Production systems built on our context-engineering principles. Each MVP demonstrates end-to-end AI integration — from autonomous operation to self-healing infrastructure.
A 100% autonomous AI-managed security operations center for home, family, and small business environments
This MVP is a fully autonomous security operations center engineered for households, families, and small businesses that want continuous cyber defense without building an in-house SOC. It fuses network telemetry, graph memory, AI triage, automated containment, parental/family safety controls, and Telegram-based Mission Control coordination into one always-on defensive system.
The platform continuously detects, correlates, classifies, remembers, and works incidents end to end. High and critical events become persisted cases, move through an AI-operated Kanban workflow, trigger structured Grok-powered assessments, execute bounded remediation actions, and notify the operator through a centralized Mission Control Telegram group. The result is low-cost, full-spectrum, AI-run cyber defense for the environments most security products ignore.
Converts alert noise into persisted cases, then moves them across new, triaging, active, contained, resolved, and false-positive states through an AI-managed Kanban workflow.
Focuses model spend and operator messaging on high-value security events, reducing noise while preserving full response quality where it matters.
Assigns structured verdicts such as true positive, false positive, false negative, and watch while producing confidence, assessment, mitigation, remediation, and forensic context.
Builds incident fingerprints, stores outcomes, and determines whether a new serious event has ever been seen before — allowing the SOC to learn its environment over time.
Re-examines prior cases against delayed evidence and multi-signal correlation to surface incidents the system originally under-classified or missed.
Executes bounded security actions such as case containment, domain blocking, curfew enforcement, study-mode changes, and false-positive suppression without human approval loops.
Fuses network visibility, DNS activity, Zeek, Suricata, WiFi scans, graph memory, and enforcement state into one operational security picture.
Uses graph persistence to retain device, alert, DNS, presence, and relationship history for richer correlation and environmental awareness.
Combines real SOC operations with family safety and policy enforcement, including curfews, study windows, DNS filtering, device supervision, and difficult-to-bypass network controls.
AI-powered options flow analysis, market structure intelligence, and conviction trade generation
FlowPatrol is an AI-driven options flow intelligence platform that monitors institutional activity, detects unusual positioning, classifies market regimes, and generates conviction trade recommendations across multiple timeframes. The system processes live options chains, block trades, and cross-asset flows to surface actionable intelligence that would take a human analyst hours to compile.
A 5-wave modular execution pipeline — from raw data collection through multi-agent analysis to dashboard generation — produces 39 structured intelligence artifacts per run. The output is a self-contained, responsive dashboard with 22 collapsible sections organized across 6 analytical zones, delivered to both desktop and mobile platforms.
Each wave builds on the artifacts of the previous one. Raw market data enters Wave 0 and a fully rendered intelligence dashboard exits Wave 5 — no human intervention required.
Market state snapshot via Alpaca API — indices (SPY/QQQ/IWM), 14 mega-caps, 11 sector ETFs, 6 macro instruments, and crypto. Computes 20-day ATR, intraday change, and overnight regime classification.
Two parallel AI agents analyze market structure and flow intelligence. Agent A maps regime classification, term structure, skew topology, and GEX landscape. Agent B classifies flow direction, detects unusual activity, and maps IV surface deformation.
Block correlation analysis, whale trade reconstruction, 47-dimensional anomaly detection via isolation forest, historical analog matching with 252-day lookback, and Granger-causality sync campaign detection across institutional trades.
Top 25 edge-ranked options plays, top 25 swing trades scored across 4 factors (flow 40%, volatility surface 25%, technical 20%, catalyst 15%), and top 25 LEAPS accumulation candidates. Full Greek surfaces including second and third-order Greeks.
Four conviction plays generated: day trade (0-3 DTE, GEX edge), swing trade (5-10 day accumulation), midterm position (4+ week LEAPS campaign), and long-term equity + LEAPS structure. Each with defined entries, targets, and risk parameters.
All 39 JSON artifacts assembled into a self-contained HTML dashboard (~69KB). 22 collapsible sections across 6 zones — Command, Conviction, Structure, Flow, Analytics, and Action. Deployed to Cloudflare Pages with desktop and mobile layouts.
Overnight gap analysis classifies each session as Compression, Trend, Reversal, or Gap — informing all downstream trade generation and risk sizing.
