Mall Trip Companion Agent

For

Marketing and operations leads at mid-sized shopping centres

Solves

Loyalty cards sit unused in wallets, shoppers forget errands and miss live promotions, and centres lose footfall to online retail.

Delivers

Personalised shopping route with timed reminders, automatic discount application at checkout, trip receipts with total savings, and centre footfall, dwell and redemption dashboards

Built in

about 3 weeks of creation time, MVP in 3 days

Investment

$6,500 for the MVP, $22,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For marketing and operations leads at mid-sized shopping centres, turn shopper voice notes, centre maps, store inventories and active promotions into a personalised shopping route, timed stop reminders and automatic discount application at checkout.

1. Capture spoken errand lists and convert them to structured stops.
2. Build an efficient walking route across centre stores.
3. Read live store promotions, PDFs and images as they change.
4. Vibrate or notify at each stop, including errands like dry cleaning passed en route.
5. Apply eligible discounts and loyalty credits automatically at checkout.
6. Feed anonymised footfall and basket insights back to centre management.

What goes in, what comes out

The workflow

1. In
   Start with

   Centre floor plan and store directory, store inventories and active promotions, loyalty programme terms, shopper voice notes and location pings during trips

2. 1

   Tap the card or app to start a trip

3. 2

   Speak the errand list

4. 3

   Match stops against inventories and promotions

5. 4

   Review the suggested route

6. 5

   Walk the route with timed reminders

7. 6

   Check out with automatic discount application

8. 7

   Close the trip with a receipt and insights record

9. Out
   Finish with

   Personalised shopping route with timed reminders, automatic discount application at checkout, trip receipts with total savings, and centre footfall, dwell and redemption dashboards

AI does the heavy lifting, people stay in charge

Use speech models to transcribe errand notes, language models to extract structured stops, document and image models to read promotions as they are published, and routing logic to sequence stops by location and opening hours. Deterministic checks confirm store locations, opening times and discount rules before anything is applied. A centre-side administrator reviews promotion matches and redemption rules before they go live, and no discount is charged without a valid store-authored promotion on file.

What your team sees

Key screens: Errand capture, route map, promotion wallet. Use a voice-first capture screen where shoppers speak their errands for the day, a central map of the centre with the fastest ordered route and live store pins, and a wallet showing applied deals, loyalty credits and dry cleaning reminders. Let shoppers reorder stops and see a vibration cue on each arrival. Display planned, in progress and completed trips. Provide a centre-side dashboard with footfall, dwell time and redemption data. In this product, the first view is errand capture, followed by route map and promotion wallet.

Accounts and administration

Centre administrator accounts with role-based access, store-level promotion authors, versioned promotion records, approval states for live offers, audit trail of applied discounts and redemptions, shopper data permissions with location tracking opt in, and anonymised analytics exports.

Integrations and data access

Start with a static centre map and store directory supplied as spreadsheets, then add point of sale and loyalty systems for discount application, store promotion feeds or PDF drops, and location services on the shopper's phone.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   3 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   4 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   7 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will marketing and operations leads at mid-sized shopping centres use it to solve "loyalty cards sit unused in wallets, shoppers forget errands and miss live promotions, and centres lose footfall to online retail"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Run a paid four to eight week pilot at one centre with 200 to 500 shoppers.
4. Measure, then decide. Track 1. Uplift in average basket size among active card users against the centre baseline. 2. Share of errand lists completed as a full route with at least one promotion redeemed. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One mid-sized shopping centre with 50 or more stores, one use case: speak an errand list and receive a fastest route across those stores on a static centre map. First two modules are voice capture with stop extraction and the route map with ordered reminders. Every promotion suggestion is manually reviewed by the centre team before shoppers see it.

After the MVP. After the paid pilot, automate promotion ingestion from store PDFs and images, automatic discount application at point of sale, dry cleaning and service reminders based on location and past trips, and weekly footfall and basket insight reports for centre management.

What the build depends on. Speech recognition on the phone, document and image extraction models, indoor routing over a centre floor plan, point of sale or loyalty integration for checkout discounts, and opt-in location tracking with battery-friendly pinging.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Hypothesis: charge the shopping centre a monthly fee of 2 to 5 USD per active card user, with a centre minimum of 1,500 USD per month, plus a setup fee for store promotion onboarding.

Message to test

Give shoppers a card that plans the trip, finds every live deal and reminds them of each errand, and give your centre the footfall online retail cannot touch.

Where to find buyers

Shopping centre industry conferences, retail property management networks, regional shopping centre associations, trade publications, and direct outreach to centre marketing managers.

Lead magnet

A free two-week footfall and errand-mapping pilot at one centre, where shoppers speak errands and the centre receives a baseline report of trips, dwell time and missed promotion exposure.

The first 30 days

1. Week 1: secure one centre partner, collect the floor plan, store directory and baseline footfall data.
2. Week 2: ship voice capture and stop extraction with the static route map for a closed shopper group.
3. Week 3: run in-centre trials, gather trip recordings and manually review promotion matches with the centre team.
4. Week 4: deliver the first footfall and basket comparison report and agree a paid monthly rollout.

Paid pilot

Run a paid four to eight week pilot at one centre with 200 to 500 shoppers. Baseline footfall, dwell time and average basket from the preceding four weeks. Decision: proceed if active-user route completion and redemption rates lift basket size by an agreed threshold, for example five percent, otherwise revisit store data coverage before expanding.

Success metrics

1. Uplift in average basket size among active card users against the centre baseline. 2. Share of errand lists completed as a full route with at least one promotion redeemed.

Retention and expansion

The centre pays monthly because shopper pattern data and footfall gains compound over time, stores contribute promotions each season, and switching means rebuilding store data feeds and shopper habits from scratch.

Why clients would pick it

Each centre contributes exclusive real-time inventory, promotion and shopper pattern data, and store integrations deepen with every redemption cycle, making the dataset hard for a competitor to replicate.

Alternatives and positioning

Shoppers use paper lists, plastic loyalty cards, mall directory screens and coupon apps that require manual scanning. This differs by combining route planning, live deal application and proactive reminders into one agent that works while the shopper walks.

Main delivery costs

Model inference for transcription, promotion reading and routing, mobile and card integration development, centre onboarding and store data collection, cloud hosting, and ongoing account support.

Location tracking is opt in and trips can be deleted by the shopper. Discount application is limited to store-authored promotions with valid terms and an approval record, and never changes prices without a confirmed integration. The system must not share individual shopper identities with stores, must not apply expired or unauthorised promotions, must not route shoppers to closed stores, and centre dashboards only expose aggregated anonymised data.