One Model. Every Portfolio Company. The Same Definitions.

Apply one model, one set of definitions, across every portfolio company, that's the core function Orbicul is built for, since inconsistent definitions across companies make any cross-portfolio comparison meaningless no matter how much reporting each individual company produces. A fund with ten companies reporting in ten formats doesn't have a portfolio view; it has ten separate views that happen to sit in the same folder.

Orbicul combines four value layers, financial performance, strategic direction, risk & opportunity, and ESG & sustainability, and eight business domains into a single, comparable model per company and aggregated across the fund. The point isn't just centralising data that already existed elsewhere; it's applying the same structure and definitions to every company so the aggregated fund-level view is actually a real comparison rather than an average of incompatible numbers.

Score every company across all eight domains, market reality, growth, delivery, customer lifetime, operations, human & machine, external leverage, innovation, not just the P&L, since EBITDA alone says nothing about whether the drivers behind it are sustainable or fragile. Two companies with identical EBITDA can have very different underlying health once scored across all eight domains, and that difference is usually exactly what an exit process will surface anyway.

The eight Orbicul domains, each covering a distinct set of value drivers most reporting packages never isolate, from customer lifetime value and innovation pipeline strength to how dependent operations are on a small number of key people. Most standard financial reporting captures the outcome these drivers produce without ever measuring the drivers themselves, which means problems in them typically aren't visible until they've already shown up in the P&L.

Ground the prediction in sector data, comparable transactions, and macro factors, updated continuously rather than fixed as a static benchmark set once at acquisition and never revisited. Markets, comparable transaction multiples, and macro conditions all move over a typical hold period, and a multiple prediction that isn't updated against current data is really just describing conditions at some earlier point in time.

Because reporting is typically financial and lagging, Orbicul's risk & opportunity layer surfaces operational signals before they hit the numbers, which is usually a full quarter or two ahead of when the same problem would show up in standard financial reporting. By the time a problem is visible in the P&L, the operational cause has often already been developing for months, unmeasured because nobody was tracking that specific signal.

Build every plan on the same eight-domain model so quality and completeness become comparable across companies, rather than allowing each portfolio company's management team to define its own plan structure and level of detail. Without a shared structure, a fund has no reliable way to tell whether one company's thin-looking plan reflects a genuinely simpler situation or just a less rigorous planning process.

Tie KPIs directly to the domain and layer they influence in the model, rather than tracking them in isolation as a general operational dashboard disconnected from valuation. A KPI that isn't mapped to a specific domain is difficult to prioritise against another KPI in a different part of the business, mapping both into the same model makes it possible to compare their actual impact on value, not just their movement.

A continuously updated valuation, driven by monthly actuals against the company's own trajectory rather than an annual point-in-time exercise typically only revisited around fundraising or exit events. Because it updates monthly, the valuation reflects what's actually happening in the business right now, rather than a snapshot that's already several months stale by the time anyone in the fund next looks at it.

Aggregate domain scores across companies, a shared weakness shows up as one fund-wide theme instead of ten separate problems that each look isolated when viewed one company at a time. A weakness in, say, key-person dependency or pricing power that shows up across half the portfolio is a single strategic issue worth addressing at the fund level, not ten unrelated coaching conversations with ten different management teams.

Use the valuation bridge to see which domain and layer is adding or blocking value, per company and aggregated across the fund, rather than relying on management commentary alone to flag where problems might be developing. The bridge turns 'something feels off in this company' into a specific, ranked answer about which domain is actually driving the concern, which is a very different starting point for a conversation with management.

Rank value at risk by domain and company, the model answers with numbers where sentiment alone can't, replacing the instinct-driven prioritisation that usually determines which portfolio company gets the most attention from an operating partner in any given quarter. A ranked, evidence-based intervention list also makes it far easier to defend resourcing decisions to the rest of the investment committee than a purely qualitative judgement call.

Score it as one of the four layers, against ESRS or existing ESG data points, feeding directly into the valuation bridge rather than existing as a separate compliance report disconnected from the rest of the value story. Treating ESG as its own isolated workstream is how it ends up seen as a cost centre; folding it into the same valuation model as the other three layers makes its actual financial relevance explicit.

Add the risk & opportunity layer, which draws signals from operational KPIs and structural exposures before they surface financially, giving management and the fund a preview of where financial performance is headed rather than only a record of where it's already been. This is the layer specifically designed to answer the question standard financial reporting structurally can't: what's about to happen, not just what already did.

Track it inside the same model used for ongoing performance, so thesis and execution stay in the same conversation instead of drifting apart the way they typically do once the deal team moves on to the next transaction. A deal thesis that isn't tracked inside the operating model tends to become a document nobody revisits until exit, at which point it's too late to notice it stopped matching reality years earlier.