SHIFT.
SHIFT / Exponet AI Concept / Pre-seed

Pay only when a car journey disappears.

SHIFT is an AI-powered mobility verification platform designed to measure when low-carbon travel actually replaces driving.

See how it works Contact Ahmad

Don’t reward activity. Reward verified behaviour change.

Live system model Concept
01Bike trip8.4 km observed
02VerifySignals aligned
03ReplacementLikely car alternative
Outcome Avoided vehicle-km Reward eligible
01 / The problem

Cycling is easy to track.
Impact is harder to prove.

A mobility app can tell you that someone cycled 8 km. That does not automatically mean 8 km of driving disappeared.

Fun ride10 km

No displaced car trip

The rider would otherwise have stayed home.

0 verified avoided car km
Commute10 km

Likely replaced driving

The trip matches a habitual car journey to work.

Verified avoided car km
Activity≠Impact

SHIFT focuses on the counterfactual: what would likely have happened without this low-carbon trip?

02 / The core model
Observed low-carbondistance
×
Car-replacementprobability
=
Verified avoidedvehicle-km
Verified avoided vehicle-km→Estimated CO2 avoided→Rewardable outcome

We only reward the part of behaviour change the system can defend.

03 / How SHIFT works

From movement to
measurable outcome.

Five linked decisions turn an observed trip into a sponsor-ready outcome.

  1. 01
    D

    Detect

    Trip and route data

  2. 02
    V

    Verify

    Mode and trip integrity

  3. 03
    E

    Estimate

    Car-replacement probability

  4. 04
    Q

    Quantify

    Avoided vehicle-km and CO2

  5. 05
    R

    Reward

    Sponsor-funded incentive

04 / Product concept

Evidence users can
understand.

A consumer experience that makes verified impact visible without hiding uncertainty.

9:41
S.AK

Good morning

Your mobility impact

Illustrative demo
6.2avoided car km
1.1 kgCO2e avoided
€0.46reward
Cycling detectedTrip verification active
→

01 Home

9:41
←Trip details···
Qualified tripCycling detected
Illustrative model output
Car-replacement confidence92%
Trip integrityHigh
Distance8.4 km

02 Verification

9:41

Impact

S.

This week

Illustrative demo
24.8avoided car km+18% vs last week
MTWTFSS
CO2e avoided4.2 kgEstimated

03 Impact

9:41

Rewards

S.
Illustrative demo
Mobility balance€12.80Sponsor funded

Use your reward

Energy billTransit creditLocal commerceWallet

Recent activity

Morning commuteToday, 08:42+€0.46
Transit journeyYesterday+€0.30

04 Rewards

05 / Measurement, Reporting & Verification

The hard part is not detecting cycling.

The hard part is proving what the cycling replaced.

Trip data+Personal baseline+Context+ML model=Car-replacement probability
01

Trip integrity

GPS continuity, speed profile, sensor consistency, and anomaly detection.

02

Mode detection

Bike, walk, transit, and vehicle signatures combined with route behaviour.

03

Counterfactual

Historic behaviour, habitual routes, destination context, and car-alternative likelihood.

04

Audit trail

Confidence threshold, model version, evidence, assumptions, and fraud controls.

“We cannot observe the alternate universe. We estimate it using multiple independent signals.”

SHIFT is designed as incentive-grade MRV. It does not claim to create certified carbon credits; any carbon-credit eligibility would require a separate methodology and independent validation.

06 / Deployment

Software first.
Hardware optional.

SHIFT is designed to work primarily using smartphone sensors and mobility data. That keeps participation easy and programme launches practical.

SHIFTSmartphone sensing
High-assurance verificationFraud preventionGround-truth datasetsSpecialised pilots

Hardware should strengthen verification — not block adoption.

08 / Business model

Aligned with verified outcomes.

01

Platform subscription

Annual programme fee

02

Verified outcome fee

Per qualified trip or avoided vehicle-km

03

Integration & reporting

API, analytics, billing, and reward integration

Reward pools are sponsor-funded pass-through amounts and are not SHIFT platform revenue.

09 / Scale

Start with cycling.
Build the verification layer for mobility.

Cycling→Walking→Public transport→Shared mobility→Future modes
Better
verification
More tripsBetter modelsBetter sponsor ROIMore programmesMore data
10 / Why now

Mobility funding needs a better feedback loop.

Cities are investing in modal shift and active mobility.

Public climate budgets increasingly need measurable outcomes.

Smartphone sensing and ML can support stronger verification.

Sponsors need evidence that incentives changed behaviour.

11 / Current status

Where SHIFT is today.

Clear about the stage. Focused on the next proof point.

StageConcept / Pre-seed
Current focusMVP + verification model
First target marketItaly
Next milestonePilot with a city, mobility programme, or utility
12 / Roadmap

From model to repeatable pilot.

  1. 0–3 monthsMVP

    Trip verification + baseline model

  2. 3–6 monthsPilot #1

    Initial riders + calibration

  3. 6–9 monthsVerification review

    Sponsor / utility integration

  4. 9–12 monthsPilot #2

    Second geography + repeatable launch process

13 / Founder

Built by Ahmad Karami.

Founder of Exponet AI. AI Engineer and Full-Stack Developer focused on building practical AI-driven products.

Let’s talk about SHIFT
The question

What if mobility budgets paid only for behaviour change we could verify?

SHIFT is building the measurement layer between low-carbon travel and measurable climate outcomes.

Contact AhmadBack to portfolio
Exponet AI · Padova, Italy