# Retter — Full Site Content > Retter designs, builds, and runs enterprise-grade agentic AI systems on AWS serverless — live in weeks, not months. Platforms serving 20M+ consumers across 10+ countries for brands including Starbucks, Emirates Leisure Retail, PAUL, istegelsin, Gratis, MMI, Carrefour, and A101. Two ways to work with Retter: build a new AI-native platform end to end, or layer agentic outcomes onto the platforms you already run — no replatforming required. Track record: 20M+ consumers served, 10+ countries, 100K+ daily transactions, zero downtime in production, typical cloud cost reductions around 70%. Products: the Retter Orchestrator (takes enterprise AI agents to production — secure, governed, live in weeks) and the Rio serverless platform. Contact: hello@retter.io, the chat on https://retter.io, or https://retter.io/contact (offices in İstanbul, Düzce, and Dubai). ## About Retter Source: https://retter.io/about Retter designs, builds, and runs enterprise-grade agentic AI systems on AWS serverless. There are two ways to work with Retter: build a new AI-native platform end to end, or layer agentic outcomes onto the platforms you already run — no replatforming required. The platforms Retter runs serve 20M+ consumers across 10+ countries with 100K+ daily transactions and zero downtime in production. ### Frequently asked questions **Q: What does Retter do?** Retter designs, builds, and runs enterprise-grade agentic AI systems on AWS serverless — platforms where AI agents handle operations, commerce, loyalty, and reporting. Live in weeks, not months. **Q: What is the Retter Orchestrator?** The Retter Orchestrator takes enterprise AI agents to production: secure, governed, and live in weeks. Almost anyone can build an AI agent demo — the Orchestrator is what makes agents safe to run a real business on. **Q: Do we have to replace our existing platform?** No. There are two ways to work with Retter: build a new AI-native platform end to end, or layer agentic outcomes onto the platforms you already run. Replatforming is never a requirement. **Q: Which brands run on Retter platforms?** Starbucks (loyalty across 3 countries), Emirates Leisure Retail (300+ outlets), PAUL (9 countries), istegelsin, Gratis, MMI, Carrefour, and A101 — platforms serving 20M+ consumers in 10+ countries. **Q: How fast is "live in weeks"?** A reporting agent goes live in 3–4 weeks, a customized store in as little as 2 weeks, and a first backend workflow in 6–8 weeks. We start with a contained, high-value piece and expand from there. **Q: How is the AI kept safe in production?** Every agent works inside limits you set: scoped permissions, budgets, approval gates for irreversible actions, and a full audit trail of every decision. That is the difference between a demo and an enterprise system. ## Solutions ### Agentic Backend Systems Source: https://retter.io/solutions/agentic-backend-systems AI that runs your operations, so your team doesn't have to. AI that runs your operations — on new systems, or the ones you already have. Routine work — order processing, inventory updates, data syncs — handled automatically by AI agents. Campaigns launch from performance data during the day, reviews are moderated, and fraud and suspicious transactions get resolved without a human in the loop. Your team steps in only when it matters. Capabilities: - No human clicks: Handles repetitive backend tasks without human clicks. - Fixes itself: Catches and fixes issues before they become outages. - Always in your limits: Every action logged, every limit set by you. How an engagement runs: 1. Discover: We map your systems and find where people babysit processes today. 2. Design: We decide what the AI handles, and where humans stay in charge. 3. Build: We build it on AWS, ready for production from day one. 4. Operate & scale: We run it with you, giving the AI more work over time. Proof: - 100K+ daily transactions processed - Zero downtime in production - 70% typical cloud cost reduction FAQ: **Q: Do we have to replace our existing backend?** No. The AI connects to what you already run. Your systems stay. A full rebuild is an option, never a requirement. **Q: How do you keep the AI safe in production?** It works within limits you set: what it can access, what it can spend, what needs your approval. Every action is logged. **Q: How long until it's live?** A first workflow is usually live in 6–8 weeks. We start small and grow from there. **Q: What does it run on?** AWS serverless — the foundation we have run in production for a decade for brands like Starbucks, Carrefour, and Emirates Leisure Retail. It scales itself and bills for use, not idle servers. **Q: Who runs agentic backends like this today?