Weavable

Product Introduction

1. Definition: Weavable is a managed, cloud-based AI context orchestration platform. Technically, it functions as a persistent, live context layer that sits between a company's existing SaaS tool stack (like Jira, Slack, HubSpot) and AI agents (Claude, ChatGPT, Cursor).
2. Core Value Proposition: It exists to solve the problem of unreliable and inefficient AI agent outputs by providing structured, scoped, and continuously updated work context. Its primary value is enabling deterministic, accurate agent reasoning across real business workflows, leading to reduced token usage and more reliable AI behavior without constant data re-ingestion.

Main Features

1. Live, Continuous Context Graph: Weavable does not perform point-in-time queries. It maintains a real-time changelog across all connected tools, tracking entities (like accounts, tickets, deals), their changes, and crucially, their relationships. This means the context provided to an AI is a pre-built, connected graph of relevant data, not a collection of disconnected API snapshots.
2. Single MCP Endpoint with Scoped Access: The platform exposes one standardized Model Context Protocol (MCP) endpoint. This endpoint delivers pre-processed, scoped context to any MCP-compatible AI client (Claude Desktop, Cursor, etc.). Access control and data scoping are managed centrally in Weavable, allowing teams to share context securely without sharing credentials or managing per-user OAuth.
3. Zero-Maintenance Tool Integrations: Weavable handles upstream API changes, schema migrations, and tool updates (e.g., renamed Slack channels, restructured Jira projects) automatically. This ensures AI workflows built on the context layer remain stable without requiring manual maintenance or breaking when connected tools evolve.

Problems Solved

1. Pain Point: The "context window flood" from direct tool-to-AI connections. When agents pull raw data from multiple tools on every query, they are forced to sift through excessive, unstructured information on-the-fly, leading to inconsistent outputs, high token costs, and reasoning "drift."
2. Target Audience: Engineering teams building internal AI agents, product managers orchestrating cross-functional workflows, and operations teams in scale-ups/enterprises who need reliable AI assistance integrated with their core systems (Jira, GitHub, Slack, HubSpot, Salesforce).
3. Use Cases: An AI agent accurately summarizing the status of a key customer "Acme Corp" by pulling together and connecting the latest support ticket (Zendesk), recent commits (GitHub), deal stage (HubSpot), and internal discussion (Slack) into a single, coherent narrative without manual prompting for each tool.

Unique Advantages

1. Differentiation: Unlike direct MCP server connections or simple data connectors, Weavable is not an access layer but a context orchestration layer. It moves the decision of "what's relevant" from the LLM's runtime to a pre-processing stage, ensuring deterministic, scoped inputs. It also differs from vector databases by focusing on live, structured relational graphs rather than semantic search over historical documents.
2. Key Innovation: The core innovation is building context "on a changelog, not a snapshot." By continuously resolving entities and mapping relationships across tools before a query is made, it provides AI agents with a living understanding of business state. This approach of pre-building a cross-tool entity graph is what enables portability, consistency, and significant reductions in token usage.

Frequently Asked Questions (FAQ)

1. How does Weavable reduce AI token usage? Weavable reduces token usage by up to 90% by pre-processing, scoping, and structuring raw data from your tools before it reaches the AI model. Instead of the LLM processing every ticket, commit, and message to determine relevance, it receives only the context you've defined, formatted efficiently within a connected graph.
2. Is Weavable secure for enterprise data with SOC2 and HIPAA? Yes, Weavable is SOC2 Type II and HIPAA certified, with annual independent audits. It uses read-only OAuth scopes, never uses customer data for model training, and is designed to only access data explicitly included in a user-defined scope, making it suitable for sensitive enterprise and healthcare workflows.
3. Can I use Weavable with my existing AI setup in Claude or Cursor? Yes. Weavable is designed as a drop-in enhancement. You simply replace direct MCP server URLs with Weavable's single MCP endpoint. Your existing AI clients (Claude Desktop, Cursor, etc.) and workflows remain unchanged but now receive structured, live context instead of raw API data.
4. What happens if Weavable's service goes down? As a managed cloud service, its availability is critical. The platform's architecture is built for reliability. For business continuity, its read-only design means your underlying tools remain fully operational, and you could revert to direct, albeit less efficient, MCP connections if absolutely necessary.
5. How does Weavable handle data from new or updated third-party tool APIs? The Weavable platform team manages and updates the connectors for all supported tools. When an API changes, they update the integration to ensure continuity. This maintenance burden is absorbed by Weavable, so your defined contexts and AI workflows do not break due to upstream vendor changes.