AI applied to a specific business problem
Not a chatbot on your homepage. Document processing, demand forecasting and support automation — measured against the manual process it replaces.
Where AI actually pays back
- Staff retype data from invoices, POs and delivery notes every day
- The same twenty customer questions are answered manually, all day
- Stock planning runs on instinct because demand patterns are never analysed
- Reports exist, but nobody has time to read them and spot what changed
- Quality inspection is manual and inconsistent between shifts
AI Automation — what you get
Document extraction
Invoices, POs and delivery notes read automatically into your system.
Support chatbot
Trained on your catalogue and policies, escalating when it should.
Sales assistant
Qualifies enquiries on WhatsApp and hands over warm leads.
Demand forecasting
Reorder suggestions based on actual patterns and seasonality.
Natural-language reporting
Ask a question in plain language, get the number.
Anomaly detection
Unusual transactions or consumption flagged as they happen.
Content automation
Product descriptions and listings generated at scale.
Voice & call analysis
Call transcription and summary into the CRM record.
What changes
- 1
Discovery
1–2We sit with the people who do the work — not only management. Most of the real requirements are in what they have quietly worked around for years.
- 2
Blueprint & estimate
1–2Scope, module list, integration points, timeline and fixed cost. You can take this document to another vendor — that is the point.
- 3
Design
2–3Clickable screens before development. Changing a screen at this stage costs an hour; changing it after build costs a week.
- 4
Development
4–16Two-week sprints with a working demo at the end of each one. You see progress continuously instead of waiting for a reveal.
- 5
Testing & UAT
2–3Your team uses it on real data before go-live. Every issue is logged and closed in writing.
Frequently asked questions
Is our data used to train public models?
No. We use API-based models where your data is not used for training, or self-hosted models when the data must not leave your infrastructure at all. Which approach applies is agreed before any work starts.
What if the AI gets something wrong?
Anything financial or irreversible goes through human confirmation. AI drafts, a person approves. Confidence thresholds route uncertain cases to a human automatically.
How do we know it is worth the cost?
We baseline the manual process first — hours spent, error rate, cost. If the automation cannot beat that measurably, we say so rather than build it.
Can it read documents in Hindi or regional languages?
Yes, including mixed-language and handwritten documents, though accuracy on handwriting varies and we test it on your actual documents before committing.
Start with the problem, not the software
A 45-minute call. We map where the manual work actually is and tell you whether it is worth building anything at all. No cost, no obligation.
If custom software is the wrong answer for you, we will say so on that call.