** The Starbucks platform we run handles 100,000+ transactions a day across 3 countries with zero downtime. Same foundation, with agents on top. ### Agentic E-Commerce Source: https://retter.io/solutions/agentic-e-commerce A store that never sleeps — selling, supporting, and fulfilling 24/7. A store that never sleeps — fully customized and live in 2 weeks. AI agents help every customer find the right product, answer their questions, and keep orders moving. Fully customized and live in 2 weeks. Running a marketplace? Agents own seller onboarding and product approval end to end, and coach your sellers with plain, actionable recommendations that reduce returns and grow profitability. Capabilities: - Personal recommendations: Personal product recommendations for every shopper. - Answers any hour: Instant answers to customer questions, any hour. - Sellers, handled: Seller onboarding, approval, and coaching handled end to end. How an engagement runs: 1. Discover: We look at your store and find where shoppers drop off. 2. Design: We build a working prototype and pick what the AI runs first. 3. Build: We build it on AWS and test it against your current store. 4. Launch & scale: Go live market by market and watch the numbers move. Proof: - 20M+ consumers served - 10+ countries - 10x faster go-to-market FAQ: **Q: Does this work with our existing e-commerce platform?** Yes. It connects to your store through APIs. You never have to replatform to start. **Q: How does the AI stay on-brand?** It works from your brand voice, your catalog, and your rules, with human escalation built in. **Q: What moves first: revenue or cost?** Usually revenue. AI-assisted shoppers buy more. Cost savings follow as the AI takes on more work. **Q: How fast can a store go live?** A fully customized store is live in 2 weeks. Adding the AI layer to an existing store moves just as fast, because nothing gets rebuilt. **Q: Does this work for marketplaces?** Yes. Agents own seller onboarding and product approval end to end, and coach sellers with plain recommendations that reduce returns and grow profitability. ### Agentic Loyalty & Engagement Source: https://retter.io/solutions/agentic-loyalty-engagement Loyalty that knows every customer, in every market. Loyalty that knows every customer — runs on your existing program, in every market. Create a promotion with your dedicated agent: chat, test, publish — all in 10 minutes. The promotion you discussed in Monday’s meeting can be live before the meeting ends. And your promotion agent talks to your analytics agent, handing you ready-to-launch campaigns built from live performance data. Capabilities: - 10-minute promotions: Idea to live promotion in 10 minutes. - Campaigns propose themselves: Campaigns proposed automatically from real performance data. - Every market: Runs on your existing loyalty system, in every market. How an engagement runs: 1. Discover: We look at your member data and program economics. 2. Design: We set the AI's goals and limits: spend, frequency, brand rules. 3. Build: We connect the AI to your loyalty system and channels. 4. Optimize & scale: The AI tests and improves offers, member by member. Proof: - Millions of members managed - 300+ outlets — Emirates Leisure Retail - 3 countries on one Starbucks platform FAQ: **Q: Does this work with our loyalty platform?** Yes. It connects to your loyalty system, CRM, and channels. No program yet? We build both. **Q: How do you protect our margins?** You set hard limits: cost per member, margin floors, contact caps. Every offer is logged. **Q: How is this different from marketing automation?** Automation runs the campaigns someone designed. Our AI designs, tests, and improves them itself, for each member. **Q: How fast can a promotion go live?** About 10 minutes. Chat with your promotion agent, test, publish — the promotion from Monday’s meeting can be live before the meeting ends. **Q: Who runs loyalty on this today?** Starbucks, across 3 countries and millions of members, and Emirates Leisure Retail across 300+ outlets. This is the discipline we know deepest. ### Agentic Reporting & Analytics Source: https://retter.io/solutions/agentic-reporting-analytics Ask your business anything, in plain words. Ask your business anything — no dashboards, no SQL, no BI tools. Chat with your analytics agent and get the snapshot in seconds — no BI tools needed. Need a new report or a new filter? Just ask. No tickets, no waiting on IT. Capabilities: - No BI tools: No dashboards to learn, no SQL to write, no BI tools. - Reports on request: New reports and filters on request — no tickets. - Checked against live data: Answers checked against your live data. How an engagement runs: 1. Connect: We connect your data sources into one clear picture. 2. Tune: The AI learns your terms, tested on questions you already know. 3. Run side-by-side: Two weeks next to your analysts, until the answers earn trust. 