This month at Xempla, we focused on strengthening the workflows that matter most — helping engineers collaborate more effectively, scaling the throughput of our autonomous maintenance pipelines, and laying the AI foundations for the next leap forward in facilities management.

The updates you’ll read below are all about reducing miscommunication, improving reliability, and preparing for AI-first delivery models in FM.

1. Task Management – Collaboration Without Friction

Investigations and triages often lead to scattered notes, missed details, or forgotten follow-ups. Engineers needed a way to capture discussions and revisit them later, without leaving their flow of work.

With Task Management, engineers can now create tasks directly from investigations, pin items for discussion, and return to them days later — all within the Xempla interface.

Think of it as a CRM for engineers: everything in one place, easy to find, and nothing missed.

• Problem solved: reduced miscommunication, fewer dropped threads.
• Value delivered: faster resolution cycles, less time wasted, and clearer collaboration.

2. Pipeline Improvements – Scaling Capacity and Reliability

As the scale of autonomous triaging grew, we faced a natural challenge: how do we handle more investigations without slowing down or losing accuracy?

This month, we upgraded the pipelines that power our agents. They can now process 50–60 triages per hour — more than 1,000 triages per day.

• Problem solved: bottlenecks in investigation throughput.
• Value delivered: faster fault detection, higher reliability, and readiness for larger deployments.
• Outcome: teams can manage more assets, with confidence in accuracy and consistency.

3. Local LLM Deployment – The AI Foundation

FM data is often siloed, fragmented, and underused. To unlock its full value, we’ve taken a major technical step: deploying LLMs locally within Xempla.

This is more than a milestone — it’s the foundation for the future of AI-first FM:

• Conversational reporting that turns raw data into live insights.
• Adaptive fault detection and diagnostics.
• Modular AI services built directly on top of asset performance and maintenance data.

By building this layer now, we’re paving the way for proprietary IP and AI-driven workflows that make FM more intelligent, connected, and outcome-focused.

🌍 The Bigger Picture

September’s updates were about boosting collaboration, scaling throughput, and setting the AI foundations.

• Engineers save time and avoid miscommunication.
• Pipelines scale to handle thousands of triages daily with greater reliability.
• The platform is ready for the next generation of AI-driven services in facilities management.

Xempla continues to connect autonomous maintenance with operational workflows — ensuring that facilities managers and engineers alike can focus less on process friction, and more on delivering measurable outcomes.

🔮 What’s Next (Roadmap Preview)

Looking ahead, here’s what’s coming over the next two months:

• Compliance & Finance Workflows → Bringing maintenance and operations closer together, with the first iteration going live in October.
• Search & Knowledge Hub 2.0 → An updated, conversational reporting layer that links insights directly to workflows and outcomes.
• Adaptive FDD Layer → Built on the DIIV framework (Discover, Investigate, Implement, Verify), enabling modular deployment and moving beyond traditional FDD approaches — including on-premise options without data transfers.

With each step, we’re moving closer to an AI-first FM delivery model — where autonomous maintenance, connected operations, and intelligent workflows come together to transform facilities management.