InfraIntel
Decision support across the infrastructure investment lifecycle — the entry decision and, more importantly, the exit that nobody has a tool for.
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The problem
Infrastructure positions are held for twenty-five years or more, and risk accumulates across that life. The tooling, however, is concentrated almost entirely at the entry decision.
Exit timing — the decision that determines a large share of realised return — is generally made on committee intuition supported by spreadsheets and periodic consulting reports.
Where AI has entered this space, it has mostly produced confident narrative without traceable quantitative support. That is not a tool an investment committee can put its name to.
Entry decision
- Scenario simulation over the asset's operating life with explicit probability structure
- Adjusted present value analysis with the financing-structure gap quantified rather than assumed away
- Refinancing paths modelled as decisions, including the transition from bank debt to capital markets
- Sensitivity that shows which assumptions actually move the outcome
Monitoring and exit timing
- Live position monitoring against the case that was underwritten, not against a static plan
- Regulatory, market and counterparty events interpreted and mapped onto the quantitative model
- Exit-timing support for operational portfolios — the stage current products leave uncovered
- Evidence trail for every recommendation, in the form a committee can review
How we prove it before you pay
- Retrospective backtesting on closed deals you select, compared against what actually happened
- Parallel run on selected current positions alongside your existing process
- Commercial discussion only after the platform has performed against your own deal history
The architectural rule
Quantitative first, AI second
Quantitative models process financial metrics, scenario probabilities and risk indicators. The language-model layer interprets events only afterwards. No qualitative recommendation reaches you without quantitative evidence behind it — this is enforced in the architecture, not by policy.
Methodology you can check
The reasoning core rests on published financial methodology and Bayesian probabilistic reasoning, developed and defended academically rather than assembled from vendor claims.
Three competencies in one team
Wind turbine engineering, project finance and probabilistic modelling rarely sit together. Competitors are usually analysts without engineering depth, engineers without finance depth, or technology firms without the domain.
Questions we are usually asked
Which asset classes do you cover?
We start where our own expertise is deepest: wind, with offshore refinancing decisions as the first case. Solar, storage, hydrogen and transmission follow. We would rather be defensible in one domain than plausible in ten.
How is this different from the data providers we already pay for?
Data providers give you data and generalised models. This is decision support tied to your positions, with the reasoning exposed and the uncertainty stated — and it is demonstrated against your own closed deals before any commercial commitment.
What do you need from us to run a backtest?
The deals you choose and the documentation you already hold on them. The comparison is against outcomes you know, which is the only honest way to evaluate a claim like ours.
Test it on your own deal history
Pick a handful of closed transactions where you know how the story ended. We will show what the platform would have said at the time.
Request a backtest