Our approach to AI
AI projects that ship, get used and pay back.
Most AI projects stall at the demo. Ours are designed from day one to run inside your daily work, with your data kept private and your people in control.
What we believe
Six principles behind every AI project.
Problem first, model second
We start from the hours and errors in your process, then choose the simplest AI that removes them.
Data before AI
Clean, connected data matters more than clever models. That’s why we set up the ERP and data flows first.
People stay in control
AI drafts, people approve. Clear checks, confidence thresholds and easy overrides are built in.
Measured from day one
We agree the numbers that matter before we start, and report them after go-live.
Private and model-agnostic
We pick the right model for each job, cloud or private, and your data is never used to train public models.
Built to be run
Monitoring, tuning and support are part of the plan, because AI needs looking after.
Our method
Five steps from idea to AI in daily use.
- 01
Discover
We map your workflows and find where AI saves the most time.
- 02
Prioritise
Each idea is scored on value, effort, data readiness and risk.
- 03
Pilot
A small build on your real data, used by real staff, proves it works.
- 04
Build and integrate
We connect AI to your ERP, Tally, WhatsApp and apps, and train your team.
- 05
Run and improve
We monitor accuracy, handle exceptions and keep improving.
What we build
Applied AI, not science projects.
- AI agents that handle customer, dealer and supplier conversations on WhatsApp and the web.
- Document AI that reads purchase orders, invoices and delivery notes into your ERP.
- Knowledge assistants that answer staff questions from your own manuals, policies and price lists.
- Forecasting for demand, stock and cash, built on your own sales history.
- AI inside mobile apps: voice order entry, document capture and smart suggestions.
- Workflow automation that connects your ERP, Tally, email and messaging tools.
Responsible AI
Safe, private and explainable by design.
- We sign an NDA before we see any of your data.
- Your data is not used to train public AI models.
- Private or on-premise hosting where your data must stay in-house.
- Role-based access and a log of what the AI did and why.
- A person approves anything the AI isn’t confident about.
Who leads the work
Digant Bhatt leads every AI engagement.
Elinsys has been my passion, my dream. Over the past decade Elinsys has seen rapid growth and we have successfully delivered technology projects and provided creative solutions to a global customer base.
Digant Bhatt
CEO & Founder
Digant has spent 25+ years in IT, from software delivery to building AI products. Since 2024, Digant has led Elinsys’s research and development in applied AI and personally shapes the AI approach on every client engagement.
Questions
What owners ask us about AI.
Will AI replace my staff?
It takes over repetitive work like retyping orders and answering status calls, so your team can spend time on customers and production.
Is our data used to train AI models?
No. We use business-grade services and private options that don’t train on your data.
What if the AI gets something wrong?
Every automation has a human check for low-confidence cases, and we monitor accuracy after go-live.
Do we need an ERP before we can use AI?
Not always. Some automations work on top of Tally and Excel. The audit tells you which.
How quickly will we see results?
The pilot is designed to show value on your own data before you commit to a full build.
Find out where AI pays back first in your business.
The audit gives you a ranked list of AI opportunities, each with estimated savings and effort.
Book an AI Audit