Software company Pipe17 raised $15.5 million in a Series A funding round to support its efforts to build and scale its infrastructure for artificial intelligence-powered composable order operations.
The company’s AI-powered Order Operations platform integrates systems and synchronizes data flows to support omnichannel selling and fulfillment, Pipe17 said in a Friday (Jan. 10) press release.
“We are at a pivotal moment in commerce, where businesses are under immense pressure to adapt to an increasingly complex and fragmented landscape,” Pipe17 CEO and co-founder Mo Afshar said in the release. “Pipe17 is uniquely positioned to address these challenges and shape the future of commerce.”
Pipe17’s platform is designed to help retailers provide the consistency and delivery consumers expect when they shop across channels ranging from TikTok to Amazon, according to the release.
To do so, the platform synchronizes order, pricing and inventory data in real time across enterprise resource planning (ERP) systems, third-party logistics (3PL), commerce platforms, marketplaces and stores, the release said.
The platform also features an AI Order Operations Agent called Pippen that assists users with tasks like order routing, administration and exception handling, per the release.
“By working with Pipe17, businesses can leverage the platform and AI innovations like Pippen to sell across channels with precision, greater agility and reduced operating costs, all while delivering exceptional customer experiences in today’s dynamic market,” the release said.
About a third of United States consumers are Click-and-Mortar™ shoppers who choose experiences that work convenient digital features into the in-store shopping journey, according to the PYMNTS Intelligence report “2024 Global Digital Shopping Index: U.S. Edition.”
The report also found that 19% of U.S. consumers prefer to shop in stores with the assistance of digital technologies, 11% prefer to make purchases online for in-store pickup, and three-quarters want to be able to use their preferred payment methods.
The lines between online and offline shopping continue to blur as direct-to-consumer (D2C) brands adopt omnichannel strategies that offer consumers a seamless experience across multiple touchpoints, PYMNTS reported in December.
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Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
