NEWNew: MnT AI Desk · MnT AI CRM · MnT Commerce India
MnT Future
AI automation

AI automation for business workflows

The work your team repeats every single day, automated: document handling, data entry, classification, routing and reporting. With a person still in the loop wherever being wrong is expensive.

01Document processing
02Classification & routing
03Human in the loop
04Measured, not assumed

Start where the work is boring and the volume is high.

The best automation candidates are not the exciting ones. They are the tasks somebody does forty times a day, where the rules are mostly consistent, and where being wrong occasionally is recoverable. Those pay back quickly and build the confidence to attempt harder things.

What we build01 / 03

What we typically automate

01

Documents into data

Invoices, purchase orders, forms and delivery notes read and turned into structured records, with anything uncertain routed to a person rather than guessed.

02

Classification and routing

Incoming email, tickets and enquiries read, categorised and sent to the right team, with priority and sentiment attached.

03

Drafting and replying

First-draft replies written from your own documents so your team edits rather than composes. Faster, and consistent in a way people never are.

04

Extraction and checking

Pulling the specific facts out of contracts, reports or statements, and flagging where two documents disagree with each other.

05

Reporting that writes itself

The weekly summary somebody assembles by hand, generated from the source systems with the commentary drafted.

06

Human in the loop by design

Confidence thresholds decide what goes through and what a person checks. You choose where that line sits, and you can move it as trust grows.

Why MnT Future02 / 03

How we approach automation

01We measure the task before we touch it: how long it takes, how often it happens, and how often it goes wrong today. Without that there is no way to prove the automation helped.
02We start with the automation running alongside your team rather than instead of it, and compare.
03Confidence thresholds are set so the system escalates rather than guesses. An automation that is confidently wrong is worse than no automation.
04Everything it does is logged and reversible, so a mistake is a correction rather than an investigation.
05We widen scope only after the narrow version has proved itself on real volume.
How we engineer compliance
How we work03 / 03

Discovery, build, certify, scale.

A senior-led delivery model built for revenue-critical commerce: predictable and transparent.

01

Discovery

We map the workflow, the constraints, and the compliance surface before a line of code.

02

Build

Senior engineers ship in two-week sprints. You see working software, not status decks.

03

Verify

Security and compliance are tested as we go (ADA/WCAG, PCI DSS, SOC 2 controls), never bolted on at the end.

04

Scale

We harden, instrument, and hand over, or stay on as your embedded product team.

FAQ

Questions buyers ask us first

In our experience it moves them onto the work that needed judgement, because the volume that was queuing up gets handled. We are not going to promise you headcount reduction, because the honest outcome is usually more throughput from the same team.

It is designed to escalate rather than guess. You set the confidence line, everything is logged, and actions are reversible. Before go-live we run it alongside your team and compare, so you see the error rate before it matters.

We measure the task first: time, volume and current error rate. If those numbers do not justify the build, we will tell you before you spend anything.

Only if you are comfortable with that, and we tell you exactly what would be sent. Where policy does not allow it we run smaller models on your own infrastructure instead.

Next step

Name the task that eats your team's week.

The repetitive one everybody complains about. A senior consultant will tell you whether it automates well, what it would cost, and whether the numbers justify doing it.