4. Roll out: Open it to everyone. Your data team moves from tickets to oversight. Proof: - 3–4 weeks to production - 100+ integrations with 3rd parties - 1 source of truth FAQ: **Q: How do you stop wrong numbers?** Three ways: fixed definitions, automatic cross-checks against known totals, and full visibility into every query. **Q: Which data sources can it use?** Data warehouses, ERPs, commerce platforms, and databases. Anything reachable by API or SQL, behind your access rules. **Q: Why is this live so fast?** Nothing gets rebuilt. The AI connects to data you already have. Most of the 3–4 weeks is testing. **Q: Do we need new BI tools or dashboards?** No. You ask in plain words and get verified answers. No dashboards to learn, no SQL to write, no new tools to roll out. **Q: Does the data team lose control?** The opposite. Access stays governed by your rules, every query is visible, and the team moves from ticket queues to oversight. ## Industries ### Grocery & Quick Commerce Source: https://retter.io/industries/grocery-quick-commerce Brands: istegelsin · Carrefour · A101 Real-time stock, fast delivery, and easy product search for grocery at scale. AI for grocery: live stock counts, fast delivery, easy shopping. Proven with istegelsin, Carrefour, and A101. Grocery is the hardest kind of commerce. Thousands of products, stock that changes by the minute, and delivery windows measured in minutes. Our AI makes the thousand small decisions all day. It keeps stock fresh, plans deliveries, and helps shoppers find things fast. We've done this at national scale for istegelsin, Carrefour, and A101. The 6 p.m. rush is just another minute. Challenges Retter solves: - Stock counts go stale fast across hundreds of stores → Our AI keeps counts fresh in real time. The app never sells what isn't there. - Fast delivery promises, limited couriers → The AI assigns and re-routes orders live, so promises hold. - Shoppers search in half-sentences → "Breakfast stuff for the weekend" becomes a full basket. Sales go up. - Thin margins leave no room for waste → Pay only for what you use. Cloud costs drop as much as 70%. Proof: - Millions of weekly shoppers served - 100k/s request volumes handled - 70% typical cloud cost reduction FAQ: **Q: Can you work with our existing grocery platform?** Yes. Most projects start on top of what you already run. A full build is optional. **Q: How do you handle demand spikes?** The platform scales itself, up to 100k requests a second. You pay for use, not idle servers. **Q: How fast can the first piece go live?** Small pieces like search or reporting ship in 3–6 weeks. Bigger systems take 6–8. **Q: Who do you run grocery commerce for?** istegelsin — millions of weekly shoppers on one platform — plus work with Carrefour and A101. National scale is our normal. **Q: How does AI improve grocery search?** Shoppers search in half-sentences. "Breakfast stuff for the weekend" becomes a full basket, and conversion and basket size go up. ### Marketplace & Omnichannel Source: https://retter.io/industries/marketplace-omnichannel Brands: Gratis · Lululemon One store across web, app, and shop floor, always in sync. Your web store, app, and shops work as one. Proven with Gratis and Lululemon. Most retail breaks between channels. The store doesn't know what the app promised. The customer feels like three different people to the same brand. Our AI fixes that. One brain watches stock, orders, and customers across every channel, and acts on what it sees. For Gratis and Lululemon, commerce finally behaves like one system, because underneath it is one. Challenges Retter solves: - Every channel has its own stock count → The AI keeps one live count across stores, warehouses, and marketplaces. - Customers look different in app, web, and store → One profile everywhere. The app offer works at the till. The store return finishes in the app. - Delivery rules can't keep up → The AI picks the best route for each order: from store, warehouse, or pickup. - Every new channel is a new project → New channels plug into the same backbone. Weeks to launch, not quarters. Proof: - 20M+ consumers served - 10+ countries - 100+ third-party integrations FAQ: **Q: We already have a store platform. Where do you fit?** On top. We connect what you already run. Nothing gets ripped out to start. **Q: How do you launch a new channel fast?** The backbone already has 100+ integrations. New channels reuse it, so launches take weeks. **Q: How does a project start?** A 1–2 week discovery, then a first build, live in 6–8 weeks. **Q: Who runs omnichannel retail on this?** Gratis, Turkey’s leading personal-care retailer, migrated live in under 5 months with zero disruption. MMI runs every brand and storefront on one backend. **Q: How do you unify customer identity across channels?