Easier Employee Rewards
Ask what your points can get you, whether the hoodie is in stock, or where your order is — then redeem, return, or approve from chat. Orders, returns, and approvals ask you to confirm first.
Why Employees Don't Use The Rewards They Earned
Company Store Agent attacks the four specific failures that turn earned points into expiring points — without changing how recognition, redemption, or fulfillment work.
Nobody Remembers How Many Points They Have
Recognition lands, points accumulate quietly, and the employee never opens the store. By the time someone says "you should redeem something," the moment has passed and the points feel abstract. The reward stops feeling like a reward.
Browsing The Catalog Is A Tab-Switch Tax
"What can I get for under 500 points?" requires opening the store, applying a price filter, scrolling pages of items. Most employees give up at step one and the catalog stays unused.
Orders Get Placed Then Forgotten
"Did my hoodie ship?" "When does the gift card arrive?" Without a way to ask, employees hit the store admin or write off the order entirely. Fulfillment status is invisible from where the employee actually works.
Affordability Is A Mental-Math Problem
The premium backpack costs 4,200 points. The employee has 3,450. How short are they, and what else can they get instead? Today they do the subtraction by hand. The catalog doesn't tell them what they can afford right now.
Points Expire Without Anybody Seeing The Warning
Annual recognition cycle ends in three weeks and 40% of the workforce has unspent points that are about to zero out. The store admin sends a generic "remember to redeem!" email that goes straight to filtered folders. The employee finds out their points evaporated the next time they check — and now the program feels like a bait-and-switch instead of a reward.
New Catalog Items Launch To Silence
The store team adds 30 new items for Q4 — premium jackets, a new tech category, holiday bundles — and nobody notices because the catalog page isn't anyone's daily destination. A simple "what's new this week?" answer in chat would surface new SKUs to the people whose point balance can actually afford them, but today every catalog refresh ships into a void.
Company Store At A Glance
Company Store AI
Balance, catalog, orders, affordability — answered from chat.
Inside Company Store Agent — The Actual Capabilities
Every block below maps to a real tool the agent uses against your Company Store data. Reads are instant; redeeming, checking out, returning, cancelling, and approving or declining each show a confirmation before anything is spent or changed. Adding to or removing from your cart happens right away and spends nothing.
Know Your Points At Any Moment
Employees ask "how many points do I have?" and the agent returns the current balance, pending points, and lifetime earned/spent in one response. No app-switching, no recalculating.
get_my_points_balance— current balance, pending points, lifetime earned and spent.get_points_history— transaction history filterable by period (week, month, quarter, year) and type (earned, spent).- 30-day activity summary — earned, spent, and transaction count for the last 30 days.
- Pending points surfaced — recognitions awaiting approval are visible separately from spendable balance.
Browse The Catalog Without Browsing The Catalog
Employees ask "what can I get for under 500 points?" or "is the company hoodie available?" and the agent returns matching items with the can-afford flag already computed against the user's live balance — no mental math required.
search_catalog— by keyword, category (swag, gift card, experience, charitable), or max points price.check_item_availability— returns stock status, current price, can-afford flag, and points-needed if short.get_featured_items— promoted items surfaced for "what's new?" or general browsing.get_item_details— full record including variants, estimated delivery days, and description.
Track Orders And Where Their Rewards Are
Employees ask "where is my hoodie?" or "did the gift card arrive yet?" and the agent answers from live order data — including tracking numbers, and whether a digital reward's link is waiting on the order page — without opening a single email or admin ticket.
get_my_orders— every order the user has placed, filterable by status (pending, processing, fulfilled, cancelled).get_order_details— for one specific order: item, quantity, points spent, status, tracking number, whether a reward link is ready (the link itself stays on your order page), can-cancel flag.- Reward links stay private — a gift card's link can be spent by whoever holds it, so the agent tells you it is ready and sends you to your order page to open it; it never puts the link in chat.
- Audit trail on every retrieval — even read calls log the requesting user and which tool ran.
Outcomes Teams Can Measure
The agent's job is to convert earned points into actual rewards — without adding admin work for the store team. Measure adoption and redemption against your pre-agent baseline.
