Appspace, the San Diego‑based workplace experience platform, revealed a new AI‑driven product line at the Gartner® Digital Workplace Summit on March 23, 2026. The company calls the offering the Custom Assistants Framework, a developer‑oriented engine that promises to turn internal data sources into actionable, persona‑focused assistants without the months‑long development cycles typical of traditional AI projects.
A shift from generic bots to configurable agents
Instead of layering a one‑size‑fits‑all chatbot on top of existing tools, Appspace positions the framework as a “native” component of its platform. According to Thomas Philippart de Foy, Appspace’s chief design officer, “We are moving past the era of chat and into the era of agency.” He adds that customers need an “engine that anchors digital workflows within the physical and digital office,” and that the new framework “gives organizations the power to solve their own specific business problems in minutes, not months.”
The claim is bold: teams can reportedly spin up a functional workplace assistant in under 30 minutes. If accurate, that timeline would undercut the typical enterprise AI deployment cycle, which often involves weeks of data preparation, model fine‑tuning, and integration work.
Architecture anchored by the Model Context Protocol (MCP)
At the core of the framework lies the Model Context Protocol (MCP), an open integration standard backed by major AI players such as Anthropic, Google, Microsoft, and OpenAI. MCP acts as a bridge between Appspace’s internal data (occupancy sensors, digital signage content, intranet activity, employee roles, and project assignments) and external enterprise systems like Workday, ServiceNow, and Jira.
By exposing a common interface, MCP enables assistants to not only retrieve information but also trigger actions—for example, opening a cleaning ticket in ServiceNow when a floor’s occupancy drops below a threshold. This bidirectional capability distinguishes the framework from many “information‑only” bots that stop at surfacing answers.
Real‑world use cases illustrated at the summit
- Policy Pilot – An HR‑focused assistant that delivers location‑ and role‑specific policy answers instantly, eliminating the need for manual support tickets.
- Smart Floor Concierge – A facilities‑management agent that monitors space utilization, automatically closes underused floors, updates signage, and creates service tickets for cleaning staff.
- Workplace Navigator – A personal assistant that confirms desk reservations, guides employees to meetings, and aggregates relevant content for the day upon arrival.
These examples highlight a consistent theme: role‑specific automation that reduces friction for both end users and operational teams. The ability to configure such assistants quickly could accelerate the adoption of AI across traditionally siloed departments.
Competitive positioning and market implications
The framework’s reliance on an open protocol gives Appspace a defensive edge against vendor lock‑in—a frequent concern for enterprises wary of committing to a single AI stack. By supporting multiple large‑language‑model providers through MCP, organizations can swap underlying models without rewriting integration code.
From a market perspective, the announcement signals a maturing of AI in the workplace segment. Earlier generations of enterprise AI tools have largely been “add‑on” services that sit atop existing platforms. Appspace’s approach—embedding AI directly into the core data layer—mirrors a broader industry trend toward AI‑native architecture, where context and action are inseparable.
Availability and next steps
Appspace plans to showcase live demos of the Custom Assistants Framework throughout the remainder of the Gartner Digital Workplace Summit. Attendees will be able to see how raw enterprise data can be transformed into immediate, actionable workflows.
While the company has not disclosed pricing or a general release timetable, the emphasis on rapid configuration suggests a self‑service model that could appeal to both large enterprises and midsize firms looking to experiment with AI without heavy upfront investment.
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