** One profile everywhere. The offer shown in the app is honored at the till, and the return started in-store finishes in the app. ### QSR & Food & Beverage Source: https://retter.io/industries/qsr-food-beverage Brands: Starbucks · Paul · Costa Loyalty, mobile ordering, and engagement platforms for the world's most loved F&B brands. Mobile ordering, personal loyalty, and busy quiet hours. Proven with Starbucks, Paul, and Costa. In coffee and food, the app is the business. Ordering must be effortless, and every member should feel known. Our AI times each order so it's ready on arrival, and picks the offer that brings each member back. This is our home turf. We run Starbucks loyalty in three countries, Paul in nine, and work with Costa. 100,000+ transactions a day. Challenges Retter solves: - The morning rush overwhelms apps and stores → The platform scales itself, and the AI paces orders so promises hold. - Loyalty blasts treat a million members as one → The AI picks offer, timing, and channel per member, inside budgets you set. - Quiet hours, empty seats → The AI sends the right members the right offer to fill them. - Menus, currencies, and partners differ by country → One brain, local execution. It runs Starbucks in 3 countries and Paul in 9. Proof: - Millions of loyalty members - 3 countries — Starbucks platform - 9 countries — Paul platform FAQ: **Q: Can you connect to our store systems?** Yes. Tills, kitchen screens, and store systems are standard. 100+ integrations so far. **Q: We have an app. Do we have to rebuild it?** Usually no. The AI connects behind your app through APIs. If the app is the problem, we rebuild it in weeks. **Q: How do you prove loyalty ROI?** Every offer is a measured test with a control group. You see the extra revenue per campaign and per member. **Q: Which F&B brands run on your platform?** Starbucks loyalty in 3 countries, PAUL in 9, and work with Costa. 100,000+ transactions a day, zero downtime. **Q: Can it survive the morning rush?** Yes. The serverless platform scales itself through the spike, and ordering is paced against store capacity so promises hold. ## Case Studies ### Starbucks — One loyalty platform. Three countries. Millions of members. Source: https://retter.io/work/starbucks Industry: QSR & Food & Beverage Challenge: Loyalty across three countries usually means three platforms. Every change ships three times, and the experience drifts apart. Starbucks needed one platform that still feels local: one brain, with local payments, partners, and languages in every market. What Retter built — One loyalty platform, built to carry millions of members: - One member and rewards engine for all three countries - Mobile ordering connected to store systems in each market - Local payments and partners per country, without copies of the platform - Infrastructure that scales itself through campaigns and rush hours Outcome: Today the platform serves 750+ stores and 2M+ app users, with 50,000+ transactions a day and zero downtime. Cloud costs are down about 65%. New features ship about 10x faster, and one change reaches all three countries at once. Metrics: - 50K+ daily transactions - 65% lower cloud cost - 10x faster development ### Emirates Leisure Retail — CRM, loyalty, and wallet — modernized across 300+ outlets. Source: https://retter.io/work/emirates-leisure-retail Industry: QSR & Food & Beverage Challenge: 300+ outlets ran on an old, closed system. It was expensive, and every change was a fight. They needed a modern platform, and a live move that wouldn't disrupt hundreds of stores. What Retter built — A full move to AWS, on infrastructure that scales itself: - CRM, loyalty, and wallet rebuilt on AWS - Apple Pay and Apple Watch built in, end to end - Customer history moved live, nothing lost - Infrastructure sized by real demand, not worst-case guesses Outcome: Costs fell about 70%, and new features ship about 10x faster. Since launch, the team has spent nothing on DevOps. The platform runs itself across 300+ outlets. Metrics: - 300+ F&B outlets - 70% lower cost - 10x faster development ### PAUL — Digital F&B across nine countries. Source: https://retter.io/work/paul Industry: QSR & Food & Beverage Challenge: Nine countries means nine menus, currencies, and partner sets. And every channel must feel like the same premium brand. PAUL needed one platform that grows across markets without multiplying systems. What Retter built — One digital platform for ordering, loyalty, and engagement: - Mobile and web ordering in nine countries, with local menus and pricing - One loyalty program that follows the customer across channels and borders - Engagement that fits a premium brand - New markets launch in weeks on the same core Outcome: PAUL's digital experience runs on one platform across nine countries. New