QED42 · PEOPLE × AI OS
How this started
We built People × AI OS on our own operations first
AI was already being used across how QED42 won work, delivered projects, ran meetings, and managed capacity.
The individual tools helped. The gains did not always carry through the full operation.
A faster task could still lead to more review. A useful meeting summary could remain disconnected from the work tracker. More prompts and tokens could show adoption without showing whether delivery improved.
So we built our own tools and connected them to the context behind the work.
Past engagements began informing new opportunities and estimates. Contracts were read against live milestones. Worklogs were compared with original estimates. Meetings were tied to the outcomes and decisions they were meant to move.
That became People × AI OS.
The framework was tested across QED42’s own Acquire, Deliver, Operate, Prove, and Adopt workflows before it became a consulting service.
The framework
Five motions, one operating model
Each motion connects the tools already in use with the people, context, and measures behind the work.
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Use what the organisation already knows to pursue and plan new work
Past engagements, capabilities, delivery benchmarks, and commercial data inform which opportunities to pursue and how the work should be estimated.
Inside QED42, RFP Radar, RFP Copilot, and Revenue Forecast support this motion.
RFP Copilot currently works from 247 internal benchmarks across 44 engagements.
Keep what was promised connected to what is being delivered
Scope, design, engineering, milestones, worklogs, and reporting stay part of the same operating context.
Inside QED42, Contract ↔ Milestone compares live delivery with the statement of work, while Event Horizon and Dashboard Automator support delivery visibility.
The value is not another status update. It is seeing drift while there is still time to act.
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Read the signals behind projects, meetings, capacity, and decisions together
Project activity, dependencies, risks, meetings, and portfolio priorities are interpreted against the outcomes they are expected to move.
Inside QED42, Op Intelligence reads the existing work-tracker log, while Meet Sense connects discussions and actions with the purpose of each meeting.
Measure whether AI changed the work, not only whether people used it
Original estimates, logged effort, review activity, AI usage, and completed work remain separate signals.
Inside QED42, Acceleration Efficacy compares estimated and logged effort at the work-item level.
In one measured set, 100 estimated hours were compared with 69 hours and 26 minutes of logged effort.
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Carry what works into everyday practice
Useful workflows, context, and working patterns are retained so more teams can use them without rebuilding the same knowledge.
Adoption is measured through the work and its outcomes, not by turning individual activity into a performance score.
Together, Acquire, Deliver, Operate, Prove, and Adopt connect how work enters the organisation, moves through it, is measured, and becomes repeatable.
How we work with you
People × AI OS is delivered as a consulting engagement, not a standard product implementation.
Start inside the operation
Understand the operation
Identify the first use case
Define the first pilot
Establish the measurement framework
Build the roadmap from real use
The engagement runs on a time and material basis, with the forward-deployed engineer working closely with one department throughout the first use case.
Process
How we work
We listen before we build. The partnership continues past launch
YEARS OF DELIVERY
Net Promoter Score
Drupal Diamond Partner
Understand
Technical debt, constraints, users need the things the brief doesn't say. That's where we start. Before a line of code is written
Build
Architecture, design, engineering — in cycles, with continuous client sync. No disappearing acts. Delivered in cycles, not in one go
Stay
Post-launch support, performance tracking, platform evolution. Results you can point to. The work doesn't stop at go-live
Integrations
Works with your existing systems
People × AI OS uses the tools, data, and workflows already in place, connecting only what the chosen use case needs.
Recently asked questions
Where should we start with AI agents?
How do we know if AI is improving the work?
How do we measure AI ROI?
Can AI agents work with our existing systems?
How can AI agents understand how our company works?
Where do people need to stay involved?
Why do AI projects fail to scale?
How long does it take to see value from AI?
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