Mason is an AI transformation platform that maps how your enterprise actually works, identifies where AI can create the most value, and helps deploy and improve the right solutions. Its Work Context Graph connects people, processes, systems, and agents so your AI investments are grounded in operational reality.

No integrations are needed to begin observing work. Team members can submit recordings while they work, with AI-led interviews and agentic data collection adding context. You can start understanding a workflow before connecting your systems. Any integrations needed to deploy an automation are scoped separately.

The Work Context Graph is a living map of how your enterprise runs. It connects the people, activities, artifacts, systems, and handoffs behind each process, including exceptions and workarounds. As new work is observed, the graph updates to show where time goes, where value is lost, and where AI can help against your KPIs.

Personal AI can help someone write, analyze, or code faster. But faster individual tasks do not automatically improve an end-to-end business process. AI adoption is not the same as AI transformation. Mason maps how work moves across teams, tools, and agents, then identifies where to deploy AI and measures its impact. It complements the assistants, point solutions, internal builds, and systems you already use, providing the operational context that connects them.

Owning your AI strategy does not mean rebuilding every layer of infrastructure. With Mason, you own your processes, data, and Work Context Graph. The hard part we handle is the intelligent infrastructure: turning recordings or scattered datapoints into reliable understanding, resolving actors, detecting handoffs, and maintaining a changing graph, and then evaluating recommendations against real business outcomes. Mason combines this infrastructure with our proprietary recommendation system, agent platform, and AI and industry expertise. All while optimizing cost, latency, security, trust, and quality at enterprise scale. With Mason, your team can focus on differentiated solutions while avoiding the ongoing cost of building and maintaining complex LLM research and infrastructure.

Yes. Mason is model-agnostic and designed to work alongside your existing AI tools, internal builds, and business systems. You can generate deployment blueprints for your own stack or deploy on Mason’s platform. We scope compatibility, access, and hosting with your team so you can build on the investments you have already made.

Yes. Mason can help deploy pre-tested automations on its model-agnostic, audit-ready platform, generate blueprints for your existing systems, or deliver a white-glove custom deployment in your environment. The deployment path depends on your workflow, controls, and operational requirements.

No. The right solution may be an AI agent, deterministic code, an existing ERP capability, a human decision, or a simpler process with fewer steps. Mason helps map each activity to an appropriate solution based on the work and your KPIs, with human review where judgment or control is required.

We establish a baseline and success criteria with your team, such as cycle time, cost per transaction, rework, or throughput. As new automations are observed, the Work Context Graph helps compare outcomes against that baseline and surface further improvements. The goal is measurable operational impact, not simply counting AI seats or tool usage.

New observations and deployed automations feed back into the Work Context Graph. That keeps the operational picture current as your teams, systems, and workflows evolve, helping flag improvements, risks, and changes in ROI. It is a living system, rather than a one-time process map.

Mason supports PII redaction, controls over which applications are observed, and deployment within your own environment. We scope data access and handling with your security team before an engagement begins. Our Security page and Trust Center provide further details for your review.

Mason focuses on workflow patterns and team-level process improvement, not individual productivity tracking. The objective is to understand handoffs, repetitive work, and bottlenecks so teams can spend more time on valuable work. Recording scope and privacy controls are defined as part of the engagement.

Start with a specific workflow, such as invoice review, reporting, or deal diligence. In the initial call, we identify the operational problem, the teams involved, and the outcome you want to improve. We then agree on scope, a baseline, and success criteria before expanding to additional processes.

Engagements are structured around the complexity of your needs, the requirements involved, and the scope of work. Pricing is typically discussed after the initial call, once we understand your operational requirements and the level of deployment support you need.