markets launch without rebuilding anything. Metrics: - 9 countries - Multi channels - End-to-end platform ### istegelsin — National-scale grocery commerce, end to end. Source: https://retter.io/work/istegelsin Industry: Grocery & Quick Commerce Challenge: Grocery is brutal: thousands of products, stock that changes by the minute, and a 6 p.m. rush that breaks normal systems. istegelsin needed live stock counts, fast delivery at national scale, and easy search for shoppers on phones. What Retter built — A national grocery platform built for real-time scale: - Live stock counts across stores. The app never sells what isn't there - Orders and deliveries planned against live courier capacity - Mobile-first shopping built for quick, frequent baskets - Infrastructure that absorbs the rush and bills for use, not idle servers Outcome: Millions of weekly shoppers order through the platform. Stock counts and delivery promises hold through peak hours, with a lean team running it all. Metrics: - Millions weekly shoppers - Real-time inventory - 100k/s request capacity ### Gratis — Omnichannel beauty retail, unified on one platform. Source: https://retter.io/work/gratis Industry: Marketplace & Omnichannel Challenge: Every peak season was a capacity project. Every channel was a separate integration. The goal: move a busy, live store onto a modern platform without disrupting the business. What Retter built — A full move of Gratis' web store onto the Rio platform: - Web store moved live in under five months - One backbone connecting online and store operations - Infrastructure sized by real demand, not peak guesses - New channels plug in instead of starting over Outcome: The move finished in under five months with zero disruption. Gratis ran a nationwide campaign just three days after go-live, on the same foundation that already powered its app for two years. Metrics: - <5 mo live migration - 3 days to first campaign - Zero disruption ### MMI — Award-winning beverage commerce on one backend. Source: https://retter.io/work/mmi Industry: Marketplace & Omnichannel Challenge: Multiple brands and storefronts usually mean a backend for each one. Experiences drift apart, and every new launch takes quarters. MMI needed one backend for every brand and channel, so new storefronts launch fast. What Retter built — One backend for every MMI brand and channel: - One backend for MMI e-commerce, apps, and regional platforms like MMI RAK - Personal taste profiles that tailor shopping to each customer - Fulfillment connecting online and in-store - Mircate's engagement engine built into the shopping flow - New storefronts, including Spinneys, launch on it in weeks Outcome: MMI was named Most Admired E-Commerce Retailer of the Year at the 2025 IMAGES RetailME Awards. Every brand runs on one backend, so a new storefront is a launch, not a rebuild. Metrics: - Winner RetailME 2025 - One backend, every brand - Omnichannel commerce ## Blog ### Why AI-Native Commerce Platforms Win Source: https://retter.io/blog/why-ai-native-commerce-platforms-win Published: 2026-05-28 · Category: AI Commerce Strategy Every commerce platform now claims to “have AI.” A chatbot here, a recommendations widget there. But there’s a structural difference between a platform with AI features and an AI-native platform — and that difference compounds. ### Features vs. foundations A bolted-on AI feature works against the grain of the system it lives in. The search engine doesn’t know what the loyalty engine knows. The pricing module can’t see what the recommendation model sees. Every integration is a seam, and every seam leaks context. An AI-native platform inverts this. Customer events — searches, orders, redemptions, abandonments — flow into one substrate that every capability reads from: - Search understands intent because it sees purchase history, not just keywords. - Recommendations adapt in real time because they share the same event stream. - Loyalty triggers the right offer because it knows the member’s actual behavior. - Pricing moves with demand because it isn’t waiting for a nightly batch job. ### Why this shows up in revenue The metrics that matter in commerce — conversion, basket size, repeat rate — are all functions of relevance. Relevance is a function of context. And context is exactly what seams destroy. When we rebuilt search for a grocery platform serving millions of weekly shoppers, the win didn’t come from a better model. It came from giving the model access to signals that previously lived in three separate systems. ### You don’t need to rebuild everything The good news: AI-native doesn’t mean starting over. Our enhance engagements add an intelligence layer on top of existing platforms — your ERP, your e-commerce stack, your loyalty system stay where they are. The layer unifies the