Agentic Synth

Enterprise-Grade Simulation Engine with Self-Learning AI

--- ## Overview Agentic Synth is a self-learning simulation engine that generates realistic synthetic data at scale. Unlike static generators that produce random values, this engine learns from every run—extracting patterns from your data to improve quality over time. Generate 100 records in 1ms or 50,000 records in 215ms across 37 different domains. Self-Learning Neural Architecture (SONA) powers the engine with three learning tiers: | Tier | What It Does | Example | |------|--------------|---------| | Instant | Learns patterns during generation | "Electronics products cluster around $200-500" | | Background | Trains on batch completion | "Bloomberg buy ratings correlate with sector performance" | | Deep | Cross-session pattern retention | "Medical diagnoses improve ICD-10 code accuracy over time" | The engine extracts data-type specific patterns: price distributions correlate with product categories, analyst recommendations match rating distributions, medical billing codes align with procedures, and supply chain lead times reflect regional logistics. Key Capabilities: - 150x faster than JavaScript generators (Rust/WASM powered by RuVector) - 5 embedding models for semantic search (all-MiniLM-L6-v2, bge-small, all-mpnet, e5-small, gte-small) - Real brand matching per category (Samsung for Electronics, Nike for Sports, LEGO for Toys) - Consistent data logic (stock counts match availability, shipping prices match free flags) - Neural pattern training per data type with EWC++ memory protection For developers, it eliminates rate limits and captchas. For enterprises, it provides compliant test data without legal risks. For AI teams, it generates unlimited training data with semantic embeddings. The simulation mode streams data in batches—push 50 records every 2 seconds for real-time pipeline testing. Seeds ensure reproducible results for CI/CD. Pairs with AI Memory Engine for semantic search and RAG applications. Benchmarks: 100 records in 1ms | 1,000 in 7ms | 10,000 in 53ms | 50,000 in 215ms (232K records/sec) --- ## What's New in v3.0 - 4 Tier-1 Premium APIs: Bloomberg, ZoomInfo, FactSet, LSEG/Reuters clones ($70K+/year value) - 5 Biosignal/Security: EEG brainwaves, CGM glucose, SIEM logs, threat intel, NetFlow - 5 Industrial/Scientific: SCADA, LiDAR, CAN bus, genomic VCF, satellite imagery - 5 Exotic/Research: fMRI brain scans, protein PDB, power grid, AIS maritime, radar - Crunchbase Clone: Real company data via Gemini Grounding API with web search - Memory Session Persistence: Cross-session data sharing between actors - 37 total data types covering web, finance, healthcare, security, industrial, and scientific domains --- ## 37 Data Types - Complete Reference ### Core Web Data (10 types) | Type | Description | Use Case | |------|-------------|----------| | ecommerce | Amazon/eBay style products, reviews, sellers | Scraper testing | | social | Twitter/TikTok posts, likes, comments | Social dashboards | | jobs | LinkedIn/Indeed listings, salaries | Job board testing | | real_estate | Zillow properties, addresses, prices | Real estate apps | | search_results | Google SERPs, snippets, rankings | SEO tools | | news | Articles, authors, engagement | News aggregators | | api_response | REST API mock responses, pagination | Backend mocking | | timeseries | Time-stamped metrics, trends | IoT dashboards | | events | Page views, clicks, form submissions | Analytics testing | | embeddings | Vector data (384-768 dimensions) | ML/RAG training | ### Tier 1: Ultra-Premium Financial APIs (4 types) - $70K+/year value | Type | Real API Cost | What You Get | |------|---------------|--------------| | bloomberg | $24-32K/year | Full terminal data: quotes, fundamentals, analytics, news, consensus | | zoominfo | $15K+/year | B2B contacts, technographics, intent signals, org charts | | factset | $12K/year | Financial analytics, estimates, ownership, supply chain | | lseg | $3.6-22K/year | Reuters news, M&A deals, ESG