Gamma exposure mapping identifies zero gamma lines, max gamma strikes, and negative gamma pockets — revealing where dealer hedging creates predictable price magnets.
AI-scored anomaly detection across volume, premium, strike concentration, and open interest changes. Conviction scores flag high-probability institutional positioning.
Multi-leg structure detection identifies complex institutional strategies hidden across separate order flow — spreads, collars, and risk reversals reassembled from fragments.
Weighted 0-100 risk score from 8 components: GEX (20%), skew (15%), 0DTE gamma (15%), P/C ratio (15%), unusual activity (10%), IV (10%), sector rotation (10%), smart money (5%).
Full Greek computation including second-order (vanna, charm, volga) and third-order (speed, color, zomma, ultima). Monte Carlo stress test across 10K paths with spot, IV, and time perturbations.
Net delta and premium aggregation across 11 sector ETFs with rotation directional arrows — detecting institutional sector reallocation before it manifests in price.
252-day lookback using cosine similarity and dynamic time warping to match current market conditions against historical analogs — surfacing regime-specific precedents for each trade thesis.
Automated macro and systemic risk monitoring across 8 risk domains with graph-powered trend analysis
The Financial Risk Dashboard is a fully automated pipeline that collects, validates, and visualizes financial risk indicators across 8 interconnected domains — from options market structure and margin debt levels to Japan carry trade exposure and hidden systemic vulnerabilities. Data is sourced directly from market APIs and regulatory filings, not scraped headlines.
A LadybugDB graph database maintains a rolling 21-day history of every tracked metric, enabling automated anomaly detection (z-score > 2.0), trend velocity analysis, and cross-metric correlation. The pipeline runs at market open every weekday, rendering 10 self-contained HTML dashboards and deploying them to Cloudflare Pages with access restricted to authorized users only.
Seven-stage pipeline from raw data collection to deployed dashboards. Every stage is instrumented, validated, and self-healing. The entire cycle completes in under 30 seconds.
Alpaca API (~28 calls, FREE) for real-time stock snapshots, option chains with full Greeks, and crypto bars. FINRA Excel direct download for margin debt. Brave Search with 5-tier TTL caching (~7 queries/run avg) and 1.0-source-only domain whitelist for macro indicators. Alpaca News API (FREE) for 7-day historical market context. Brave Search for 12-hour breaking risk developments.
Every collected metric passes through type checking, range validation, and freshness detection. Quality scoring produces a 0-100 completeness rating. Stale or invalid data falls back to cached values from the previous run — no missing fields in production output.
All metrics are archived to a LadybugDB graph database as timestamped Metric nodes connected to Run nodes. The graph maintains a 21-day rolling window with automatic pruning of older data. Cross-run edges enable temporal analysis.
21-day trend analysis computes slope, velocity, and z-scores for every metric. Anomaly detection flags metrics with z-score > 2.0. Persistent event tracking identifies risk signals that span multiple collection cycles.
Original hand-crafted HTML dashboards serve as templates. Live data and graph intelligence are injected via targeted string replacement — preserving the exact CSS, JavaScript, and visual design while updating every metric, date, and trend indicator.
10 HTML files committed (GPG-signed) and pushed to GitHub. Cloudflare Pages auto-deploys on push. Cloudflare Access enforces email-based authentication — only authorized users can view the dashboards. Both the custom domain and pages.dev subdomain are protected.
ATM implied volatility, IV rank, put/call ratios, delta skew, and full Greeks across 10 tickers (indices + Magnificent 7). VIX and SKEW index tracking.
Yield curve (2s10s spread), SOFR rate, ISM PMI, high-yield credit spreads, initial jobless claims, and unemployment rate. Bond ETF proxy monitoring (TLT, HYG, LQD, IEF).
Direct FINRA Excel file parsing for monthly margin statistics. YoY and MoM change calculations, all-time high detection, net margin debit computation, and 349-month historical depth.
Gold/SPX, Utilities/SPX, Copper/Gold, and Silver/Gold ratios with 6-month and 1-year change rates. Historical percentile rankings from 252 trading days of data.
Yen proxy monitoring via FXY, Nikkei exposure via EWJ/DXJ, 20-day yen volatility, BOJ rate tracking, and carry trade size estimation from institutional sources.
