Recon is a foundational component for teams deploying AI agents in high-stakes environments like customer support and sales. By serving as an MCP (Model Context Protocol) server, it provides a standardized way for agents to pull verified, real-time context from a company's entire tool stack—including production databases and codebases—without manual engineering intervention.

In the broader agent ecosystem, Recon occupies the context and knowledge management layer. It addresses the reliability gap in Retrieval-Augmented Generation (RAG) by focusing on data integrity and conflict resolution. For developers, Recon provides a plug-and-play "company brain" that agents can use to cite their sources and resolve contradictions, making it a critical tool for those building agents that need to act on production reality rather than just static documentation.

The engineering bottleneck for context

In most B2B SaaS organizations, the most accurate information regarding customer health and product status resides in systems that commercial teams cannot easily access. When a support representative or sales lead needs to know why a specific account's usage dropped or if a promised feature is actually functional, they typically ping an engineer in Slack. This creates a triage layer where engineering talent is diverted toward answering SQL queries and digging through logs rather than shipping product. Recon is a customer-context layer designed to automate this retrieval process by connecting directly to the production tools where the answers live.

Founded by Pratik Kumar Jain, a former AWS engineer based in the Bay Area, Recon is the result of observing this dynamic firsthand. Jain spent years as the engineer who had to drop everything to investigate cross-system discrepancies. Recon is his attempt to formalize that investigation process into a single, queryable record. The company is currently in its early stages, offering a founder-onboarded early access tier for teams looking to consolidate their internal knowledge.

Verification over capture

The technical thesis behind Recon is that capturing data is no longer the primary hurdle for AI; the challenge is determining what is true. Traditional knowledge management systems often treat all documents as equally valid, leading AI agents to hallucinate or confidently state outdated information. Recon introduces a "verdict" layer that weighs competing claims. If a Notion doc from March says a feature is released, but three Intercom tickets from June report it as failing, Recon flags the belief as disputed. Every answer provided by the system includes a "receipt"—a direct link to the source line in GitHub, a specific Slack message, or a database record—ensuring that users can verify the claim themselves.

Recon integrates with a broad stack of thirteen tools, including Postgres, GitHub, Linear, Sentry, and HubSpot. This multi-source approach allows it to build what Jain calls a "customer-shaped record" that accounts for plan details, adoption rates, billing, and technical incidents. The system is read-only by default, requiring human approval for any write actions, which addresses the security concerns of connecting LLMs to production databases.

The Model Context Protocol bridge

One of Recon's most significant technical choices is its implementation of the Model Context Protocol (MCP). By exposing its verified company record as an MCP server, Recon allows tools like Claude Desktop or ChatGPT to access internal company context mid-conversation. This transforms the AI from a general-purpose assistant into a teammate with specific production knowledge. Instead of an agent re-deriving context for every individual task, it reads from a persistent memory layer. This structure potentially changes the economics of running agents, as they no longer need to spend tokens on a "treasure hunt" for data across multiple siloed APIs; the work is already synthesized and ready for consumption in the Recon record.