scores, analyst research | ### Priority 1: Biosignal & Security (5 types) | Type | Description | Real-World Application | |------|-------------|------------------------| | eeg | 5-band neural oscillations, 10-20 electrode system | BCI research, wellness apps | | cgm | Continuous glucose with meal events, trends | Diabetes management ML | | siem | Security events, MITRE ATT&CK, correlations | SOC training, SIEM testing | | threat_intel | IOCs (IPs, domains, hashes), malware families | Threat detection ML | | netflow | Network flows, 5-tuple, application detection | Network security analysis | ### Priority 2: Industrial & Scientific (5 types) | Type | Description | Real-World Application | |------|-------------|------------------------| | scada | PLC registers, process variables, OPC UA format | Digital twin development | | lidar | 3D point clouds, object detection, bounding boxes | Autonomous vehicle ML | | canbus | Vehicle ECU messages, DBC signals | Automotive development | | genomic_vcf | Genetic variants, annotations, population frequencies | Bioinformatics pipelines | | satellite | Multi-spectral bands, NDVI, cloud masks | Remote sensing analysis | ### Priority 3: Exotic & Research (5 types) | Type | Description | Real-World Application | |------|-------------|------------------------| | fmri | BOLD signal voxels, connectivity matrices | Neuroscience research | | protein_pdb | Molecular 3D structures, binding sites | Drug discovery ML | | power_grid | 3-phase electrical, PMU phasors, harmonics | Grid simulation | | ais | Maritime ship tracking, collision risk | Logistics optimization | | radar | Weather reflectivity, vehicle detection | Autonomous systems | ### Enterprise & Healthcare (4 types) | Type | Description | Use Case | |------|-------------|----------| | medical | Patient records, ICD-10, billing | EHR testing | | company | Org structure, financials, leadership | CRM development | | supply_chain | Shipments, inventory, logistics | SCM systems | | financial | Transactions, accounts, fraud detection | Banking apps | ### Utility Types (2 types) | Type | Description | Use Case | |------|-------------|----------| | structured | Custom schema definition | Any specialized need | | demo | Mix of all types | Quick exploration | --- ## Quick Start ### Basic Usage json { "dataType": "demo", "count": 100 } ### Premium Financial Data json { "dataType": "bloomberg", "count": 500 } ### Biosignal Streaming json { "dataType": "eeg", "count": 1000 } ### Security Operations json { "dataType": "siem", "count": 500 } ### Industrial Telemetry json { "dataType": "scada", "count": 200 } --- ## Tutorials ### Tutorial 1: Bloomberg Terminal Alternative Generate enterprise-grade financial data worth $24K/year: json { "dataType": "bloomberg", "count": 1000, "seed": "financial-test-v1" } Sample Output: json { "terminalId": "BBG1734012345678", "security": { "ticker": "AAPL", "name": "Apple Inc", "assetClass": "equity", "sector": "Technology", "exchange": "NASDAQ" }, "pricing": { "last": 178.50, "bid": 178.45, "ask": 178.55, "volume": 45000000, "vwap": 177.82 }, "fundamentals": { "marketCap": "2.8T", "peRatio": 28.5, "eps": 6.26, "dividendYield": 0.52 }, "analytics": { "beta": 1.25, "volatility": 22.5, "sharpeRatio": 1.45 }, "consensus": { "recommendation": "buy", "targetPrice": 210.00, "numAnalysts": 45 } } ### Tutorial 2: EEG Brainwave Data for BCI Research Generate neural oscillation data for brain-computer interface development: json { "dataType": "eeg", "count": 500, "seed": "bci-research-v1" } Sample Output: json { "sessionId": "EEG_1734012345678", "samplingRate": 250, "channels": ["Fp1", "Fp2", "F3", "F4", "C3", "C4", "P3", "P4", "O1", "O2"], "epoch": { "startTime": "2024-12-14T10:30:00Z", "duration": 4000, "samples": 1000 }, "bands": { "delta": { "power": 15.2, "range": "0.5-4Hz" }, "theta": { "power": 8.7, "range": "4-8Hz" }, "alpha": { "power": 25.3, "range": "8-13Hz" }, "beta": { "power": 12.1, "range": "13-30Hz" }, "gamma": { "power": 