events they emit, and capabilities like intelligent search or dynamic pricing go live on top of it in weeks. Enterprise-grade. Live in weeks. That’s the standard. ### Serverless Loyalty at Enterprise Scale: Lessons from 20M+ Consumers Source: https://retter.io/blog/serverless-loyalty-at-enterprise-scale Published: 2026-04-15 · Category: Serverless Architecture Loyalty platforms have a brutal traffic profile: near-zero at 4 a.m., vertical spikes when a campaign drops. Provisioning servers for the spike means paying for idle capacity 95% of the time. Provisioning for the average means falling over exactly when marketing needs you most. This is why we build loyalty engines on AWS serverless — and have for a decade, across brands serving 20M+ consumers in 10+ countries. ### What serverless actually buys you Elasticity that matches campaigns, not forecasts. A push notification to a few million members generates a wall of traffic within seconds. Lambda-based ingestion absorbs it without a capacity meeting ever happening. A cost curve that follows usage. Pay-as-you-go isn’t a slogan; it’s the difference between paying for transactions and paying for servers that might handle transactions. Across our deployments, that has meant cloud cost reductions north of 60%. Less undifferentiated operations. No fleet patching, no autoscaling group tuning, no 2 a.m. capacity pages. The engineering time goes into the loyalty logic — earn rules, tiers, redemption flows — not the plumbing under it. ### The patterns that matter A few hard-won architectural rules from running these systems in production: - Event-source everything. Points balances are projections of an event log, not rows you mutate. Disputes, audits, and replays become trivial. - Idempotency at the edge. Mobile clients retry. Networks duplicate. Every write path needs an idempotency key before it needs anything else. - Regional isolation, global identity. Members travel; data residency laws don’t. Keep identity global and transactional data regional. - Design for the campaign, not the steady state. If your architecture review doesn’t include “what happens when 5 million push notifications land,” it isn’t finished. ### Proven, not theoretical These patterns run Starbucks loyalty across three countries, Emirates Leisure Retail’s CRM, loyalty, and wallet across 300+ F&B outlets, and ordering platforms for brands like Paul across nine markets. 100,000+ transactions a day, zero downtime. If you’re building — or rescuing — a loyalty platform, this is the architecture conversation to have first. ### Agentic Reporting: Ask Your Business Anything Source: https://retter.io/blog/agentic-reporting-ask-your-business-anything Published: 2026-03-10 · Category: Agentic Analytics Every enterprise has the same reporting problem: the data exists, but the answer doesn’t. Dashboards answer the questions someone predicted when they were built. The question you have today — “why did basket size drop in Riyadh last weekend?” — means a ticket to the data team and a three-day wait. ### From dashboards to agents Agentic reporting replaces the dashboard-request cycle with an agent that has governed access to your data and the ability to do the work itself: - You ask in plain language. “Compare loyalty redemption rates across markets since the campaign launched.” - The agent plans the analysis. It decides which sources to query, joins what needs joining, and checks its own numbers against known totals. - You get an answer, not a chart dump. A direct response, the supporting figures, and the queries it ran — so analysts can verify instead of reconstruct. The difference from “chat with your data” demos is reliability. An enterprise reporting agent needs governed access controls, a semantic layer so revenue means the same thing in every answer, and an audit trail for every figure it produces. ### What this looks like in practice For commerce operators, the highest-value questions are operational and time-sensitive: - “Which stores will miss their delivery SLA this week at current courier capacity?” - “What did the price change on our top 50 SKUs do to margin, by market?” - “Which loyalty segments are quietly churning?” None of these are dashboard questions. All of them are agent questions. ### Why it ships in weeks, not quarters Agentic reporting is an enhancement layer — it connects to the warehouse, ERP, and commerce systems you already run. Nothing gets replatformed. A typical engagement: week one connects sources and builds the semantic layer, week two tunes the agent on your domain’s definitions, weeks three and four run it side-by-side with the analytics team before rollout. It’s usually the fastest agentic win in the portfolio — live in 3–4 weeks — because the data already exists. The agent just finally makes it answerable.