Summary

• DeepSights MCP is generally available for compatible tools including Copilot, ChatGPT, Claude and custom AI agents.
• Responses are grounded in curated market intelligence and are designed to remain permission aware.
• The connection allows specialist research to enter wider AI workflows without requiring users to work inside a separate research application.

Market Logic Software is making its governed market intelligence callable from enterprise AI tools, using Model Context Protocol to connect licensed research and internal evidence with assistants and agents operating elsewhere in a company’s technology stack.

DeepSights MCP became generally available on 7 October and can connect Market Logic’s intelligence platform with compatible clients including Microsoft Copilot, ChatGPT, Claude and customer-built agents. Employees can therefore request market evidence from within an AI environment they already use rather than moving into a separate research application for each question.

Enterprise AI deployments have increasingly focused on connecting models to internal documents, databases and operational software, but market intelligence brings an additional governance constraint. Organisations often combine proprietary research with licensed material whose access rights do not disappear merely because an AI system is retrieving it.

Market Logic describes DeepSights responses as cited and permission aware, with the system drawing on the body of research curated for each customer. The design is intended to widen access without turning commercially licensed or sensitive research into an unrestricted knowledge store.

MCP becomes part of the control layer

Model Context Protocol provides a standard route through which AI systems can interact with external information and tools instead of requiring a bespoke integration for every combination of assistant and data source. The same interoperability increases governance exposure because every additional connection expands the information or functions an AI system can potentially reach.

DeepSights supports several levels of interaction. An assistant can ask a market question and return a cited answer, while more complex workflows can call specialist market intelligence as part of a broader automated task. The user does not necessarily need to know how to search the underlying research estate directly.

As delegation increases, source controls become more consequential. A person explicitly asking a question can inspect the evidence returned with the answer, whereas an automated workflow may decide independently when market information is required and then pass its output into another step.

That creates a chain in which research provenance can be lost unless each system retains enough context about where an answer originated. Market Logic’s design attempts to keep citation and curated evidence attached to the response, although enterprises will still need to assess how connected AI clients handle that provenance after the information leaves DeepSights.

Enterprise AI is becoming a network of specialists

The architecture reflects a wider shift away from expecting one general-purpose model to contain all the knowledge required for enterprise work. Organisations increasingly operate broad assistants alongside specialist systems that understand particular functions, datasets or controls, with protocols such as MCP allowing one system to consult another when required.

Market Logic positions DeepSights as the market intelligence specialist in that arrangement. Its platform can draw on research reports, proprietary studies and other curated material while employees continue working through AI products adopted more widely across the organisation.

Wider access could reduce the dependence on a small insights team to answer every routine market question, but it also changes the work of that team rather than removing it. Specialists still have to determine which research is credible, manage permissions and licences, curate information and resolve conflicting evidence.

Automated retrieval can make those upstream decisions more important because more people and software agents are able to act on the material. A poorly governed research repository becomes easier to query just as a well-governed one does, so the quality of the source estate remains separate from the convenience of access.

Market Logic has not provided independent evidence showing how much decision quality improves when DeepSights is accessed through another AI system, and general availability is a product milestone rather than proof of broad adoption. Enterprises will have to establish whether the integration actually increases research use without weakening licence controls or creating ambiguous provenance.

As more enterprise AI products are connected to one another, the decision shifts from choosing the strongest standalone model towards deciding which specialist systems can be trusted to supply context. DeepSights MCP is an attempt to make governed market intelligence one of those callable resources rather than another application sitting outside the main AI workflow.