Problem

Okibi addresses the difficulty of turning repetitive business work into reliable AI automation. Corporate users spend roughly two hours a day on tasks that could be automated, while existing agent builders often rely on awkward drag-and-drop interfaces that do not capture the workflow users actually want. Building a dependable agent from scratch is also slow and technically demanding, requiring tool calls, LLM-syntax management, orchestration, browser automation, human-in-the-loop controls, evaluations, and hosting. Without a better approach, teams fall back on manual work, Zapier, Lindy, custom scripts, or fragile LLM prototypes.

The core use case is automating internal workflows that involve multiple systems, documents, and approval steps. Okibi has cited examples including identifying the right prospects, generating invoices from email and contracts, preparing for sales meetings, and producing pricing or proposals from meeting notes and existing agreements. These are economically attractive because they replace recurring administrative labor while reducing the maintenance burden of bespoke automation.

Product / Service

Okibi began as a natural-language web application for creating AI coworkers, but its current product positioning is a codebase-to-CLI platform. A user connects selected repositories through a controlled GitHub App, Okibi analyzes the code and drafts commands, then generates and verifies a versioned, documented CLI. The result is a compiled binary with typed commands and structured JSON output that AI agents can invoke directly in the terminal, allowing agents to use a company’s product through the same interface as its people rather than through a separate bespoke integration.

The delivery model is self-serve software with a free tier for one CLI and five generations per month, a paid Pro plan for teams, and custom Enterprise plans. Okibi’s stated benefit is to make internal software agent-ready without requiring users to hand-build integrations or manage the full infrastructure of agent deployment, while its verification process checks that the generated CLI builds, passes tests, and survives a live test drive before release.

Market

Okibi competes in B2B AI-agent, workflow-automation, and AIOps software. Its initial positioning overlaps with natural-language agent builders such as Lovable-for-agents, while its current CLI approach places it closer to infrastructure that lets agents operate internal software. Named competitors in market databases include Rasa, Sierra, and Hyro; Okibi’s own launch materials also identify Zapier, Lindy, custom scripts, and improvised LLM tools as alternatives customers use today.

The company is an early-stage Y Combinator Summer 2025 startup listed as active, and it reported working with 15 YC companies on workflow automation at launch. Third-party company databases report approximately $500,000 raised and a two-person team, indicating limited scale relative to larger agent platforms. Public revenue evidence is inconclusive: one database labels a 2025 seed record as “Generating Revenue,” but its current-revenue field is blank, so Okibi should be described as early commercial traction rather than definitively pre-revenue or revenue-scaled.