Our Mission:

Building the AI-Native Hedge Fund

Infinite R&D. Infinite models. Infinite strategies. Full-market coverage across any instrument, automated.

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AI Native Systematic Asset Management

Asset Management Automation

We combine continuous research, market intelligence, and adaptive execution into one living investment system.

Trading & Investing Automation

Unified Adaptive Trading System (UATS)

Institutional-grade, multi-horizon systematic platform that unifies quantitative research, portfolio construction, execution, and monitoring.

Multi-horizon

Systematic

Adaptive

AI Research

Deep Research

Research engine that turns complex questions into auditable evidence, structured claims, and decision-ready briefs.

Evidence

Facts

Deductions

Connections

What can I help with?

Investigate causality, map 1st/2nd/3rd-order effects, and visualize evidence in an interactive graph.

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Add document

Analyze

Evidence Graphs

Report

What can I help with?

Investigate causality, map 1st/2nd/3rd-order effects, and visualize evidence in an interactive graph.

|

Add document

Analyze

Evidence Graphs

AI-Assisted Investigative Analysis

Market Matrix

Event-driven market intelligence system that maps how real-world events propagate across companies, sectors, and portfolios.

Event Radar

Ripple Effects

Explainable Impact

AI Copilot

Razor Copilot

Operator and R&D copilot that orchestrates research, experiments, evaluations, and operational responses under explicit guardrails.

Orchestration

Strategy Evolution

Safe Automation

Hey David!

Here is your Custom project & schedule

On going project :

Customer Support Chatbot

90% Finsihed

Schedule

Mo

Tu

We

Th

Fr

Sa

Su

Discovery call

10:00 am to 10:30 am

Custom automation

06:00 pm to 06:30 pm

Hey David!

Here is your Custom project & schedule

On going project :

Customer Support Chatbot

90% Finsihed

Schedule

Mo

Tu

We

Th

Fr

Sa

Su

Discovery call

10:00 am to 10:30 am

Custom automation

06:00 pm to 06:30 pm

Our Process

Continuous Self-Improving Process

idea -> research -> evidence -> model/strategy -> evaluation -> deployment -> monitoring -> adaptation/evolution

Step 1

Observe and Frame

Continuously monitor markets, narratives, and macro developments; define hypotheses and risk questions worth testing.

Event Radar

Source credibility

Narrative clustering

1st-order effects

2nd/3rd-order effects

Cross-asset exposure

Risk hypothesis queue

Step 2

Research and Structure

Convert unstructured information into evidence-backed research objects with provenance and confidence tracking.

  • hypothesis = MarketMatrix.frame_hypothesis(
    regime=regime_state,
    horizon=["intraday", "swing", "macro"],
    )
    research_obj = DeepResearch.structurize(
    hypothesis=hypothesis,
    min_sources=3,
    require_provenance=True,
    track_confidence=True,
    )
    if research_obj.confidence >= 0.78 and research_obj.provenance_score >= 0.85:
    candidate = Razor.EvolutionLab.spawn_variant(
    base_strategy=current_strategy,
    thesis=research_obj.thesis,
    constraints=risk_policy.hard_limits(),
    )
    score = candidate.evaluate(
    objectives=["risk_adjusted_return", "drawdown", "stability"],
    stress_tests=["regime_shift", "liquidity_shock", "latency_spike"],
    )
    if score.promotable:
    UATS.promote(candidate, mode="shadow_then_scale")
    else:
    Razor.EvolutionLab.learn(candidate, reason=score.failure_reason)
    UATS.adapt_live(
    drift=model_drift,
    pnl=live_pnl,
    risk=risk_state,
    actions=["throttle", "hedge", "deallocate", "retrain_trigger"],
    )
  • hypothesis = MarketMatrix.frame_hypothesis(
    regime=regime_state,
    horizon=["intraday", "swing", "macro"],
    )
    research_obj = DeepResearch.structurize(
    hypothesis=hypothesis,
    min_sources=3,
    require_provenance=True,
    track_confidence=True,
    )
    if research_obj.confidence >= 0.78 and research_obj.provenance_score >= 0.85:
    candidate = Razor.EvolutionLab.spawn_variant(
    base_strategy=current_strategy,
    thesis=research_obj.thesis,
    constraints=risk_policy.hard_limits(),
    )
    score = candidate.evaluate(
    objectives=["risk_adjusted_return", "drawdown", "stability"],
    stress_tests=["regime_shift", "liquidity_shock", "latency_spike"],
    )
    if score.promotable:
    UATS.promote(candidate, mode="shadow_then_scale")
    else:
    Razor.EvolutionLab.learn(candidate, reason=score.failure_reason)
    UATS.adapt_live(
    drift=model_drift,
    pnl=live_pnl,
    risk=risk_state,
    actions=["throttle", "hedge", "deallocate", "retrain_trigger"],
    )

