AI Practice
We test AI in real workflows. We report what works, what doesn't, and what's worth building. The deliverable is a product spec, not a slide deck.
Our AI Practice is grounded in the Bloom's Framework — Jasem's public work on effective human-AI partnership.
What's included
A workflow audit, a pilot run in production, and a build-or-don’t verdict backed by evidence. You leave with a decision you can defend — not a demo.
Proven in
TeamSoul — a human and AI collaboration & knowledge surface
TeamSoul is Triden’s proprietary AI collaboration platform for the development cycle. It maintains architectural design, decisions and shared knowledge, tracing what humans and AI decided and why, with people reviewing and accepting AI contributions.
Collaboration with AI for developers
TridenITS delivered two successful Ministry of Defence (MOD) cohorts. The curriculum connects thinking with AI to building software, with human judgement and review throughout.
AI Business Analyst — requirements captured by voice
A self-serve tool that captures a real requirement over a voice conversation: an operator scopes a session and sends a link, a respondent talks to an AI business analyst, and the system returns a structured requirements report — with the initiator notified when it's ready.
Farm Management AI — IoT, AI decisioning, marketplace
A unified Farm Management platform — from IoT plant management to AI-driven decisioning to marketplace. A Triden internal build, with a real trial site running on a real farm.
How an engagement runs
Starts with a free Discovery sprint, then a pilot in a real workflow. Take the verdict to your team, or we stay on and build what it points to.
We don't ask you to trust the AI. We make its work checkable.
The practice runs on one line — the point where a model's output stops being verifiable against anything real. We call it the auditability boundary, and we keep it visible: the consequential calls stay above it.
- Below the line — grounded.
- Recall, explanation, drafting, analysis. Each is checkable against something real — a source, a runtime, the spec you just approved. If it's wrong, we catch it; the safety isn't the model's confidence, it's that a referent exists.
- Above the line — yours.
- Judgment, values, and the build, buy, or unwind decision. There's nothing to check these against but your context and your stakes — so they stay with you. Every deliverable names its assumptions, its risks, and the test we hold ourselves to: what would change our mind.
- The line moves with the stakes.
- Reversible and low-stakes, the AI does more. Binding, novel, or expensive to verify, the line rises and human review expands. One question sets it every time: if this output is wrong and we act on it, what breaks?
- The outcome is a governed decision.
- Not a slide deck — a product spec you can take to a specialist with informed questions, proven on real engagements like the AI decisioning we govern on a live farm in Farm Management AI.
We'll teach it, too.
TridenITS delivered two successful Ministry of Defence (MOD) cohorts on collaboration with AI for developers. Training is on demand for special projects only.