AI-driven optimisation of venture capital allocation in start-ups
Venture capital decisions are portfolio decisions. It is not the ‘best’ start-up that wins, but rather the combination of investments that, within the constraints of fund size, stage requirements and sector exposure, enables the highest expected total return whilst managing risk. StratePlan makes this selection explicit and reproducible as an optimisation problem.
Objective
Maximisation of the expected portfolio return potential whilst balancing risk and sector exposure across defined investment stages.
Assessment Inputs
- AHP-based Global Priority Score (integrates market, team, technology, traction, moat)
- Funding Ask (capital requirement per start-up)
- Stage (e.g. Seed, Series A)
- Sector (e.g. AI, Cleantech, Fintech)
Evaluation and selection mechanism
The AHP (Analytic Hierarchy Process) provides a consistent, comparable prioritisation across multiple criteria. This results in two robust decision-making paths:
-
Option A: Ranking + feasibility selection
Start-ups are ranked according to their AHP score; subsequently, a subset is selected that satisfies all constraints. -
Variant B: Optimisation with AHP utility function
AHP weights are converted into a utility function, and StratePlan directly calculates the optimal combination subject to constraints.
In both cases, the result is not a subjective shortlist, but a portfolio decision, which explicitly respects your rules: fund size, sector limits and minimum stage quotas.
Constraints
- Total Fund Size : 20 million USD
- Sector limit : maximum of 3 start-ups per sector
- Stage requirement : at least 2 early-stage investments (e.g. seed)
Result
- Optimal selection of investments within the fund size
- Controlled sector exposure (diversification rather than concentration risk)
- Explicit fulfilment of the stage strategy (e.g. seed quota)
- Transparent rationale: why these start-ups and why in this combination
- Reproducible decision-making logic for the investment committee and LP reporting
Technology
AHP structures the multi-criteria assessment (market, team, tech) into a global priority value. StratePlan uses this to calculate the optimal portfolio selection as a 0-1 optimisation problem subject to constraints (fund size, sector caps, minimum stage allocations). This transforms ‘deal flow’ into a quantitatively justified investment portfolio.
Common Patterns Across Cases
Assessment
Qualitative and quantitative factors are converted into comparable scores – using scales, valuation models or structured expert assessment. The aim is to establish a consistent, decision-ready evaluation basis.
Ranking
Elements are prioritised. However, ranking is rarely the final decision. In complex environments, prioritisation is frequently embedded directly into a combinatorial optimisation process in order to systematically account for interactions and constraints.
Group Selection
The final selection goes beyond a simple ‘Top-k’ approach. StratePlan solves structured selection problems such as knapsack, portfolio or scheduling models and calculates the optimal combination under real-world constraints.
Constraints
Constraints reflect real-world scarcity: capital, time, resources, risk appetite, regulatory requirements, strategic mandates or sustainability requirements. They are an integral part of the decision-making logic.
Technologies
Hybrid use of MCDA methods (e.g. AHP, TOPSIS) for structured evaluation combined with StratePlan for constraint-aware group or portfolio selection.
These cases demonstrate how StratePlan evolves decision-making processes from pure ranking to intelligent, constraint-aware portfolio construction. Evaluation data is translated into actionable, optimised group decisions – aligned with financial, strategic and sustainability-related objectives.
The underlying core logic – structured evaluation → quantitative prioritisation → constrained group selection – scales across different sectors and is adapted in each case to domain-specific success metrics and constraints.
