agents, in brief
An AI agent is software that can plan and carry out a sequence of steps toward a goal, using tools you give it, such as reading the maintenance schedule, checking spare stock and drafting a work order. We build agents for maintenance, quality, procurement, scheduling and safety, each with clear limits on what it may do on its own.
Background
Where AI pays back in operations
| Use case | What AI does | Typical systems involved |
|---|---|---|
| Quality inspection | Computer vision flags defects inline | Cameras, MES, quality records |
| Predictive maintenance | Models project failures from sensor data | Sensors, historian, CMMS |
| Operations copilots | Answer questions over SOPs, manuals and plant data | Document stores, MES, ERP |
| AI agents | Carry out multi step tasks such as raising a work order or chasing a delayed PO | MES, ERP, email, ticketing |
| Document automation | Read invoices, test certificates and delivery notes into the ERP | ERP, email, scanners |
| Forecasting | Demand, spares and energy forecasts | ERP, MES, meters |
When we are not the right choice
- You want a generic chatbot on your website. Off the shelf tools are cheaper.
- There is no process owner who will use the result.
Common concerns, answered
Will this disrupt production?
No. We connect read only first, install during planned stops, and edge devices buffer data so a network problem never stops a machine. Nothing writes to a PLC without your controls team and a test.
Our machines are old. Do we need to replace them?
No. Old machines are the usual case. A running signal, a cycle signal or a current sensor on the motor is enough to start, without touching the machine program.
We tried a system before and nobody used it.
That is the most common failure, so we design with the operators and supervisors who will use it, start with one report they already fill in by hand, and only extend once they rely on it.
What about IT security?
Read only by default, a segmented OT network, and a review with your IT team before anything connects, following the ISA/IEC 62443 zone approach.
We will not replace SAP or Oracle. Do we have to?
No. We connect to the ERP you already run through its standard interfaces and add what the shop floor is missing. Your ERP stays the system of record.
Do your engineers understand a heavy industrial floor?
Our systems run in steel plants, warehouses and process lines. We start every project by walking the line with your operators and supervisors, and the first release is tested by them, not only by IT.
Are we locked in?
No. You own the source code and the data, documented, in your own repository. Another team can maintain it.
Frequently asked questions
What does AI native mean for a services company?
It means AI is part of how we design and deliver every system, not an add on: we look for where AI removes manual work or improves a decision, and we use AI in our own engineering.
Can AI work with our old systems?
Usually yes. AI connects through the same interfaces as any integration: APIs, databases, files or OPC UA for plant data. Older systems sometimes need a small integration layer first.
Will our data be used to train public models?
Not with our designs. We configure providers so your data is not used for training, or run models inside your own infrastructure when policy requires it.
How do you stop an AI agent doing something wrong?
Defined permissions, human approval for consequential actions, full logging and the ability to switch automatic actions off. Agents start in suggestion mode.
Related reading
References
Start with one line and one number
We scope one line, agree the number that should move, and show it moving before anything is scaled. You keep the code and the data.
Step 1
An engineer reads it
Not a sales bot. Our team replies within 24 hours with questions or times for a call.
Step 2
30 minute call
Your process, machines, systems and the number you want to move. We tell you honestly if we are a fit.
Step 3
A written scope for one line
What we would connect, what you would see, how long it takes. You decide; nothing starts without your approval.
Reviewed by Amey Kadle, Founder, Ajinkya Technologies. Last reviewed: .