Hawl Technologies provides a centralized memory layer for AI assistants, enabling users to retain context across multiple tools without repetitive re‑explanations. Its Memory Box automatically captures conversation snippets and integrates them into future interactions, while the Control Box offers transparent chain‑of‑thought editing and confidence scoring so users can see and adjust how AI reaches its answers. The platform streamlines AI workflows for developers and end‑users alike.
Funding
Funding not disclosed
Founders
Product
Problem
Users interacting with multiple AI assistants must repeatedly re‑enter the same context, leading to fragmented conversations and reduced efficiency. Additionally, the lack of visibility into AI reasoning makes it difficult to trust and guide responses.
Solution
Hawl Technologies offers a centralized memory layer, Memory Box, that automatically captures and syncs conversational context across different AI platforms, eliminating the need for manual re‑entry. The platform enriches future interactions by recalling prior knowledge, enabling more relevant and coherent replies. Control Box provides real‑time transparency into the AI’s chain‑of‑thought, allowing users to edit reasoning steps and view confidence scores. Users can also restrict which data sources the AI may access, ensuring precise and controlled responses. Together, these tools streamline AI workflows and increase user confidence in generated outputs.
Target Audience
Primary customers are developers and product teams building AI‑driven applications, as well as power users who rely on multiple conversational agents for tasks such as research, productivity, or customer support.
Features
- Automatic capture of conversation snippets without manual saving or copy‑paste
- Cross‑assistant memory synchronization so context follows the user across ChatGPT and other models
- Context‑aware response generation that leverages stored memories for more relevant answers
- Editable chain‑of‑thought view enabling users to modify AI reasoning before final output
- Visual confidence scoring indicating the AI’s certainty for each response
- Data source control allowing users to specify which information the AI can draw upon