- Points redemption rate — share of earned points that get spent within 12 months vs the historical baseline.
- Balance-check frequency — how often employees check their own balance (the leading indicator of engagement).
- Time-to-redemption after a points-earning event — median days from recognition to first redemption.
- Catalog browse-to-order conversion — share of catalog searches that lead to an actual order.
- Order-status questions to admin — Slack/email interruptions absorbed by the agent.
Writes Happen — But Points Never Move Silently
Redeem an item, check out a cart, file a return, cancel an order, or (for redemption approvers) approve a held redemption right from chat. Each of those shows a confirmation before any points are spent or an order changes. Cart edits are the one exception: adding or removing an item happens right away, and nothing is spent until you confirm checkout.
- Confirmation before points move — redeem, checkout, return, cancel, approve, and decline each ask you to confirm before they run. Cart edits don't ask, and spend nothing. No silent orders, no silently-spent points.
- Permission-aware — employees act only on their own balance and orders; budget visibility is limited to managers and admins, and approvals to the people allowed to decide a held redemption.
- Pending vs spendable — the agent never conflates pending recognition points with spendable balance.
- Audit trail on every call — reads and writes alike log the requesting user and which tool ran.
WHAT TEAMS TRY INSTEAD
The four alternatives — and why none of them tell THIS employee what they can afford right now
HR and rewards teams trying to drive redemption usually try one of these four. None of them answer "what can I afford with my current balance?" without the employee opening the store.
Pasting the catalog into ChatGPT, Claude, or Copilot
Employees copy a catalog page and ask "what fits 500 points?"
- The agent answers from the employee's live balance — generic AI has no idea what they actually have
- Affordability check is live; a generic chatbot can't tell pending recognition from spendable balance
- Order tracking comes from the real fulfillment record — not a guessed status
Awardco AI / Bonusly AI Marketplace
Vendor-trapped recognition-marketplace AI inside one rewards platform
- Composes with Recognition, Communications, Mango Spend, and Personal AI — not stuck inside one rewards-vendor surface
- One agent runs on the platform employees already use for shifts, pay, and recognition — no separate vendor login
- Catalog updates surface in chat as "what's new this week?" — vendor AIs assume employees open their app
A custom rewards-store chatbot
An engineering team's six-month build, then forever maintenance of catalog and fulfillment integrations
- Shipped already. Engineering spends zero weeks plumbing balance, affordability, ordering, or order-status flows
- Orders and returns are confirmation-gated — the agent can place an order or file a return, but only after the employee confirms
- Inherits new capabilities (new categories, point-expiry warnings) as the platform evolves
The manual fallback — generic "remember to redeem!" emails
The default when points are about to expire and HR sends a blast
- Personalized affordability ("you have 3,450 — here's what you can afford right now") beats a generic reminder
- Order tracking self-serves — store admins stop being the shipping desk
- New catalog launches reach the right employees instead of dying in filtered email folders
PLATFORM ADVANTAGE
Company Store Agent inherits everything the platform already runs
A standalone rewards-store bot has to plumb each of these. Company Store Agent gets them for free because Recognition, Communications, and Mango Spend already do.
Cross-app data plane
Recognition points, catalog, order status, and Mango Spend balances all reach the same agent — no separate sync between recognition and the rewards storefront.
Unified permission model
The user sees their own balance, their own orders, and the catalog their tier allows — same model as the Company Store app, no parallel ACL.
Audit trail on every retrieval
Even read calls log the requesting user and which tool ran — useful when HR audits redemption patterns.
Answers in your language
Ask AI replies in the language each person asks in, so multilingual workforces can browse the catalog and check their balance in their own language.
Mobile delivery for the floor
A frontline employee checks balance and affordability from the same mobile app they used to clock in — no separate rewards client to install.
Provider-agnostic model tiering
The platform assigns each agent one model tier suited to its work — set for the agent, not picked per question.
INDUSTRY FIT
Industries where redemption rates make or break the rewards program
Company Store Agent helps wherever a large frontline workforce earns points they're under-using.
Retail
Hourly associates check balance and order status from the same app they use for shifts — redemption rates climb without HR campaigns.
