Systematic hedge funds
Monitor strategy drift, portfolio interactions and capital weights across a multi-strategy book.
AI-NATIVE PORTFOLIO MANAGEMENT
Intelligence for the portfolio.
Prospect is building the decision layer for systematic hedge funds, quant teams and asset managers — continuously monitoring strategy behaviour, portfolio risk and changing market context to support explainable capital-allocation decisions.
Stage Pre-seedStatus Pre-product · Building V1Location London
PROSPECTILLUSTRATIVE ONLY
MODEL PORTFOLIO100.00
RISK WITHIN LIMITSALLOCATION STATEAdaptiveActive
PORTFOLIO RISKWithin limits5 / 5 checks passed
STRATEGY ALLOCATIONCURRENT
Strategy A42%
Strategy B29%
Strategy C18%
Cash11%
Allocation proposal updatedStrategy A reliability increased; concentration remains inside portfolio limits.
Illustrative product concept · No live client assets or performance are represented.
01Research
Standardise how strategies enter the portfolio decision process.
02Monitor
Track reliability, behaviour and regime context as evidence changes.
03Risk
Apply portfolio-wide limits around exposure, concentration and drawdown.
04Allocate
Turn evidence and constraints into explainable capital proposals.
THE THESIS
More strategies create a harder capital-allocation problem.
Systematic investment teams can research and deploy increasingly sophisticated strategies. The harder problem begins after a strategy exists: does it still deserve capital, how much, and what changes when the evidence weakens?
Backtests age. Correlations move. Drawdowns deepen. Market regimes change. Prospect is being built to make those decisions continuous, portfolio-aware and traceable rather than fragmented across disconnected tools.
MANY INPUTS. ONE PORTFOLIO.
MARKETLive context
STRATEGIESEvidence
AIDecision intelligence
PORTFOLIOCapital allocation
WHO IT'S FOR
Built for teams making repeated capital decisions across systematic strategies.
Prospect is designed for professional investment teams where strategy quality changes through time and portfolio context matters as much as standalone performance.
Quant teams
Connect research evidence to decisions that determine whether a strategy should keep, gain or lose capital.
Asset managers
Add a traceable intelligence layer around systematic sleeves, risk constraints and allocation review.
Proprietary trading firms
Standardise how approved strategies are monitored and compared once they move beyond research.
THE PRODUCT
A decision layer above approved strategies.
Prospect connects strategy evidence, changing market context, portfolio risk and capital allocation in one operating loop.
Strategy intelligence
Bring approved strategies into a common evidence framework so research quality, live behaviour and recent changes can be compared consistently.
Behaviour-change detection
Identify when a strategy begins behaving materially differently from its tested or recent operating range.
Regime-aware scoring
Combine longer-term evidence with volatility, drawdown, market context and regime fit into a changing view of strategy reliability.
Portfolio risk
Evaluate exposure, concentration, correlation and drawdown at portfolio level rather than strategy by strategy.
Adaptive allocation
Translate strategy scores and portfolio constraints into bounded, explainable capital-allocation proposals.
Decision traceability
Record what the system saw, why a score changed, which rule applied and how the portfolio response followed.
INITIAL WEDGE
Know when strategy behaviour changes — and what the portfolio should do next.
The first product focus is deliberately narrow: monitor the changing reliability of systematic strategies, place that change in portfolio context, and make the capital response clearer.
That wedge can expand into the broader Prospect operating system: research standards, portfolio risk, allocation, decision logging and controlled execution.
Start narrow. Build the decision layer. Expand with evidence.
INSTITUTIONAL BY DESIGN
AI where judgement changes. Hard rules where capital is at risk.
Prospect is designed so model output can inform strategy scores and allocation proposals without overriding explicit portfolio controls. The product should remain understandable, reviewable and recoverable as automation increases.
01Bounded outputs
Models propose within fixed ranges and eligibility rules.
02Hard risk controls
Exposure, drawdown and concentration limits remain explicit.
03Human approval
High-impact actions retain operator oversight during early deployment.
04Audit trail
Data, model versions, allocation changes and interventions are logged.
Initial deployment is intended as analytics and decision support. The customer retains discretion over capital deployment.
DECISION LAYERScoring · Market context · Allocation
PORTFOLIO RISKExposure · Concentration · Correlation · Drawdown
STRATEGY RUNTIMEResearch · Validation · Paper · Live
DATA PLATFORMMarket data · Strategy evidence · Audit records
V1 is designed to keep research, allocation, risk and live operation on one consistent platform.
WHY NOW
The bottleneck is moving from strategy creation to strategy selection and allocation.
Research can test more ideas, faster
As research workflows become more productive, teams face a larger set of strategies to compare, monitor and govern.
The portfolio decision remains fragmented
Research, risk and allocation often live in separate systems, leaving the decision about where capital should move under-connected.
AI only matters if the decision is trusted
For serious capital, intelligence is useful only when the inputs, rationale, limits and resulting actions remain visible and reviewable.
COMPOUNDING INTELLIGENCE
A decision system that can learn from the decisions it records.
The long-term advantage is not a single model. It is the structured history connecting strategy state, portfolio context, capital decisions and subsequent outcomes.
01ObserveStrategy behaviour + market context
→02DecideScores + portfolio constraints
→03MeasureOutcome + risk impact
→04CalibrateImprove the next decision
18-MONTH PLAN
Build, validate, then scale.
Prospect’s pre-seed plan is staged so each phase produces a testable operating system before the next layer is added.
Foundation
Common strategy format, data pipeline, reproducible research engine and basic portfolio-risk framework.
Multi-strategy
Several strategies in one environment, reliability scoring, behaviour-change detection and first allocation logic.
Paper validation
Continuous paper operation, decision logs, portfolio controls and live-vs-test monitoring.
Controlled live
Small live deployment to validate execution, system behaviour and the allocation process before meaningful scale.
BUSINESS MODEL
Enterprise software first. Expand economics as the platform becomes mission-critical.
Prospect is being built first as B2B institutional software, sold through platform contracts to professional investment teams. As validation deepens, the model can expand through additional modules and, where appropriate, capital-linked economics.
INITIAL BUYERSystematic investment teams
INITIAL REVENUEPlatform contracts
EXPANSIONModules + aligned economics
FOUNDER
Built from the strategy workflow upward.
Fred Boxer founded Prospect after independently building and operating automated systematic trading systems with AI-assisted development, then repeatedly confronting the same problem: once several strategies exist, the harder question is which deserve capital as evidence changes.
That experience led to the larger Prospect thesis — build the decision infrastructure around systematic strategies rather than another isolated strategy. The product direction is grounded in the workflow from research through risk, allocation and live operation.
Fred has built systems independently, worked through live trading constraints and secured Prospect’s first external angel backing. Prospect is now focused on turning that operating insight into institutional software.
Fred Boxer on LinkedIn ↗CURRENT ROUND
£1m pre-seed to build V1 and reach controlled validation.
The round is designed to fund approximately 18 months of product development, the first technical team, research/data infrastructure and controlled validation.
ROUND£1m
RUNWAY~18 months
STAGEPre-seed
TARGET MILESTONESWorking V1 · Design partners · Continuous paper validation · Controlled live deployment
CONTACT
Build the next layer of systematic investing.
FOUNDER & CEO
Fred Boxer
founder@prospectpartnership.co.ukLinkedIn ↗Investor, design-partner and founding technical-talent conversations are welcome.