Building effective LLM-powered applications requires managing agent stability, optimizing information retrieval, and ensuring secure code execution, which can be complex and time-consuming. Existing solutions often lack the necessary tools for creating controllable agents and achieving state-of-the-art retrieval quality.

Pointable offers a platform for developing LLM-native products, providing tools to build stable agents and optimize retrieval systems. The platform's Bitdriver framework allows users to define agent goals and constraints, select from prebuilt tools, and automatically generate optimized agents. Stardriver technology identifies optimal retrieval pipelines for specific data, leveraging a Rust-based retrieval engine and bare-metal compute infrastructure. Pointable also includes Waterbear, a hardened Javascript runtime, to enable agents to safely evaluate code.

Bitdriver: Agent architect framework for creating controllable, stable agents by defining goals, constraints, and selecting from prebuilt or custom tools.

Stardriver: Retrieval pipeline optimizer that finds optimal configurations for specific data using a pure-Rust retrieval engine (Starpoint).

Waterbear: Hardened Javascript runtime, implemented in Rust, designed for secure and reliable execution of code written by Language Models.

Secure infrastructure: Services written in defensive, high-performance Rust, following security, monitoring, and scalability best practices.

Flexible deployment: Can be fully managed in Pointable's environment or deployed on major cloud providers.

Pointable targets organizations building LLM-native applications such as intelligent knowledge bases, process automation tools, customer-facing chat interfaces, and copilots.