Is the problem clearly defined, valuable, and aligned to leadership priorities?
How we work
A disciplined process, not a pitch deck
Every Praximind engagement follows the same structure: audit, design, build, operate. The detail below is intentionally an overview — the full scoring methodology and stage-by-stage deliverable specification are proprietary and shared directly with engaged clients.
Our methodology
The Four-Dimension
AI Audit framework
No dimension may be skipped — each guards against a specific, documented category of AI project failure. Skipping one to save time is how a short project becomes a long one.
Does the client have sufficient, accessible, clean, and lawfully-sourced data?
What systems, APIs, and infrastructure exist — and what is the real integration burden?
Does the client have the internal capacity and appetite to adopt and sustain the solution?
Guarded
Each dimension is scored against a proprietary rubric, and the resulting scores route to one of three outcome tracks — proceed, proceed with remediation, or not yet ready. We walk every client through their own scores and outcome track in the Audit itself, rather than publishing the scoring mechanics here.
Engagement model
From audit
to final report
Every engagement moves through the same named stages, each with a defined owner and a documented exit condition. This is not bureaucracy — it is how we prevent the most common project failure modes.
Stage 01Discovery
Readiness assessed across four dimensions; findings signed off before design begins.
Stage 02Design
At least two model options compared; HITL level assigned to every workflow the solution touches. No build until this is signed.
Stage 03Build
Sprint-based delivery with scheduled client demos — no black-box builds.
Stage 04Handover
Structured transfer of ownership, documentation, and training so the client is never indefinitely dependent on us.
Guarded
The detailed stage deliverables, sign-off checklists, and internal exit criteria are part of our client-facing methodology pack, shared once an engagement begins.
Human-in-the-loop
Oversight levels,
assigned before the build
Assigned during the Audit — not added after the build. Before any implementation begins, every workflow the solution touches gets a specific HITL level. AI decisions that carry consequence always have a human in the chain.
The system acts autonomously and logs its actions for human review. Used for low-stakes, easily-reversible tasks.
The system recommends an action; a human confirms before it executes. Used where consequences are moderate.
A human makes the decision; the system assists with information and options only. Used for high-stakes, rights-affecting decisions.
Want to see the full
methodology pack?
We share the detailed scoring rubric, stage deliverables, and HITL design worksheets directly with clients once an engagement begins.
Talk to us →