Investment-grade CDS spreads, passive ETF market share concentration, and commercial real estate delinquency rates — the risks that don't make headlines until they detonate.
Semiconductor valuations (NVDA, AMD, INTC, TSM), hyperscaler capex exposure (MSFT, GOOGL, META, AMZN), core PCE, and CPI tracking against the AI investment thesis.
50-day and 200-day moving average crossover detection. 30/60/120-day rolling correlations against SPY, QQQ, GLD, and UUP — tracking Bitcoin's evolving relationship with traditional assets.
AI-powered financial risk monitoring API built exclusively for autonomous AI agents — no UI, no login page, no human-facing interface
FinRisk is a non-human identity (NHI) API — built exclusively for AI agents, not humans. Autonomous agents authenticate with tiered API keys, query financial risk scores and market data, and receive structured JSON responses designed for machine consumption. The system meters every agent request, enforces tiered rate limits, and gates paid access via X-402 payment protocol. There is no UI, no login page, no human-facing interface — the dashboards exist solely for the human operator to monitor infrastructure health.
The API continuously collects market data from multiple sources (FRED, Alpaca, FINRA, xAI/Grok), computes composite risk scores using AI analysis, and serves them to authenticated agent clients. It persists scores, customer data, and usage events in Neo4j, exposes Prometheus metrics, and runs a 5-level self-healing health monitoring engine — all autonomously on a single production droplet behind HTTPS with rate limiting, X-402 payment gating, and Zero Trust-protected operator dashboards.
A single production droplet running behind HTTPS with tiered API key authentication, usage metering, rate limiting, X-402 payment gating, and Prometheus metrics. The API's sole purpose is to serve as a reliable, self-healing financial data backbone that AI agents programmatically consume to inform their own decision-making.
Tiered API key system with per-agent usage metering. Every request is authenticated, rate-limited, and logged. Customer data, usage events, and billing records persist in Neo4j. Agents discover capabilities via free OpenAPI schema endpoint.
Continuous market data ingestion from FRED (yields, unemployment, CPI), Alpaca (real-time prices, options, crypto), FINRA (margin debt), and xAI/Grok (AI-driven analysis and search). Multi-source fusion with TTL caching and staleness detection.
All 10 monitor scores computed with weighted composites, regime classification (ELEVATED/NORMAL/LOW), score deltas, confidence levels, and active alert counts. AI analysis enhances raw data with contextual risk assessment.
Risk scores, customer data, and usage events persist in Neo4j graph database. Prometheus metrics expose request latency, error rates, monitor health, and circuit breaker states for operational observability.
Pay-per-request pricing in USDC on Base blockchain. Tiered rate limits enforce fair usage. Free discovery endpoints (health, pricing, schema) require no payment. Paid endpoints return 402 without valid X-PAYMENT header.
Health and monitoring dashboards exist solely for the human operator — protected behind Cloudflare Zero Trust access control. No agent-facing UI exists. The API serves JSON exclusively; humans observe infrastructure only.
USD/JPY, JGB yield, and EWJ tracking with 1.5x weight. Monitors yen volatility and BOJ rate movements for carry trade blow-up risk.
Direct FINRA margin statistics with 1.3x weight. YoY change rates, all-time high detection, and net margin debit computation.
Yield curve inversion, initial claims, CPI, and unemployment rate from FRED. 1.2x weight with multi-indicator confirmation logic.
CRE delinquency, bank credit contraction, and Treasury General Account drawdowns. 1.4x weight — the risks that detonate without warning.
Gold, silver, utilities vs. SPY and IWM ratio analysis. 0.8x weight detects institutional flight-to-safety before it hits headlines.
NVDA, AMD, MSFT, GOOGL, META, SMCI semiconductor and hyperscaler valuations. 1.1x weight tracking the AI investment thesis unwind risk.
50-day and 200-day moving average crossover detection on BTC/USD. 0.7x weight with golden cross / death cross regime classification.
Rolling correlations against QQQ, TLT, GLD, and UUP. 0.6x weight tracking Bitcoin's evolving relationship with traditional asset classes.
VIX proxy analysis and SPY options market structure. 1.2x weight monitoring implied volatility regime shifts and derivatives stress.
