Introducing the new airfocus: The product intelligence platform built for alignment for the AI era

As AI accelerates engineering execution, the work that comes before delivery and coding – deciding what to build and why – has become the new bottleneck for product organizations. Connecting customer signals to strategic priorities, keeping teams aligned across multiple product lines, and making sense of an ever-growing stream of feedback are still largely manual tasks. The next generation of airfocus is designed to close that gap.

The launch introduces a suite of capabilities that embed AI across the airfocus platform: Connecting customer feedback, strategic priorities, and business objectives into one unified system and giving every team member, agent, and AI tool in the stack access to the same product context.

"The way product teams work is evolving rapidly. AI has made execution faster, but the primary bottleneck has shifted upstream to product management," says Malte Scholz, Head of Product and co-founder at airfocus by Lucid.

The teams that succeed will be the ones where humans work hand in hand with AI agents to stay aligned at scale: Connecting customer signals, strategy, and priorities so they can make better decisions about what to build and why it matters. That's exactly what this evolution of airfocus is designed to solve.

Malte Scholz

Head of Product and co-founder, airfocus by Lucid

New capabilities ship with the launch: airfocus agent (chat), Insights agent, and MCP server. Each addresses a different part of the upstream product workflow, but they share a single design principle: AI should be embedded in the platform itself, working from your real product context, not bolted on as a separate feature that operates from a blank slate.

Docs on items will also be released as part of the launch, allowing users to attach documents directly to roadmap items, co-authored with the airfocus agent. This ensures decision rationale and research live next to the work they inform. The context that used to walk out the door when someone left now remains in the system, visible and traceable to both humans and AI. 

How AI connects strategy, signal, and roadmaps

A key component of the launch is a conversational interface for exploring data across the platform. Product leaders can ask questions in plain language, synthesize trends across feedback and roadmap data, and generate summaries and recommendations without manually digging through documents and threads.

• The airfocus agent works because the underlying data is connected. Feedback links to opportunities. Opportunities link to delivery in Jira, Azure DevOps, or Linear, and roll up to initiatives. Initiatives roll up to OKRs. When the assistant answers a question, it draws on the same product context the team is working from, not an external dataset or a generic foundation model.

How AI agents automate customer feedback analysis

For most product teams, the volume of incoming feedback has long outpaced the time available to process it. Two new capabilities target that problem directly:

• The Insights agent analyzes large volumes of customer feedback to identify patterns and surface themes, connecting customer needs directly to product decisions. Every new signal gets categorized and linked to existing opportunities automatically, so the link between what customers are asking for and what the team is building stays current without manual triage.

How AI helps product leaders spot strategic drift

The third new capability targets a problem most product leaders know well: The gap between what was agreed at the strategic level and what teams are actually working on day to day.

• Strategic drift detection monitors product initiatives against company objectives and flags misalignment before it shows up in delivery outcomes. Rather than discovering at the end of a quarter that priorities and execution have diverged, leaders see the drift as it happens, with enough lead time to course-correct.

The capability is designed to work proactively. It surfaces risk, not just status, and it does so in the context of the broader portfolio, so teams understand not just that something has drifted but what else it might affect.

How the airfocus MCP server brings product context into external AI tools

The final piece of the launch extends airfocus beyond its own interface. 

• The airfocus MCP server provides secure, bidirectional access to structured airfocus data, including roadmaps, objectives, and priorities, from across external AI tools and copilots such as Claude, ChatGPT, and Microsoft Copilot.

Use cases include cross-system search inside Copilot Studio, daily briefing agents that combine airfocus with email, calendar, and chat, and roadmap-as-knowledge-graph workflows for AI agents that need access to product context to reason effectively.

The direction of data flow matters. Where some product tools have added MCP support as a client (pulling external data in), airfocus ships a full MCP server, pushing your product context out to every AI tool in your stack. That bidirectional flow is what turns airfocus from a workspace into a context layer.

Early feedback from our enterprise customers has been hugely positive. They're already using the MCP server, layering airfocus context into the AI tools their teams use every day. We are confident this is going to have a meaningful business impact for our customers, particularly the ones investing in AI transformation across their organization.

Malte Scholz

Head of Product and co-founder, airfocus by Lucid

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