portfolio-optimiser/shared/CONCEPT.md

The Concept

A plain-language description of what this project is and why it matters — written for a non-specialist, e.g. a business developer at another company. Framework-neutral: it describes the method, not any particular implementation.

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The problem

Many organizations run a portfolio of independent projects at the same time — construction projects, IT services, infrastructure works, production lines. Inside each individual project sit hidden cost savings: a material that could be substituted, a specification that is needlessly conservative, a procurement contract that could be renegotiated. Finding them requires an experienced specialist to study that specific project in depth — and that expertise is expensive and scales poorly across a whole portfolio. So the savings are left on the table.

Generative AI can suggest ideas, but a business developer immediately sees two obstacles: you cannot trust numbers a language model guesses, and the model does not know your industry's actual rules and experience. The concept is built precisely to remove these two obstacles.

How it works

For each project, everything known about it is gathered — project documents, the discipline's assessment methodology, relevant professional literature, and the hard constraints (budget, what cannot change, regulatory requirements) — into a curated, per-project knowledge base. It is built on an open, vendor-neutral standard (Google's Open Knowledge Format), so it is portable and not locked to a single vendor.

A team of AI agents reads this context, proposes concrete measures, and debates them against each other — one proposes, another critiques. But — and this is the heart of it — the agents are never allowed to decide the value themselves. Every number is sent to a separate, deterministic calculation engine (mathematical optimization plus risk simulation) that computes the actual saving. The AI proposes; the math decides. That is the trust anchor that separates this from "ask a chatbot."

The measures that survive the calculation are presented to a human domain expert who issues a verdict: approve, improve, or reject.

What makes it valuable over time

This is where the differentiation lies. The system learns from the experts' verdicts. There is almost always a gap between what a model computes and what an experienced specialist actually approves — because the expert knows how measures behave in practice, not just in theory. The system captures that gap and feeds it back, so the proposals get sharper on your organization's reality, not generic averages. The more verdicts, the better.

And it respects how experts actually work: sometimes they respond on the spot, other times it takes days or weeks. The expert simply places their assessment into a folder, and the system picks it up whenever it arrives. No requirement for real-time, and no one has to be standing by.

What it is — and isn't

It is a purely technical framework, not a finished compliance product. The organization that adopts it owns its own purpose, privacy, and governance; the framework only provides the technical preconditions (run locally, traceability on every proposal, no data leaving silently). It can run locally or in the cloud, and is published openly so others can adopt it.

Why it is built twice

The same concept is built on two different AI agent platforms — Microsoft's Agent Framework and Claude's Agent SDK — on exactly the same example, so they can be compared fairly. This gives both an open reference others can copy, and an honest basis for judging which platform fits the task best.

Concretely

Consider energy efficiency across a portfolio of buildings. Each building is a project with its own measure options (ventilation, lighting, insulation). The calculation engine computes the modeled saving for a package of measures under a budget. But an energy advisor knows that modeled and realized savings are rarely the same — behavior, measurement uncertainty, and interactions between measures create a gap. The expert's verdicts teach the system to close that gap over time. It is exactly in a field like this — where genuine expert judgment exists beyond pure calculation — that the concept comes into its own.