Jakit Labs is an active participant in the agent ecosystem, specifically providing the engineering and infrastructure required to make agents functional in production. They build voice AI agents and the agentic workflows that allow LLMs to take actions in digital environments. Their work with Superagent, a platform for building AI agents, suggests they are deeply involved in the infrastructure that powers the next generation of autonomous tools.
They matter to the ecosystem because they solve the "production gap." While many companies can build a basic agent, Jakit Labs provides the expertise in RAG search, document processing, and GPU scaling needed to make those agents reliable at scale. They are also unique in pushing the agent concept into the physical world, exploring how autonomous intelligence can be applied to factory vision and robotics, which expands the potential scope of the agent stack beyond the browser and the terminal.
Jakit Labs operates as an engineering firm focused on the deployment of production AI systems. The company divides its work into two primary verticals: Agentic AI for digital environments and Physical AI for industrial settings. This split is uncommon in an ecosystem where most firms choose one or the other. By maintaining expertise in both, they apply the fast-moving software patterns of LLM agents to the high-reliability requirements of industrial automation. Their work includes the development of voice AI agents, agentic workflows, and semantic search engines, alongside physical solutions like factory computer vision and robotic systems.
A central theme in the Jakit Labs approach is the belief that AI systems are only as good as the infrastructure they run on. They do not just build models; they build the plumbing. This is evidenced by their work with Superagent, where they built core document processing infrastructure from scratch. Their expertise extends to Kubernetes orchestration and bare-metal GPU deployment, which they have used to reduce operational costs for clients while maintaining enterprise-level reliability. For companies moving past the wrapper phase of AI development, this focus on the underlying compute and deployment stack is a significant differentiator. They provide the technical depth required to manage NVIDIA GPU clusters and terraform cloud environments specifically for AI workloads.
The company has established itself as a reliable partner for key players in the AI agent and infrastructure space. James Briggs of Aurelio AI and Ismail Pelaseyed of Superagent are among the clients who have credited Jakit Labs with building their core search and document processing systems. These are not typical corporate clients; they are technical founders building the tools that other AI developers use. This suggests that Jakit Labs operates at the frontier of the stack, solving engineering problems that are too complex or specialized for in-house teams to handle during rapid scaling phases. Their portfolio also includes work for Thallo and FastMedical, indicating a broad application of their RAG and infrastructure expertise across sectors like climate tech and healthcare.
Jakit Labs markets itself on the ability to ship actual software rather than just advice. Their engineering team, which operates remotely, focuses on implementing APIs, dashboards, and integrations that turn models into usable products. They utilize a modern stack comprising Python, PyTorch, FastAPI, and Vector Databases for AI work, paired with TypeScript, React, and Node.js for the user-facing components. This end-to-end capability allows them to take a client from a basic idea to a production-ready AWS or GCP landing zone with automated CI/CD pipelines. For the industrial vertical, this translates to deploying edge intelligence and computer vision solutions that can operate within factory constraints, further highlighting their focus on real-world utility over theoretical research.
Intelligent systems for the digital world including voice agents and agentic workflows.
Jakit Labs is hiring.