Agentic Synth

Enterprise-Grade Simulation Engine with Self-Learning AI

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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)

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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

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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

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Quick Start

Basic Usage

{ "dataType": "demo", "count": 100 }

Premium Financial Data

{ "dataType": "bloomberg", "count": 500 }

Biosignal Streaming

{ "dataType": "eeg", "count": 1000 }

Security Operations

{ "dataType": "siem", "count": 500 }

Industrial Telemetry

{ "dataType": "scada", "count": 200 }

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Tutorials

Tutorial 1: Bloomberg Terminal Alternative

Generate enterprise-grade financial data worth $24K/year:

{
  "dataType": "bloomberg",
  "count": 1000,
  "seed": "financial-test-v1"
}

Sample Output:

{
  "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:

{
  "dataType": "eeg",
  "count": 500,
  "seed": "bci-research-v1"
}

Sample Output:

{
  "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:

{
  "dataType": "siem",
  "count": 1000,
  "seed": "soc-training-v1"
}

Sample Output:

{
  "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:

{
  "dataType": "lidar",
  "count": 100,
  "seed": "av-perception-v1"
}

Sample Output:

{
  "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:

{
  "dataType": "threat_intel",
  "count": 500,
  "seed": "threat-ml-v1"
}

Sample Output:

{
  "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:

{
  "dataType": "genomic_vcf",
  "count": 1000,
  "seed": "genomics-v1"
}

Sample Output:

{
  "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"
  }
}

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Memory Session Persistence

v3.0 introduces cross-session memory for data accumulation and sharing between actors:

{
  "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

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Self-Learning (SONA)

The Self-Optimizing Neural Architecture learns patterns from generated data:

{
  "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:

{
  "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:

# 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)

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

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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

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API Integration

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

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

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}'

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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)

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Links

• Agentic Synth on Apify
• AI Memory Engine - Companion actor for persistent AI memory
• GitHub Repository
• Report Issues

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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.