Systematic hedge funds
Monitor strategy drift, portfolio interactions and capital weights across a multi-strategy book.
AI-NATIVE SYSTEMATIC INVESTMENT INTELLIGENCE
Intelligence for the portfolio.
The intelligence and decision layer between systematic strategies and capital.
01Understand
Encode strategy identity, thesis, family, dependencies and evidence provenance.
02Measure
Track strategy-specific behaviour, reliability and statistical change.
03Context
Interpret evidence against changing market and regime conditions.
04Portfolio
Model correlation, concentration, common exposures and diversification.
05Allocate
Generate constrained, explainable capital-allocation scenarios for human review.
THE THESIS
The hard problem is not producing another strategy. It is deciding what deserves capital.
Systematic investment firms can research, deploy and operate more strategies than ever. The decision problem grows faster: what is each strategy actually doing, what changed, what is caused by the market environment, how does it alter the portfolio, and what capital response is justified?
Prospect is being built as the intelligence layer that answers those questions continuously. Monitoring is one primitive inside that system; the product is the portfolio-level decision infrastructure connecting strategy evidence to capital.
STRATEGIES → EVIDENCE → CONTEXT → PORTFOLIO → CAPITAL
STRATEGIESThesis + dependencies
EVIDENCEMeasured behaviour
PROSPECTPortfolio intelligence
CAPITALHuman decision
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
One intelligence layer across the portfolio.
Prospect turns strategy definitions, measured evidence, regime context and portfolio relationships into explainable allocation intelligence. The portfolio is the first-class object, not a list of independent alerts.
Strategy understanding
Represent each strategy as more than a return series: its family, thesis, expected edge, dependencies, intended behaviour, research basis and data lineage.
Strategy intelligence
Measure generic and strategy-specific evidence, behavioural fingerprints, drift, statistical significance, uncertainty and changing reliability.
Regime intelligence
Model the market conditions a strategy depends on and distinguish strategy deterioration from a change in the environment around it.
Portfolio intelligence
Understand correlation, concentration, common exposures, diversification, capacity and how changes in one strategy affect the whole book.
Capital-allocation intelligence
Combine strategy evidence, regime compatibility, portfolio effects and hard constraints into explainable allocation scenarios with reasons and uncertainty.
Institutional decision memory
Record the evidence, model/config versions, human decisions and subsequent outcomes so the system can become more useful and institution-specific over time.
CURRENT EXECUTABLE SLICE
Reliability is the first working capability inside the wider intelligence layer.
The current build can ingest strategy histories, validate data quality, create behavioural fingerprints, detect meaningful change and produce traceable STABLE / WATCH / REVIEW evidence.
That capability is being extended directly into strategy specifications, regime context, portfolio relationships and explainable allocation scenarios. It is not a standalone monitoring product and it is not the end-state of Prospect.
Monitoring is a primitive. Portfolio intelligence is the product.
HOW THE INTELLIGENCE WORKS
Evidence first. AI where it adds judgement. Hard constraints around capital.
Prospect is being built so measured evidence remains traceable and AI operates on top of it rather than replacing it. Statistical and time-series systems establish what changed; AI helps structure strategy knowledge, interpret competing explanations and communicate the decision context.
01Measured evidence
Returns, risk, dependencies, regime features and portfolio relationships remain versioned and auditable.
02AI interpretation
Structured strategy understanding, evidence-grounded explanations and research assistance sit above saved evidence.
03Constraint-aware scenarios
Allocation proposals respect explicit exposure, concentration, capacity and risk constraints.
04Human decision
Prospect explains and proposes; the investment team retains control of capital during validation.
Every measured claim should retain source, configuration and code lineage; supplied, measured and inferred information are kept distinct.
CAPITAL-ALLOCATION INTELLIGENCEScenarios · Constraints · Reasons · Uncertainty
PORTFOLIO INTELLIGENCECorrelation · Concentration · Common risk · Diversification
REGIME + STRATEGY INTELLIGENCEDependencies · Behaviour · Significance · Context
STRATEGY UNDERSTANDINGIdentity · Thesis · Family · Evidence provenance
The current reliability engine is one executable component inside this full decision architecture.
WHY THIS MATTERS
More intelligence upstream creates a bigger decision problem downstream.
Strategy creation is getting cheaper
AI, better research tooling and systematic infrastructure increase the number and complexity of strategies a firm can build and operate.
Capital decisions remain fragmented
Strategy research, regime context, portfolio risk and allocation logic often sit across separate tools, notebooks and human processes.
The durable layer is decision intelligence
A specialised system that understands strategies and their portfolio consequences can become more valuable as underlying models and research tools become more capable.
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
BUILD PATH
Build the full intelligence loop, not a demo.
The MVP is being expanded layer by layer until one coherent workflow runs from strategy understanding through portfolio context to an explainable allocation scenario.
Core intelligence domain
Strategy specifications, dependency evidence, data lineage and portfolio objects become first-class product primitives.
Portfolio + regime intelligence
Correlation, concentration, common-risk diagnostics and regime compatibility become executable and versioned.
Allocation decision engine
Constraint-aware scenarios explain proposed weight changes, confidence, uncertainty and the evidence behind them.
AI + strategy universe
AI-assisted strategy understanding, grounded explanation and a reference strategy universe connect into the same portfolio loop.
BUSINESS MODEL
Institutional software at the decision layer.
Prospect is being built as B2B software for systematic investment firms. The commercial entry point is an enterprise platform contract; value expands as Prospect becomes embedded across strategy intelligence, portfolio risk and capital-allocation workflows.
BUYERSSystematic investment firms
CORE ECONOMICSEnterprise platform contracts
EXPANSIONMore strategies · more workflows · deeper decision support
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 the institutional intelligence layer.
The round is designed to fund approximately 18 months of product development, technical hiring, data/research infrastructure, design-partner deployment and institutional hardening.
ROUND£1m
RUNWAY~18 months
STAGEPre-seed
TARGET MILESTONESWorking portfolio-intelligence MVP · Design partners · Pilot deployments · Strategy / regime / portfolio / allocation loop
CONTACT
Build the intelligence layer between strategies and capital.
FOUNDER & CEO
Fred Boxer
founder@prospectpartnership.co.ukLinkedIn ↗Investor, design-partner and founding technical-talent conversations are welcome.