India based Employee Transportation Company
10%
lower operational cost
Silo integration, vehicle right-sizing and shift-wise capacity planning reduced overall spend by a tenth with no reduction in employee coverage or pickup reliability.
NirnAI OS is the decisional AI layer for the Employee Transportation Service industry.
We sit on top of your FMS, VTS, RMS, etc., unify their data into one operational graph, and embed solver-grade optimization, ML models, and process intelligence into the decisions that power the ETS industry. Today's AI brought intelligence. The next frontier is judgement.
A transport desk makes a thousand decisions before 6am.
Which employees actually travel today, not just who booked. Which nodal points to run and which to collapse. Which vendor's vehicle is closest to an empty leg. Whether tonight's 2am drop needs a marshal.
Most of these calls are made from a spreadsheet, a WhatsApp group and twenty years of instinct. They are good calls. They are also unauditable, unrepeatable, and impossible to cost.
Platform
The NirnAI OS platform
OS is our framework for building AI decisional intelligence for ETS, unlocking maximally optimal decisions at breakneck speed across the entire process: roster reading, cab allocation, SLA compliance, and everything in between. The five stages describe NirnAI's continuously corrected decisional AI layer for ETS. Some components are interchangeable across engagements, others are scoped, built, and tuned per customer.
Integration
Inside our OS stage by stage
Stage 0101 / 05
Data Infrastructure
Data infrastructure unifies operational data across the systems you already run: FMS, VTS, RMS, Excel, etc. IDs, timestamps, and event semantics are reconciled so that booking requests, trip IDs, pick and drop locations, and shifts become first-class objects, ready for the process intelligence stage to consume. Integrations and the unified schema are shaped to each client’s data landscape.
Results
Results
Outcomes from 10+ deployments and pilots across United States and India.
India based Employee Transportation Company
35%
higher fleet utilisation
Surfacing underused vehicles, inefficient routing and poor fleet positioning let the operator consolidate trips and nodal points, lifting seat utilisation by over a third.
India based Employee Transportation Company
40%
fewer cabs deployed
A fleet operator halved deployed cab count by collapsing nodal points and matching vehicle allocation to predicted rather than booked demand, holding coverage flat.
US based Employee Transportation Company
80%
increase in bid win rate
Lower cost and tighter control on per km cost allowed fleet operator to outbid competition for 6 contracts.
US based Employee Transportation Company
20%
reduction in manpower
Automated manual tasks reduced requirement for back end analysts and manual assigners and trackers.
Engagement Model
We operate on an Outcome Based Pricing model — you pay only after accruing the financial gains in real time.
If you run an ETS Company for a campus or a multi-site enterprise, we should talk.
Book a call with our team, or ask for a read-only pilot on one shift.