From functional capital tointelligent assets
In the generative-AI era the moat is no longer how thick your IT stack is. It is the density of structured functional capital. We do not sell heavy, hollow general-purpose systems; we turn the industry knowledge your firm has accumulated for years into visibility and execution that hold up in front of a large model.
Two things happening right now
Siloed information, invisible to AI
Decision-makers and high-net-worth buyers are moving to ChatGPT, Perplexity and the rest. A company whose digital assets were never structured does not come up in the answers — not ranked low, absent. We measure exactly that, month after month, on a frozen prompt set across several engines and phrasings, with the numerator and denominator printed beside every rate.
See the real measurement for a Hong Kong brand →The cost of moving tacit knowledge
Compliance review, due diligence and content distribution run on individual experience; off-the-shelf SaaS cannot fit non-standard work, so output per person hits a ceiling. Experience that stays in one person’s head cannot be called, copied or audited — the most expensive cost on the books, and the least measured.
See the pain points we found in real reviews →Not a system purchase — end-to-end delivery
Our agile build is run by a cross-disciplinary group of expert models, on a lightweight, low-code base, delivered end to end and judged on results.
- 01
Your workflow stays as it is — the system grows onto your process, not the other way round.
- 02
Plug in and run — prove one high-drain step first, then extend sideways. No six-month IT programme.
- 03
Paid on outcomes — every stage ends in something you can accept or reject, not a proposal nobody opens.
Reverse-engineering functional capital
Your know-how is sealed into a system that is yours, in three layers:
Cognitive slices and a knowledge graph
We leave your workflow alone and take what you already have — archives, past case files, content library — then build a knowledge graph that is specific to your industry through cross-disciplinary semantic mapping.
A light interface (MVP), built at pace
No six-month development cycle. At hackathon pace we ship a minimum viable product with a dashboard people can read. Zero learning curve: your staff call complex analysis in plain language.
A data flywheel that keeps calibrating
The system ships with a feedback loop. Every correction and judgement your staff make tunes it closer to how your business actually works.
Short steps, frequent proof
Never a long IT swamp. Each phase ends in a concrete output, and you can stop at the end of any phase.
- Weeks 1–2
Find the target
Pin down the step that drains the most human effort, or the business line that most needs AI visibility.
Output: a tailored Structured Functional Capital Assessment
- Weeks 3–4
Ship the prototype
The first vertical agent interface — a due-diligence assistant, or GEO work on one part of the business.
Output: a test that runs in your real environment, with a measurable change
- Months 2–3
Extend and harden
Extend sideways to more nodes and join the separate agents into one working flow.
Output: your own AI moat, closed as a loop
What we have built
This is a path we have walked, not a proposal: MEMECMO (a GEO platform for brands going abroad), Musicraphy, DirectorAI, a live-biologics brand system, a CRM for a Vietnamese out-of-home media group, and the Hong Kong GEO monitor — whose real scan data anyone can inspect.
See the full list →Start with one diagnosis
Two weeks, one Structured Functional Capital Assessment. Decide whether to continue once you can see the findings.