5.8, "range": "30-100Hz" } }, "mentalState": "focus", "quality": { "impedance": "good", "artifacts": ["blink_detected"], "signalQuality": 0.92 } } ### Tutorial 3: SIEM Security Logs for SOC Training Generate realistic security event logs with MITRE ATT&CK mapping: json { "dataType": "siem", "count": 1000, "seed": "soc-training-v1" } Sample Output: json { "eventId": "SIEM_1734012345678", "timestamp": "2024-12-14T10:30:45.123Z", "source": "firewall", "eventType": "intrusion_attempt", "severity": "high", "riskScore": 85, "mitre": { "tactic": "Initial Access", "technique": "T1190", "techniqueName": "Exploit Public-Facing Application" }, "network": { "srcIp": "185.234.xx.xx", "dstIp": "10.0.1.50", "srcPort": 45678, "dstPort": 443, "protocol": "TCP" }, "enrichment": { "geoLocation": "Russia", "threatIntel": "known_scanner", "asn": "AS12345" }, "incident": { "correlated": true, "incidentId": "INC-2024-1234", "attackChain": ["reconnaissance", "initial_access"] } } ### Tutorial 4: LiDAR Point Clouds for Autonomous Vehicles Generate 3D point cloud data for perception system development: json { "dataType": "lidar", "count": 100, "seed": "av-perception-v1" } Sample Output: json { "frameId": "LIDAR_1734012345678", "timestamp": "2024-12-14T10:30:00.000Z", "sensor": { "type": "velodyne_vlp32", "scanPattern": "rotating", "horizontalFov": 360, "verticalFov": 40 }, "pointCloud": { "numPoints": 65536, "format": "XYZI", "points": [ { "x": 10.5, "y": 2.3, "z": 0.8, "intensity": 45, "classification": "vehicle" }, { "x": 15.2, "y": -1.1, "z": 1.2, "intensity": 78, "classification": "pedestrian" } ] }, "detections": [ { "objectId": "OBJ_001", "class": "vehicle", "confidence": 0.95, "boundingBox": { "x": 10.5, "y": 2.3, "z": 0.8, "length": 4.5, "width": 1.8, "height": 1.5 }, "velocity": { "vx": 12.5, "vy": 0.1, "vz": 0 } } ] } ### Tutorial 5: Threat Intelligence IOC Feeds Generate malware IOCs and threat actor data for security ML: json { "dataType": "threat_intel", "count": 500, "seed": "threat-ml-v1" } Sample Output: json { "iocId": "IOC_1734012345678", "type": "ip", "value": "185.234.xx.xx", "threatType": "c2_server", "confidence": 95, "firstSeen": "2024-11-01T00:00:00Z", "lastSeen": "2024-12-14T10:30:00Z", "tlpMarking": "amber", "malwareFamily": "Cobalt Strike", "threatActor": { "name": "APT29", "aliases": ["Cozy Bear", "The Dukes"], "country": "RU", "motivation": "espionage" }, "mitre": { "tactics": ["Command and Control"], "techniques": ["T1071.001"] }, "actions": ["block", "alert", "investigate"], "sources": ["internal_sandbox", "osint_feed"] } ### Tutorial 6: Genomic Variant Data for Bioinformatics Generate VCF-format genetic variant data: json { "dataType": "genomic_vcf", "count": 1000, "seed": "genomics-v1" } Sample Output: json { "variantId": "VAR_1734012345678", "chromosome": "chr17", "position": 7577120, "rsId": "rs28934578", "reference": "G", "alternate": "A", "quality": 99, "filter": "PASS", "genotype": "0/1", "annotations": { "gene": "TP53", "consequence": "missense_variant", "impact": "HIGH", "aminoAcidChange": "R248W" }, "population": { "gnomAD_AF": 0.00001, "clinvar": "Pathogenic", "dbSNP": true }, "clinical": { "significance": "pathogenic", "disease": "Li-Fraumeni syndrome", "inheritance": "AD" } } --- ## Memory Session Persistence v3.0 introduces cross-session memory for data accumulation and sharing between actors: json { "dataType": "bloomberg", "count": 1000, "memorySessionEnabled": true, "memorySessionId": "financial-data-2024", "appendToSession": true } Benefits: - Accumulate data across multiple runs - Share data between Agentic Synth and AI Memory Engine - Build persistent datasets over time - Enable cross-actor workflows --- ## Self-Learning (SONA) The Self-Optimizing Neural Architecture learns patterns from generated data: json { "dataType": "bloomberg", "count": 1000, "sonaEnabled": true, "ewcLambda": 2000, "patternThreshold": 0.7 } | Tier | What It Learns | Example | |------|----------------|---------| | Instant | Real-time patterns | "Tech stocks correlate with NASDAQ" | | Background | Batch patterns | "Q4 retail volume increases 40%" | | Deep | Cross-session | "Pharma P/E ratios range 15-25" | ### Deep Training & Optimization For production workloads, use swarm-orchestrated deep training to maximize pattern learning: json { "dataType": "bloomberg", "count": 1000, "sonaEnabled": true, "ewcLambda": 2000, "patternThreshold": 0.7, "seed": "deep-training-financial-v1" } #### Optimization Strategies | Strategy | Description | Best For | EWC Lambda | |----------|-------------|----------|------------| | Rapid Learning | Low protection, fast adaptation | New data types, exploration | 500-1000 | | Balanced | Moderate protection, steady learning | General production use | 2000 | | Conservative | High protection, stable patterns | Critical financial data | 5000+ | | Deep Training | Extended runs with cross-session memory | Enterprise pattern libraries | 2000 + memory persistence | #### Concurrent Training Results | Configuration | Runs | Records | Patterns | Duration | Records/sec | |---------------|------|---------|----------|----------|-------------| | Single data type | 10 | 1,000 | ~100 | 12s | 83 | | 5 types parallel | 50 | 5,000 | ~500 | 15s | 333 | | 20 types parallel | 200 | 20,000 | ~2,000 | 45s | 444 | | Full swarm (37 types) | 370 | 37,000 | ~3,700 | 90s | 411 | #### Pattern Learning by Data Type | Category | Data Types | Patterns/1K Records | Learning Focus | |----------|------------|---------------------|----------------| | Financial | bloomberg, factset, lseg | 150-200 | Price correlations, sector patterns | | Biosignal | eeg, cgm, fmri | 100-150 | Waveform characteristics, temporal patterns | | Security | siem, threat_intel | 120-180 | Attack signatures, IOC relationships | | Industrial | scada, lidar, canbus | 80-120 | Sensor correlations, anomaly patterns | | Scientific | genomic_vcf, protein_pdb | 90-140 | Sequence patterns, structural motifs | #### Swarm Training Command Run deep training across all 37 data types with concurrent execution: bash # Using Apify CLI with parallel execution for type in bloomberg eeg siem lidar genomic_vcf; do apify call ruv/ai-synthetic-data-generator -s \ --input='{"dataType":"'$type'","count":100,"sonaEnabled":true,"ewcLambda":2000}' & done wait #### Training Script (Node.js) javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const DATA_TYPES = ['bloomberg', 'eeg', 'siem', 'lidar', 'genomic_vcf']; // Run concurrent training batches const results = await Promise.all( DATA_TYPES.map(type => client.actor('ruv/ai-synthetic-data-generator').call({ dataType: type, count: 100, sonaEnabled: true, ewcLambda: 2000 }) ) ); ### SONA Learning Benchmark Results Comprehensive benchmarks measuring SONA's learning capabilities across multiple dimensions: #### Quantitative Metrics | Metric | Value | Description | |--------|-------|-------------| | Generation Speed | 232K records/sec | Peak throughput on Rust/WASM engine | | Pattern Detection Rate | 10-20% | Patterns extracted per 1K records | | Learning Convergence | 3-5 iterations | Iterations to stable pattern set | | Memory Retention | 85-95% | Cross-session pattern preservation | | Cross-Domain Transfer | 60-80% | Pattern applicability across types | #### EWC Lambda Performance Matrix | Lambda | Learning Speed | Memory Retention | Stability | Use Case | |--------|---------------|------------------|-----------|----------| | 500 | Very Fast | Low (40%) | Volatile | Rapid prototyping | | 1000 | Fast | Medium (65%) | Moderate | Exploration | | 2000 | Balanced | High (85%) | Stable | Production | | 5000 | Slow | Very High (95%) | Very Stable | Critical data | #### Data Type Learning Profiles | Category | Types | Pattern Complexity | Learning Rate | Quality Score | |----------|-------|-------------------|---------------|---------------| | Core Web | 10 | Low-Medium | Fast (1-2 iter) | 90-95% | | Financial | 6 | High | Medium (3-4 iter) | 85-92% | | Biosignal | 3 | Very High | Slow (4-5 iter) | 82-88% | | Security | 3 | High | Medium (3-4 iter) | 85-90% | | Industrial | 3 | Medium-High | Medium (3 iter) | 87-92% | | Scientific | 5 | Very High | Slow (4-5 iter) | 80-88% | | Exotic | 4 | Very High | Slow (5 iter) | 78-85% | #### Swarm Training Performance | Topology | Agents | Throughput | Efficiency | Best For | |----------|--------|------------|------------|----------| | Sequential | 1 | 30 rec/s | 100% (baseline) | Small batches | | Parallel (5) | 5 | 140 rec/s | 93% | Standard workloads | | Parallel (10) | 10 | 260 rec/s | 87% | Large training | | Parallel (20) | 20 | 440 rec/s | 73% | Deep training | | Full Swarm (37) | 37 | 720 rec/s | 65% | Comprehensive | #### Qualitative Learning Capabilities Pattern Recognition: - Price/value distributions by category - Temporal correlations in time-series - Hierarchical relationships in nested data - Statistical distributions per field type Memory Features: - EWC++ (Elastic Weight Consolidation) prevents catastrophic forgetting - Cross-session pattern persistence via Apify KeyValueStore - Data-type specific pattern libraries - Trajectory tracking for reward-based learning Adaptation Capabilities: - Real-time pattern adjustment during generation - Domain transfer between similar data types - Quality improvement over successive runs - Anomaly detection for edge cases #### Benchmark Methodology Tests performed on Apify cloud infrastructure: - Hardware: 4GB RAM containers - Build: v3.0.4 with SONA enabled - Configuration: EWC Lambda 2000, Pattern Threshold 0.7 - Dataset: 1,000 records per data type, 20 concurrent runs - Measurement: Duration, patterns extracted, quality scores --- ## Performance ### Benchmark Results (Rust/WASM Engine) | Records | Time | Records/sec | Use Case | |---------|------|-------------|----------| | 100 | 1ms | 100,000 | Unit tests | | 1,000 | 7ms | 142,857 | Integration tests | | 10,000 | 53ms | 188,679 | Stress tests | | 50,000 | 215ms | 232,558 | Load tests | ### By Data Type Complexity | Category | Example Type | 1K Records | Complexity | |----------|--------------|------------|------------| | Core | ecommerce | 7ms | Low | | Premium | bloomberg | 15ms | High | | Biosignal | eeg | 25ms | Very High | | Scientific | lidar | 30ms | Very High | --- ## API Integration ### Python python from apify_client import ApifyClient client = ApifyClient("your-api-token") run = client.actor("ruv/ai-synthetic-data-generator").call(run_input={ "dataType": "bloomberg", "count": 1000, "sonaEnabled": True }) data = client.dataset(run["defaultDatasetId"]).list_items().items ### JavaScript javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'your-api-token' }); const run = await client.actor('ruv/ai-synthetic-data-generator').call({ dataType: 'siem', count: 500, sonaEnabled: true }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); ### cURL bash curl -X POST "https://api.apify.com/v2/acts/ruv~ai-synthetic-data-generator/runs?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"dataType": "threat_intel", "count": 500}' --- ## Pricing ### Core Data Types | Event | Price | Description | |-------|-------|-------------| | E-commerce Record | $0.001 | Products, reviews | | Social Media Post | $0.001 | Posts, engagement | | Job/News/Real Estate | $0.001 | Listings | ### Premium Data Types | Event | Price | Description | |-------|-------|-------------| | Bloomberg Record | $0.005 | Full terminal data | | ZoomInfo/FactSet/LSEG | $0.005 | Enterprise financial | | SIEM/Threat Intel | $0.003 | Security data | | EEG/CGM Biosignal | $0.003 | Medical streams | | LiDAR/Satellite | $0.004 | Scientific data | Example Costs: - 1,000 Bloomberg records: ~$5.00 (vs $24K/year real Bloomberg) - 500 SIEM events: ~$1.50 (vs $50K/year SIEM platform) - 1,000 EEG epochs: ~$3.00 (vs $50K research equipment) --- ## Links - Agentic Synth on Apify - AI Memory Engine - Companion actor for persistent AI memory - GitHub Repository - Report Issues --- Built with RuVector. Enterprise-grade synthetic data generation with 37 data types and SONA self-learning. Pairs with AI Memory Engine for complete AI data solutions.