Step 3

Evaluate and Deploy

Test models and strategies, validate behavior, and deploy only when acceptance criteria and controls are satisfied.

Step 4

Monitor and Adapt

Track live behavior, detect drift or regime change, and run governed adaptation loops for ongoing improvement.

Model drift monitor

Retrain trigger armed

Risk guardrail engine

Limits within threshold

Research feedback loop

New evidence queued

Want to Learn More or Invest?

We are opening a limited waitlist for qualified investors who want early access to our AI-native systematic asset management platform.

Get in touch

Get in touch

"AI-driven forecasting cut inventory waste by 40% for TrailForge"

TrailForge, a suitcase brand, faced stock issues and inefficiencies. Our AI forecasting optimized inventory and production cycles, helping them save costs and deliver faster.

Impact :

40% Less Inventory Waste

35% Faster Production

20% More Accurate Forecasting

25% Faster Fulfillment

"AI-powered workflows reduced error rate by 80% in daily operations"

MedixChain, a healthcare logistics company, was dealing with frequent data errors and delays. We introduced AI validation and live tracking to improve accuracy and speed across their supply chain.

Impact :

80% Error reduction

90% Accuracy in Data Logs

30% Faster Delivery

60+ Hours Saved

"AI integration helped ScaleByte close 3x more deals in less time"

ScaleByte’s sales team struggled with follow-up delays. Our AI sales assistant automated outreach, lead scoring, and CRM updates—resulting in faster responses and more closed deals.

Impact :

3x More Deals

40% Faster Responses

95% Lead Accuracy

CRM Fully Synced

"Automating 50% of operations saved 20% in costs in 2 months"

FinSolve, a financial services firm, was overloaded with repetitive tasks. By automating workflows and integrating data systems, they streamlined operations and significantly reduced overhead.

Impact :

50% Operations Automated

20% Cost Reduction

70+ Hours Saved/Month

2x Faster Client Onboarding

Other Services

Build Your Own AI-Native Platform With Us

We help companies design and develop their own AI-powered products and internal platforms.

From first prototype to production-scale deployment, Sigmance provides end-to-end engineering, architecture, and delivery support.

Full Stack Software Engineering

- Modern web and backend systems built for reliability and maintainability - API-first architecture, data workflows, and platform integrations - Secure, testable, and scalable application delivery

Machine Learning Engineering

- Data pipelines, feature systems, and model development workflows - Applied forecasting, classification, optimization, and decision-support models - Experimentation frameworks with measurable performance tracking

MLOps

- Reproducible training pipelines and model lifecycle management - CI/CD for ML systems, model versioning, and deployment automation - Monitoring for model quality, drift, latency, and operational health

AI Engineering

- LLM-based systems, retrieval workflows, and agentic process automation - Evaluation and guardrails for quality, safety, and consistency - Human-in-the-loop workflows for critical decisions

System Design

- Service-oriented architecture for high-throughput and low-latency systems - Resilient infrastructure patterns, fault tolerance, and scaling strategy - Clear technical roadmaps aligned with business constraints

AI Solutions Architecture & Consulting

- Strategic guidance on where AI delivers real operational value - Build-vs-buy and tooling decisions based on speed, risk, and ownership - Hands-on implementation support, not just advisory slides

From Concept to Production

We combine state-of-the-art technologies, current research methods, and production-first engineering practices to move teams from:

Problem framing and architecture decisions ->Proof of concept (POC) and technical validation -> MVP implementation and workflow integration -> Production hardening, observability, and scaling

Let's build together!

Get in touch

Get in touch