A two-person startup out of San Francisco thinks it can solve one of the oldest problems in commerce: the army of workers at wholesale distributors who spend their days copying numbers from emails into spreadsheets.
Whitespace, which went through Y Combinator's Summer 2026 batch, launched an AI platform in July that connects directly to distributors' existing software systems and deploys what it calls autonomous agents to handle tasks like order entry, inventory forecasting, and customer inquiries. The company targets an industry where manual data entry still consumes 30 to 50 percent of staff time, according to company-supplied estimates.
The pitch is straightforward, if ambitious. Whitespace's software integrates with major enterprise resource planning systems including SAP, NetSuite, and Microsoft Dynamics, then builds what the founders describe as a "living model" of how a particular business operates. From there, AI agents take over repetitive workflows while flagging exceptions that require human judgment.
"I have 6 reps typing 600 lines of orders from email into the ERP every day," one customer wrote in an anonymous testimonial accompanying the July launch. "Everyone is inside our ERP and spreadsheets, copying data from one place to another."
That sentiment appears common enough in the $60 trillion global wholesale distribution sector, where technology adoption has lagged other industries. Whitespace isn't alone in sensing opportunity here. A cluster of startups has emerged targeting similar pain points, including fellow Y Combinator companies Comena and Ventura, plus competitors like Canals.ai and FlowDemand. Epicor and SAP both rolled out AI agent features in 2026, potentially offering incumbent customers an easier path than switching to a third-party platform.
How the System Works
The platform's mechanics involve extracting orders from incoming emails and PDF attachments, validating them against price lists and inventory levels, then automatically posting approved orders to the ERP system. For inventory management, the software forecasts demand based on historical patterns and flags unusual spikes or dips that might warrant attention. A customer service module monitors email inboxes, pulls relevant data from the ERP, and drafts responses.
Whitespace integrates with productivity tools like Salesforce, HubSpot, Outlook, and Gmail in addition to the ERP systems. The company says most implementations go live within two to four weeks, though that timeline likely varies with company size and system complexity.
The founders claim productivity gains above 80 percent in key workflows, with customers typically recouping their investment within three months. Customer data gets stored primarily in the European Union with encryption, and the company's privacy policy states it doesn't use one client's data to train models for others.
The Team Behind It
CEO Alex Tung previously worked at Boston Consulting Group advising Fortune 500 manufacturers and distributors on technology strategy. He later joined Index Ventures-backed Ankar as its first non-engineer hire, helping scale revenue into seven figures within a year, according to Y Combinator's company directory.
His co-founder, Leon Yao, brings a different background. As a research engineer at InstaDeep, Yao led supply chain AI projects for large corporate clients. He published academic work on robotics systems as an undergraduate. "Growing up, my mum ran a small distribution business," Yao wrote on LinkedIn around the time of the launch. The personal connection to the industry's challenges seems to have influenced the company's direction.
So far, Whitespace lists two customers publicly. Kingfisher Direct, a UK distributor handling more than 35,000 industrial and commercial products, calls the platform a way to "free the team to focus on work that matters," according to co-founder Richard Martin. Warwick Fabrics, another UK wholesaler, describes the system as "a marvel" that "develops as it learns our business."
Whether that testimonial language reflects genuine transformation or early-stage customer enthusiasm remains to be seen. The company hasn't disclosed pricing, total funding beyond Y Combinator's standard investment, or how many additional customers it's signed.
A Crowded Field
The distribution automation space has attracted attention partly because the problem set is well-defined. Orders arrive in predictable formats. Inventory follows known patterns. Customer questions tend to cluster around a few categories. These constraints make the workflows more amenable to AI automation than open-ended creative tasks.
At the same time, established ERP providers have resources and existing customer relationships that startups lack. Epicor and SAP both rolled out AI agent features in 2026, potentially offering incumbent customers an easier path than switching to a third-party platform.
Whitespace's approach of integrating with rather than replacing existing systems may help here. The founders seem to recognize that distributors won't rip out ERP infrastructure they've spent years implementing. Building on top of those systems requires less organizational disruption, though it also means navigating the technical complexity of multiple integrations.
The company targets distributors across sectors including industrial supplies, electrical, plumbing, HVAC, building materials, foodservice, consumer goods, and textiles. That's a broad potential market, though it also means competing for attention across industries with different operational rhythms and pain points.
For now, Whitespace remains early enough that questions outnumber answers. The technology clearly resonates with at least some distributors tired of manual data entry. Whether it scales beyond that initial wedge will depend on factors the founders can only partially control: how quickly the AI learns new business contexts, how well it handles edge cases, and whether customers trust it enough to let the agents operate with minimal supervision.
In an industry where profit margins are often thin and operational efficiency determines survival, even modest productivity gains matter. The founders are betting that's enough.
