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Bgabs
AI service engine

Know which products need service before they fail, and dispatch automatically.

Bgabs tracks every warranty and AMC contract, predicts which products are likely to need service based on real usage patterns, and manages the technician dispatch queue end to end.

AC UNIT

Model X200

Covered
Coverage remaining
312 / 365 days
live pulse local time
pulse.v1

Contracts

Tracked live

Warranty and AMC, per unit

Prediction

Before failure

Usage and model history

Scheduling

Proactive

Not reactive to complaints

Dispatch

Automated

Right skill, right route

The problem we are solving

Warranty and AMC operations
are still run reactively.

Products fail, complaints land, and dispatch happens by phone calls and guesswork. The moments that would have prevented all of that are lost in spreadsheets and static expiry fields.

01

Failures land on the customer, and on your support desk.

A product fails right after warranty expires. The customer's frustration lands on the brand, not on the technician who never came.

02

Service requests get scheduled after breakage.

Nothing gets booked until a complaint is raised. By then the fix is bigger, and the visit costs more than a proactive one would have.

03

Dispatch runs on phone calls and guesswork.

The dispatcher does not know who is nearest, who is skilled for the model, or which stops are already on the technician's route.

How Bgabs works

One continuous service arc,
from coverage to dispatch.

Every product moves through the same four stages. The service engine tracks it, predicts what is coming, schedules ahead of it, and dispatches the right technician.

Stage 1

Track

Coverage tracked live

Every warranty and AMC contract is stored with a real coverage status, not just a static expiry date.

Stage 2

Predict

Service need forecast

Products likely to fail soon are flagged from usage patterns and model failure history.

Stage 3

Schedule

Proactive booking

A visit is proposed inside the customer window before any complaint gets raised.

Stage 4

Dispatch

Right tech, right route

The right technician is assigned by skill, location and current route load, and tracked end to end.

What the AI actually does

Concrete capabilities. Not a contract database.

Bgabs is a decision engine, not a spreadsheet. Each capability plugs into the next, so a contract signed today becomes a dispatched technician months from now.

Coverage tracking

Real-time warranty and AMC status per product, not a static expiry date field. Coverage decays as days pass and re-computes when service is added.

WARRANTY - AC UNIT312 / 365 d

Predictive service-need detection

Flags products likely to need service based on usage patterns and known model failure history. The alert appears before a complaint would land.

Predicted service in 12 days

Cycle-time drift beyond healthy band.

Proactive scheduling

Suggests visits inside the customer window before failure, then confirms and reminds. Reactive complaint tickets become a fallback, not the default.

M
T
W
T
F
S

Dispatch and routing

Assigns the right technician based on location, skill, and current route load, then tracks the visit to close-out.

Suresh K.

stop 4 of 6 on route 7

en route

Numbers that matter

The outcomes we hold ourselves to.

Illustrative pilot targets, replaced with your real figures once your first month of coverage data is in the engine.

~ 3 in 4

Service needs predicted before failure

~ 62%

Proactive visits scheduled ahead of complaints

~ 100%

Route stops added without a dispatch call

Placeholders will be replaced with real pilot data once available.

Live pulse builder

Tell us a product and install date.
See its service pulse.

The coverage bar, the predictive alert, and the technician dispatch card all build live from your inputs. An illustrative demonstration based on typical product patterns, not a live read of an actual unit.

53 days in service so far.
Book a demo

AC UNIT

Model X200

Covered
Coverage remaining
312 / 365 days
live pulse local time
pulse.v1

Illustrative demonstration based on typical product patterns. Not a live read of an actual unit.