Proprietary anomaly detection across XLE, USO, XOM, CVX, OXY, COP. 1.3x weight — premium endpoint for a pattern no human analyst identified.
Full-spectrum health monitoring with a 5-level self-healing engine that detects component degradation, manages circuit breakers on flaky data sources, and takes graduated corrective action — from resetting connections up to AI-driven root cause analysis. A Zero Trust-protected operator dashboard auto-refreshes every 30 seconds, providing the human operator real-time visibility into the NHI infrastructure. All running autonomously.
Animated 0-100 composite health gauge with color-coded thresholds — green (healthy), amber (degraded), red (critical). Single-glance system status derived from all monitored components.
60-check visual uptime bar per component with green/amber/red state tracking. Component health table covers 6 subsystems with success rate bars, avg/p95 latency, and failure counts.
Visual state indicators (closed/open/half-open) with trip counts for each external data dependency (FRED, Alpaca, FINRA, xAI). Prevents cascade failures by isolating degraded sources while the API continues serving agents from cached or alternate data.
Memory, disk, and CPU load with progress bars and threshold coloring. Proactive alerting before resource exhaustion impacts API availability or response latency.
Severity-coded anomaly list with automatic detection. Per-monitor data freshness cards track age, staleness, and quality scores — ensuring agents always receive current intelligence.
Graduated corrective action across 5 escalation levels with cooldown timers: connection resets, cache invalidation, service restarts, pipeline resets, and AI-driven root cause analysis via xAI/Grok. All autonomous — no human intervention required.
Real-time node counts by type and relationship totals from the graph database. Monitors graph integrity and growth patterns to ensure correlation queries remain performant.
Canvas chart of the last 60 health score readings with trend analysis. Essential-Only mode triggers a red alert banner when Level 3 self-healing activates, restricting to critical operations only.
Fully automated, zero-touch deployment across two repositories. Every commit reaching production has passed full integration testing twice — once in CI and once on the live server before the service accepts traffic. No human intervention at any stage.
Developer pushes to finrisk-agents-only main branch
GitHub Actions executes full integration tests on Python 3.12
Secure SSH to production — pull latest, re-validate on-server
Service restart + health endpoint confirmation before accepting traffic
Push to finrisk-health-monitor main branch
Auto-build and deploy to Cloudflare Pages edge network
Cloudflare Zero Trust access controls enforce operator-only access
AI-enhanced OSINT analytics — autonomous intelligence aggregation, analysis, and delivery
The AlphaOne Daily Intelligence Briefing is an AI-enhanced OSINT (Open Source Intelligence) analytics platform — a fully autonomous, end-to-end AI pipeline that collects, validates, scores, analyzes, and delivers curated intelligence briefings with zero human intervention. It provides AI-driven analysis of the current global risk picture across geopolitical, cyber, economic, and technology domains.
Available on iOS, Android, and desktop web, the briefing is delivered as a native mobile experience with responsive dashboards optimized for each platform. The system operates on a self-healing architecture where AI agents continuously monitor their own health, detect anomalies, and autonomously remediate failures across the entire pipeline.
Every stage of the pipeline is AI-driven. No manual curation, no human gatekeepers, no scheduled batch jobs waiting for someone to press a button.
AI-managed feed watchdog continuously monitors and ingests intelligence sources. Adaptive scheduling adjusts collection frequency based on source reliability and freshness signals. Failed feeds are automatically retried with exponential backoff.
Every ingested item passes through AI-powered schema validation and quality scoring. Structured schemas enforce data integrity while quality scorers assess relevance, credibility, and timeliness — filtering noise before it enters the pipeline.
All validated intelligence is stored in a graph database that maps relationships between entities, sources, topics, and temporal patterns. Graph traversal enables multi-hop reasoning that flat databases cannot support — connecting dots across disparate intelligence domains.
LLM-powered analysis synthesizes collected intelligence into structured briefings. Context-engineered prompts ensure consistent output quality. The generator produces responsive dashboards for both desktop and mobile with real-time data visualization.
Finished briefings deploy automatically to desktop and mobile platforms via Cloudflare Pages. Deployment pipelines validate output integrity before going live. Zero-downtime delivery ensures consumers always have access to the latest intelligence.
A dedicated AI health monitoring system continuously observes every component in the pipeline. It detects feed failures, data quality degradation, pipeline stalls, rendering errors, and infrastructure anomalies — then autonomously remediates without human intervention.
