What we build
- Process automation: order entry from emails and PDFs, invoice matching, report generation, reminders and approvals, connecting the systems you already have.
- Document extraction: reading invoices, purchase orders, KYC documents, lab reports and handwritten forms into structured data, with a human check where confidence is low.
- Forecasting and analytics: demand, stock, cash-flow and attendance forecasts from your sales and ERP history, presented on dashboards your managers actually open.
- Chatbots and WhatsApp assistants: customer support, order status, fee reminders, appointment booking and lead qualification in English, Hindi and Marathi, with hand-off to a person.
- Language and vision models applied to your work: classifying support tickets, summarising call notes, counting items on a camera feed, checking labels on a production line.
- Data foundations: the pipelines, warehouses and clean-up that make the rest possible.
Data readiness comes first
AI projects fail on data more often than on models. Before we build anything we look at what you actually have: how many months of history, how consistent the fields are, whether the same product has three names, whether the data is in Tally, Excel, a legacy database or on paper. If the data is not ready, the first phase is making it ready, and we say so rather than promising a model that will not work.
Privacy and control
Business data, customer data and health records stay under your control. We prefer models that run on your own servers or in your cloud account in India, and where a hosted model is the right choice we agree in writing what is sent, what is retained and for how long. Personal data is minimised and masked before it reaches any model. Our own product RightYantra runs 116 tools entirely in the browser without uploading files, which reflects how we think about data.
How a project runs
AI work follows our eight-step process, with two differences. Requirements includes a data audit. Planning includes a short proof of concept on a sample of your real data, priced separately, so you see accuracy numbers before committing to the full build.
Then Design, Development and Testing proceed as for any software, with accuracy tracked against an agreed threshold. Deployment includes monitoring for drift. Training teaches your staff to review and correct the system's output. Support includes periodic retraining as your business changes.
What it costs
As a rough guide for AI and automation work in India:
| Work | Indicative range |
|---|---|
| Proof of concept on your data | ₹50,000 to ₹2 lakh |
| WhatsApp or web assistant with hand-off | ₹1.5 to 5 lakh |
| Document extraction pipeline | ₹2 to 8 lakh |
| Forecasting model with dashboard | ₹3 to 10 lakh |
The real figure depends on scope: data volume and cleanliness, the number of systems to connect, the accuracy required and whether models run on your infrastructure. Model API and hosting costs are separate and are estimated per month before you commit. More in AI automation for small businesses in India.
Why a company in Gondia and Nagpur
Automation lives or dies on the details of how work actually happens, and those details are easier to see in person. For clients in Vidarbha we sit with the accounts clerk, the dispatch desk or the front office to see the real workflow. We work Monday to Saturday, 9 am to 7 pm IST.
Clients elsewhere get the same process remotely. The analytics module of our own suite, InsightRight in RightSutra, is built on the same reporting foundations we use for client dashboards.
What to ask before you buy AI
- What accuracy will it reach on my data, and how will that be measured before I pay for the full build?
- What happens when the model is unsure: does a person check it, or does the error flow through?
- Where does my data go, who can see it and how long is it kept?
- What does it cost per month to run, not only to build?
- Would a simpler rule, report or integration solve most of this?
We cover the business case in AI chatbots for customer service: the ROI and Workflow automation to boost productivity.
Related work
- Smart agriculture system. Farmers needed crop monitoring, automated irrigation and weather prediction. We built an IoT sensor network feeding machine-learning crop analytics and a web dashboard, on Python, Arduino, LoRaWAN, machine learning and React.
- Retail point of sale. A local retail chain needed billing, inventory and live sales numbers in one system. We built a POS with inventory tracking, sales analytics and card payments on React, Node.js, PostgreSQL, Redis and the Stripe API. Sales analytics of this kind is the usual starting point for forecasting.
More on the portfolio.
Start a project
Tell us the task that eats the most manual hours, or the question you wish your data could answer, and what data you have. We will reply with a data audit plan and a proof-of-concept price. Contact us, call +91 82630 93672 or write to info@sailright.tech.
