akta.pro launches company data and news signals API for AI agents

akta.pro, from Wokelo Inc., is rolling out a company data and signals API aimed at financial services and GTM AI agents. The platform adds entity-resolved company data, deduplicated news signals and alternative data on pay-as-you-go pricing.

Why it matters: - akta.pro is targeting a core problem for AI agents: clean, entity-resolved company data that can support research, diligence, monitoring and outbound workflows at scale. - The product is pitched for finance and go-to-market teams that need company lists, enrichment, news signals and alternative data without seat-based pricing. - Wokelo is positioning akta.pro as infrastructure for the “agent economy,” where software agents need structured company context to act reliably.

What happened: - akta.pro, built by Wokelo Inc., introduced its company data and signals API for AI agents. - The platform serves financial services teams and GTM teams with company search, enrichment, news monitoring and alternative data. - akta.pro says it has already signed marquee clients across Fortune 500 companies, private equity, venture capital and consulting firms. - akta.pro is backed by KPMG Ventures, Untapped Capital, Array VC and Geek VC.

The details: - The akta.pro Company Data API enriches records or builds profiles on 20M+ companies. - The company data set includes 74 fields across firmographics, business model, products, customers, technology, funding, financial estimates, management and M&A. - The akta.pro News and Signals API monitors companies, industries or plain-language topics such as tariffs or data center buildout. - Each article is deduplicated to one story per event, resolved to the companies it mentions, scored for impact and sentiment, and tagged across 77 event types. - Alternative data includes web traffic, headcount trends, job posts, social posts, employee reviews and product reviews. - Companies not yet in the database can be requested and added on demand. - Entity resolution links each company name, domain and subsidiary to one company ID, and customers can resolve their own data to that ID. - Financial services users include private equity, venture capital, investment banking, M&A, private credit and consulting teams. - GTM users include sales, RevOps, marketing and outbound teams using the data for TAM and ICP lists, CRM enrichment, buying-intent signals, sales triggers and account research briefs. - akta.pro also serves as the data layer behind AI SDRs.

Between the lines: - Wokelo built akta.pro after seeing that legacy data providers were priced for human analysts, not high-volume agent workflows. - The company also saw news feeds matching articles to the wrong companies, repeating the same story across publishers and lacking event tags. - That gap pushed Wokelo to build its own data layer before opening it to outside developers and data teams. - The product mix suggests akta.pro is competing on both data quality and machine usability, not just coverage.

What's next: - Developers can get an API key in the akta.pro playground and access full documentation online. - The akta.pro MCP server connects to Claude, ChatGPT, Cursor, VS Code and other MCP-compatible tools. - The akta-pro-cli package installs with pipx, uv or pip. - akta.pro offers pay-as-you-go, subscription and enterprise plans. - The company also points users to its benchmarks page, where akta.pro says its Company News Signals API ranked first for company news accuracy among 10 providers at the lowest cost per correct article. - In July 2026, the MCP server and CLI became available to all users. - In August 2026, akta.pro opened to the public and ranked No. 1 on Product Hunt on Aug. 25. - In September 2026, akta.pro launched its Agent plan for agent routers and AI-first platforms, with partners including Composio, Orthogonal, Monid and Boardy. - In October 2026, the official akta.pro app for ChatGPT went live.

The bottom line: - akta.pro is betting that AI agents need company data products built for automation first, with pricing and data structures designed around machine use rather than analyst seats.