Definable AI is a significant player in the orchestration and tool-use layers of the agent ecosystem. They provide the 'connective tissue' that allows LLMs to act as agents by giving them standardized access to over 1,000 APIs and a persistent memory layer through RAG. Their platform is essentially an environment for building and running autonomous workflows without the complexity of managing underlying agent frameworks.
They matter to the ecosystem because they are championing a 'single orchestrator' philosophy, which challenges the prevailing multi-agent role-play architecture. By focusing on production features like human-in-the-loop approval gates and scheduled execution, they are pushing AI agents away from experimental chat interfaces toward reliable enterprise automation. For developers, their repo-aware CLI provides a bridge between high-level chat agents and local development environments.
Definable AI is built on the premise that the current state of AI adoption is inefficient. For most power users and teams, work is scattered across a dozen browser tabs: ChatGPT for logic, Claude for long-form writing, Midjourney for assets, and Zapier to link them together. This fragmentation leads to high monthly costs and, more critically, zero shared context between tools. Founded in 2024 by Anandesh Sharma and Shubham Shukla, the Gurugram-based company aims to collapse this stack into a single interface.
The platform is structured around four core products that share a common credit pool and memory layer. The Definable Assistant is the primary orchestrator, providing access to over 50 models, including frontier models like GPT-4, Claude 3.7, and Gemini 2.0. Unlike a standard chatbot, it is built for action, connecting to over 1,000 external applications through OAuth2 to execute tasks like fetching leads from Apollo or updating records in HubSpot.
Contextual intelligence in Definable is handled through its Knowledge Bases. This is a RAG (Retrieval-Augmented Generation) system that allows users to ingest PDFs, live databases, Git repositories, and web URLs. These sources are not static uploads; they can be re-indexed on a schedule to ensure the AI's 'brain' remains current with the team's internal documentation or codebase. When the Assistant answers a query, it cites these sources directly, providing a clear audit trail for the information it generates.
For technical teams, Def Code CLI extends this intelligence to the terminal. It is a repo-aware developer tool that operates in four modes: Plan, Build, Design, and Test. It can analyze a local codebase, propose architectural changes, and generate unit tests based on the existing patterns in the project. This makes it a direct competitor to specialized AI code editors, but with the added benefit of being part of the broader Definable ecosystem.
Definable takes a distinct stance on the 'multi-agent' trend. While many startups focus on frameworks where multiple LLMs assume roles (like 'Manager' or 'Coder'), Definable argues that these frameworks are often too brittle for production due to role drift and cost. Their approach uses a single powerful orchestrator—a 'Strategist'—that selects the appropriate model for each individual step of a workflow.
This architecture is designed for stability. Workflows can be scheduled, gated by human approval steps, and audited through full execution logs. This focus on reliability has helped the company scale to a reported 40,000 teams within its first year. Data residency is another point of emphasis, with the platform offering hosting in India, the US, or the EU, catering to regional compliance requirements that larger US-centric providers sometimes overlook. By pricing their Pro tier at a competitive per-seat rate, Definable is betting that the convenience of a unified workspace will outweigh the loyalty users have to individual model brands.
Multi-model AI chat with 1000+ app integrations and multi-step workflow automation.
A repo-aware AI developer in your terminal with plan, build, design, and test modes.
Definable AI is hiring