Devplan Raises $2.5M to Automate Product Coordination Work AI Coding Tools Leave Behind

Devplan raises $2.5M to take on the product coordination work that AI coding is leaving behind

Devplan, a Seattle startup trying to automate away the meetings and status reports that eat up a product team's week, is coming out of stealth Thursday with $2.5 million in seed funding. The company, founded by two industry veterans with experience at companies such as Uber, Amazon, Snap and Meta, is also expanding availability of its software for coordinating work across product and engineering teams, after testing it quietly with a small group of customers. Devplan traces its roots to a youth soccer pitch, of all places, where co-founder Anton Safonov coached the daughter of his future business partner, Chris Bee. They had kids at the same school, and discovered they shared a frustration from running engineering teams: too much of the work didn't have anything to do with building the product. Bee calls it an invisible tax. "With AI fundamentally changing the way we do software development, we've seen this huge acceleration in coding and in engineering work," he said in an interview. "But the rest of the coordination work, the rest of that tax -- that 'work about work' -- hasn't changed very much, frankly." They co-founded Devplan in 2025, with Bee as CEO and Safonov CTO. How it works: Devplan's core product, Weaver, connects to commonly used tools including GitHub, Jira, Slack and meeting note-takers. A user can query Weaver through a built-in chat function, a Slack bot, or through a direct link into AI coding tools like Claude Code, asking about product status, features, or who's responsible for which aspects of the project, for example. Weaver also works in the background, generating a daily digest for each person and tracking projects on its own, flagging risks and progress without anyone filing an update. The idea is to avoid scheduling a meeting or creating a status report. Bee said queries run faster and cheaper through Weaver than pointing an AI tool at the raw data each time, because Devplan processes the information in advance and stores it in a knowledge graph rather than scanning code and documents on every request. He said queries run roughly twice as fast and more than three times cheaper on token costs in internal testing. Funding: The company's $2.5 million seed round was led by AI2 Incubator, with participation from Acequia Capital, Mighty Capital, Grand Ventures and eLab Ventures. Based at AI House on Seattle's Pier 70, Devplan employs six people, with a seventh hire in the works. The seed money is going toward engineering hires and deeper integrations, Bee said. Founder backgrounds: Bee was previously CTO of Lessen, where he helped grow the property-services company from a $20 million startup to a $2 billion valuation, and earlier led product and engineering teams at Zillow, Uber and Amazon. Safonov spent seven years as a principal software engineer at Snap, where he was a lead engineer on the company's infrastructure team, and earlier worked on systems at Meta that handled realtime traffic for Messenger, Facebook and Instagram. He also built systems at LinkedIn. "Chris and Anton have lived this problem at scale, and they have the technical depth to solve it," said Yifan Zhang, managing director at AI2 Incubator, in a statement. Competitive landscape: Devplan's bet is that a tool built specifically for product and engineering teams will outperform the general-purpose AI assistants. Glean, the enterprise AI search company, may be the closest comparison, Bee said, but it works more broadly across all of a company's information while Devplan goes deeper on software development specifically. Devplan integrates with Linear, the project-management tool, making it more of a partner than a rival, he said. Increasingly, given the capabilities of AI coding assistants, the competition also includes companies deciding to build a coordination tool in-house rather than buy one. Current status: Devplan has dozens of paying business customers on annual contracts, plus hundreds of users who have tried it so far. Pricing is consumption-based, with companies quoted a flat rate based on team size and expected usage. What's next: The company is focusing on enterprise customers for now, with plans to eventually open the product to individuals on a pay-as-you-go basis.

Devplan raises $2.5M to build an intelligence coordination layer for product development

Devplan raises $2.5M to build an intelligence coordination layer for product development Devplan Inc., a company building an intelligence layer for product development, today emerged from stealth with $2.5 million in seed funding led by AI2 Incubator. Acequia Capital, Mighty Capital, Grand Ventures and eLab Ventures also participated in the funding round. Devplan focuses on providing companies with a way to handle the dramatically increased speed provided by artificial intelligence-assisted software development in product development. The integration of AI into product streams has provided a paradigm shift for accelerating software creation. But the company argues it has not given way to an equivalent shift in coordination. Product and engineering companies currently spend hours gathering context, tracking progress, aligning teamwork and communication, making decisions and trying to handle increasingly fragmented tools. Devplan cited the Atlassian State of Teams 2026 report to show that Fortune 500 companies spend $161 billion annually reconciling this fragmentation. "After two decades leading product and engineering teams, I watched talented people lose half their week to coordination work that never resulted in a customer-facing update," said co-founder and Chief Executive Chris Bee. Founded in 2025, Devplan tackles this problem with what the company calls an "intelligence layer" for AI-native product development. Its product intelligence engine, Weaver, connects to all the tools that developers and product engineers use, including GitHub, Jira, Linear, Slack, Notion, Google Workspace, meeting notes and customer feedback, creating a shared knowledge graph. Knowledge graphs are a well-known industry tool used by intelligence layers to produce relationship representations of entities to enable both humans and machines to understand, reason and extract insights from complex, interconnected data. Unlike traditional flat and relational databases, which can struggle with highly connected datasets, knowledge graphs are becoming a best practice for querying networks of diverse information. Devplan said in a survey of early users and product managers, it received a report that they saved eight hours per week on coordination work. Additionally, internal benchmarking against a standard Claude configuration connected via Model Context Protocol showed that moderately complex queries took only about six minutes and cost $1.75. With its platform, the cost was reduced to two minutes and 56 seconds and a cost of 54 cents. Almost two times faster and more than three times cheaper. The competitive landscape for coordination, especially in the wake of AI agents, which act like additional people, has been filling up. Big players include Atlassian Corp. Plc. itself and its AI agent Rovo, which coordinates between a multitude of contexts - using the company's own report to speak to product is certainly a clever move; Snowflake Inc. and CoWork with enterprise data and GitHub Copilot that focuses on code. The category exists, but Devplan doesn't fit neatly into any one part. The company's vision is to provide product management with coordination, visibility and productivity - something that the coding side is already receiving from AI. The company said Axiad IDS Inc., an identity security company and dozens of other companies that focus on engineering, product and customer feedback. With this new funding, the company intends to expand its own engineering and go-to-market efforts, while actively seeking to partner with leaders seeking to embed AI into their core business operations.