About Snipp

Snipp builds the infrastructure that lets brands run promotions, rebates, loyalty programs, and sweepstakes, and tie each one to a verified purchase rather than a self-reported claim. Its receipt and transaction validation technology, built over more than two decades, works across hundreds of retailers and points of sale, and its platform runs both one-off promotions and always-on loyalty programs for Fortune 500 brands including Nestlé, Kellogg's, Mondelez, P&G, Mars and Starbucks.

The challenge

Shoppers are increasingly likely to just ask an AI assistant directly instead of opening several different apps. But the shopper who wants to know whether a purchase earns them anything today still has to already know which app, card, or loyalty program to check, and reward types don't share a vocabulary: a grocery rebate, a retailer's points program, and a sweepstakes entry each live in a different place with different rules. Meeting ChatGPT and Claude's respective store review requirements meant solving problems a rough build can defer and a production app can't.

"AI is rapidly becoming the front door to commerce, with consumers increasingly turning to AI to discover products, compare options, and find the best value. With $nipp, we're extending our proven promotions and loyalty infrastructure into this emerging engagement channel, enabling brands to surface relevant offers, rebates, rewards, and loyalty experiences exactly when shoppers are making purchase decisions."

Atul Sabharwal, CEO, Snipp Interactive

The catalog itself is large: several thousand offers spread across eleven reward types, from cashback and coupons to sweepstakes and fuel points, pulled from many independent program sources and refreshed weekly. That breadth comes with some unevenness. Logo and banner art aren't standard across every source, some enrollment links needed validating before the app could rely on them, and because the catalog regenerates on a schedule, matching has to key off stable fields like program name rather than row IDs that shift between refreshes. A receipt upload adds its own constraint: only the merchant, items, and date could ever reach the matching logic, never a name or card number.

How it works

Snipp shows up as a single tool with two entry points, pre-purchase discovery and post-purchase claiming, depending on where the shopper is in their purchase. On ChatGPT, a shopper can also start a message with "@Snipp" to invoke it directly by name.

A shopper can reach it three ways:

• Before buying: a prompt like "what offers are there at Kroger?" or "I'm going to buy dog food and Pepsi at Walmart" returns a scrollable carousel of matching programs, ranked and ready to compare.

• After buying, described in words: "I just bought Tide and some Pampers at Target" returns the rewards still available to claim, as offer rows rather than a carousel to browse.

• After buying, from a receipt photo: no typing out every item. Snipp reads the merchant, date, and items first and shows a confirmation card before it matches anything, so the shopper checks the extraction rather than trusting it blindly.

None of this asks for an account or login; a shopper's first use works the same way as their tenth.

Every offer card carries the program name, the reward as the source lists it ("$1.00 off," "20% off," "5% cashback"), the reward type, and what it applies to, with an “Enroll” action that hands off to the program's own site to finish sign-up. Snipp doesn't create an account, credit a loyalty balance, or manage enrollment on the shopper's behalf. It finds and compares, then gets out of the way.

Shoppers can keep refining conversationally ("grocery only," "cashback, not points," "compare the first two") without restarting the search, and when the catalog itself has no real match, the app falls back to a general web search rather than returning nothing.

What Alpic built

Snipp came in with the reward catalog, the merchant relationships, and an early MCP prototype; Alpic's build team audited that prototype, rebuilt it to production standards on the Skybridge framework, and took it through submission on both platforms.

Turning a sprawling catalog into one tool

Rather than a different tool per reward type, the app resolves everything, discovery and post-purchase alike, through a single match-offers call parameterized by shopping stage, merchant, items, and category. That required mapping Snipp's raw program data into a structure the model could reason over consistently, deciding how to represent offers that arrive with no logo or no working redirect link instead of just dropping them, and deriving a reward-type icon system so eleven different reward categories still read as one coherent set of cards.

A receipt flow built around minimal data

The confirmation step between receipt upload and offer matching exists specifically so a shopper sees and approves what was extracted before anything is matched against it, and only purchase details, never a name or payment number, ever reach the matching logic.

One app, two platforms

Built on Skybridge, the app ships from a single codebase to both the ChatGPT Apps SDK and MCP Apps for Claude, with the same tool, the same offer cards, and the same catalog behind both. Snipp describes the underlying server as built specifically for shopper and loyalty marketing, adopting the same open protocol Alpic's own platform is built around.

Full-service submission and hosting

Alpic prepared the submission packages for the OpenAI Store and Anthropic Connectors Directory, where the app is now live for shoppers in the United States. The app runs on Alpic Cloud, with staging and production environments, Beacon audits ahead of each submission, and analytics into request volume and usage patterns as ChatGPT traffic starts arriving.