Hospitality
Multilingual hotel and restaurant teams self-serve the catalog in their language; recognition points stop expiring unused.
Manufacturing
Plant-floor workers without a desk get balance and affordability answers on a kiosk or phone; safety-milestone redemptions actually happen.
Logistics & Transportation
Drivers on the road check balance between routes; new catalog drops reach them without a desktop login.
Healthcare
Clinical staff between shifts check balance and order status; recognition program stays vibrant instead of fading after launch.
Distributed Knowledge Workers
Remote employees see live balance and what's affordable; new launches surface in chat instead of dying in inbox campaigns.
WHY MANGOAPPS WINS
An embedded agent beats a chatbot, a vendor add-on, or a custom build on every axis
The argument finance, security, HR, and rewards ops all share — and the one a single-vendor recognition-marketplace AI structurally cannot answer.
Cheaper than the alternatives
No per-seat ChatGPT license, no Awardco or Bonusly AI tier, no six-month custom build, no HR-team overhead for "remember to redeem" campaigns.
More secure
Redeeming, returning, cancelling, and approving are confirmation-gated and permission-aware, and every call logs the requesting user and which tool ran. Approval tools are offered only to redemption approvers, and the budget tool only to managers and admins.
Easier to deploy
Already deployed if you have Company Store enabled. Turn the agent on against the existing catalog and balance data and it's running the same day.
Easier to use
Lives inside Ask AI — no separate rewards-store app, no balance-page tab-switch, no "do I have enough?" mental math.
Easier to manage
Catalog visibility, tier rules, and expiry policy all sit in the same admin console as every other app, under one access model.
Easier to extend
Shares the agentic tool framework with every other MangoApps agent. New categories, new affordability slices, or new launch announcements ship as tools, not rewrites.
AI is actually better
A vendor rewards AI can show a catalog. Only Company Store Agent reads the employee's live balance, tells them what they can afford right now, tracks their order, and warns them before points expire.
At our head office within HR, marketing, and finance, we use ChatGPT quite a bit, and MangoApps AI Helper is a useful tool for us. As I go around to the stores, I’ve been showing them how the AI Intranet Helper can find things and answer plain language questions about our resources, and they’re quite excited about that.
Alison van Zijl
Head of Human Resources
Full House
Customer Success
MangoApps Customers
Organizations that trust MangoApps to run every workflow across their workforce.
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Frequently Asked Questions About Company Store Agent
23 tools. Reads: points balance (with pending and lifetime), points history, catalog search with affordability flags, featured items, item availability, item details with variants, your orders, specific order details with tracking and whether a reward link is ready, and your cart. Writes that ask for confirmation first: redeem an item with points, check out your cart, file a return or exchange (or report a gift card problem), cancel an order, ask the store admins to cancel one, send the store a question or request about an order, and withdraw a return or request you sent. Adding to or removing from your cart happens right away and spends nothing. For redemption approvers: view and act on held redemptions, and look back at the ones they approved or declined in the last 30 days. For managers and admins: check the team's store budget.
Yes — ask it to "order the hoodie" and it calls the redeem_item tool. That tool asks for confirmation first, so you see what is about to be ordered before anything is spent. Nothing is ordered or charged silently, and the same applies to returns and to approve/decline actions.
Every catalog and item response includes a can-afford flag computed against the user's live get_my_points_balance balance — not pending points, only spendable. If the user is short, the agent returns the exact points-needed figure so they know how close they are.
get_order_details returns the tracking number for physical items (shipped via the configured carrier) and, for digital items (gift cards, digital rewards), only whether a reward link is ready. The link can be spent by whoever holds it, so it stays on the order page and never enters the chat. Both come from the order record as it's updated by the fulfillment system — the agent is simply reading the same status the Company Store app shows.
Points redemption rate, balance-check frequency, time-to-redemption after a points-earning event, catalog browse-to-order conversion, and order-status questions to admin. Compare against your pre-agent baseline.
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Rolled out to every employee at AutoZone (125,000), PetSmart (50,000+), A.S. Watson and Raley's (20,000) — and at larger retailers we are not permitted to name.
Prefer to explore first? Ask AI about Company Store AI Agent →