The health monitoring system operates as an independent AI agent that treats the briefing pipeline as its observability domain.
Health agents poll every pipeline stage — feed latency, schema validation pass rates, graph DB query performance, generation success rates, and deployment status. Metrics are collected into a dedicated health dashboard with real-time visualization.
AI-driven anomaly detection identifies deviations from baseline behavior. Feed staleness, quality score drops, generation failures, and deployment errors trigger graduated alert levels — from advisory through critical.
When anomalies are detected, the health system autonomously executes remediation workflows: restarting failed feeds, re-triggering generation passes, rolling back bad deployments, and escalating only when automated recovery is exhausted.
A dedicated health monitoring dashboard provides real-time visibility into pipeline status, component health scores, remediation history, and system-wide metrics — giving operators confidence the system is operating within spec.
Detecting PSYOPS and information operations (INFOPS) that bypass traditional source authority ratings. SPAS traces claims back to their original source and scores accuracy across five dimensions using AI-powered analysis.
Traces each claim to its original source — government agency, wire service, or firsthand reporting — and scores the authority of that origin. Claims attributed to unnamed officials or social media posts score lower than Reuters or CENTCOM statements.
Measures how many independent sources confirm the same claim. A single-source report scores near zero; a claim corroborated by 3+ independent wire services scores near 1.0. Cross-domain corroboration (e.g., SIGINT + HUMINT + OSINT) scores highest.
Applies a credibility discount when claims originate from state media of adversarial nations, known propaganda outlets, or entities with documented information warfare programs. Calibrated against historical deception patterns.
Scores how precisely a claim is attributed. Named officials with verifiable statements score highest. "Sources familiar with the matter" or "reports suggest" score lower. Completely unattributed claims receive minimum scores.
When a global crisis escalates to the point of mass human impact, Focus Mode activates hourly intelligence collection and briefing regeneration for a 72-hour monitoring window. This ensures the dashboard stays current during rapidly evolving events that affect a significant percentage of the global population.
Focus Mode auto-activates when the Sentinel 1 alerting engine detects a CRITICAL alert from eligible agents — warfare, WMD proliferation, mass-casualty terrorism, or pandemic-level threats. Economic and cyber-only events are excluded; the trigger requires direct mass human impact at population scale.
Each Focus Mode activation runs for a maximum of 72 hours with hourly collection cycles. The dashboard displays a live countdown showing incident start time, elapsed time, and time remaining. Global crises often hit stasis within this window (ceasefire, negotiations, stabilization).
During the 72-hour window, the focused agent's master orchestrator runs every hour instead of its normal 2-4 hour schedule. After each collection, the full briefing regenerates automatically — pulling fresh data across all 15 domains while prioritizing the crisis agent.
Focus Mode automatically deactivates when the focused agent's posture drops below RED for two consecutive scans — indicating de-escalation, ceasefire, or resolution. The system can also be manually deactivated, and the 72-hour hard cap ensures no runaway cost accumulation.
Running a 24/7 AI intelligence pipeline at production scale demands deliberate cost architecture. The briefing system achieved a 95% cost reduction through strategic model migration, token optimization, and zero-LLM-cost offloading.
Strategic migration from Anthropic Claude Sonnet 4.6 to xAI Grok 4.1 (fast reasoning and non-reasoning variants). The migration preserves intelligence quality while leveraging Grok's native x_search capability for real-time X/Twitter intelligence collection — eliminating a separate collection layer. Four master agents replaced nine individual agents through domain consolidation.
Systematic token reduction across the entire pipeline. Brave Search results capped at 5 per query (eliminating ~80% of per-result token cost). Extra snippet fields disabled by default. Context-engineered prompts with structured output schemas reduce LLM verbosity. Per-run cost attribution tracks token spend across every pipeline stage.
Operations that don't require reasoning are offloaded to zero-cost compute: schema validation (deterministic), feed watchdog staleness detection (heuristic), quality scoring (local Ollama with Llama 3.2 3B), trend analysis (statistical), and graph database queries (Cypher). Only synthesis and analysis hit the LLM billing meter.
These MVPs demonstrate what's possible when AI is engineered into every layer of the stack. Let's talk about your use case.
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