Open Knowledge Infrastructure
Maintain Knolo and related open-source tools for reproducible, local-first, privacy-conscious AI retrieval across public-interest fields.
● Hive Forensics AI Inc. research
Open knowledge infrastructure for a safer digital future.
CCRNet is research and public knowledge infrastructure from Hive Forensics AI Inc. We study how fraud and cybercrime information can be structured, searched, evaluated, and used responsibly—then publish open tools, research, and practical safety resources.
A free, accessible community space for people affected by fraud to share experiences, find peer support, and learn safer next steps.
Research and public knowledge infrastructure come first. Any future paid institutional access will support—not define—that work.
CCRNet is in active research and development
The architecture, models, and interfaces described here are being researched and prototyped. We do not currently have an expected launch date.
FOUR RESEARCH TRACKS
Work the public can see, use, and evaluate.
Hive Forensics AI Inc. organizes CCRNet around durable public outputs. CCRNet is the company’s cybercrime research and public knowledge infrastructure; Knolo remains broad open-source infrastructure.
Cybercrime Research
Study how fraud and cybercrime reports can be structured, searched, connected, evaluated, and safely used in model development.
Public Education
Publish fraud typologies, research findings, safety guidance, anonymized trend reports, technical papers, and educational datasets.
Investigator & Researcher Support
Work toward free or subsidized tools, datasets, and technical assistance for qualified public-interest organizations and researchers.
RESEARCH & DEVELOPMENT
Research is the work—not a footnote.
CCRNet began as a research platform for studying how cybercrime reports can be structured, categorized, and analyzed, and how carefully prepared report data can support artificial-intelligence and blockchain-based systems.
Our current R&D examines isolated knowledge architecture, deterministic retrieval, cross-report pattern analysis, human-reviewed connection suggestions, cybercrime model training and evaluation, and responsible approaches to prediction.
Explore our research history and publicationsCurrent R&D tracks
- 01Knowledge infrastructureIsolated Knolo packs scoped to organizations and individual cases.
- 02Retrieval & connectionsReproducible search with traceable sources and human-reviewed suggestions.
- 03Cybercrime AIDataset development, training, evaluation, and evidence-aware prediction research.
- 04Responsible exchangeOpt-in methods for sanitizing and matching selected intelligence indicators.
CCRNET INTELLIGENCE EXCHANGE
Private first. More powerful together.
A future participating institution may begin with its own authorized case data. Qualified participants could later opt in to a carefully governed exchange; public-interest and research access will be part of the access model, not an afterthought.
“Contribute selected, sanitized indicators to the CCRNet Intelligence Exchange and receive matches against intelligence shared by other members.”
Participation will be selective, transparent, and reversible. Raw reports and evidence stay outside the exchange; contributors choose which eligible indicators to share.
Designed for responsible intelligence
- ✓Private by defaultYour organization’s workspace and source material remain isolated.
- ✓Explicitly opt inNothing enters the exchange simply because it is in CCRNet.
- ✓Sanitized indicatorsShare selected signals, not complete reports or case files.
- ✓Human verificationMatches are investigative leads, never findings of wrongdoing.
CYBERCRIME AI, RESPONSIBLY
Intelligence for research, training, and prediction.
Purpose-built data
Develop cybercrime-specific datasets and models around reports, tactics, infrastructure, indicators, and outcomes.
Evidence-backed development
Evaluate training and prediction work against traceable source material and deterministic retrieval results.
Controlled access
Organization and case isolation, permissions, retention controls, and auditable activity are foundational requirements.
Human-reviewed outputs
AI predictions and connection suggestions are leads—not verdicts—and must receive qualified human review.
ONE NETWORK · FOUR CONNECTED LAYERS
A research network with multiple paths to access.
Community education, independent research, public-interest access, and paid institutional deployments can share one governed technical stack. Earned revenue, if introduced, would support continued research, open tools, and public knowledge resources.
Explore the platform previewSubmit structured intelligence
Capture incidents, financial loss, entities, indicators, evidence references, and consent in one guided flow.
Community · Researchers · Public-interest organizationsMap external threat activity
Organize authorized dark-web, Telegram, marketplace, forum, and illicit-channel intelligence with provenance and handling controls.
Threat researchers · Intelligence teamsConnect cases with Knolo
Search authorized case packs, review explainable connections, map recurring infrastructure, and preserve source lineage.
Investigation teams · Fraud operationsBuild evidence-aware models
Access governed datasets, evaluation workspaces, and APIs for fraud scoring, trend forecasting, and model training.
Public-interest teams · ResearchersCORPORATE R&D
CCRNet is Hive Forensics AI Inc. research.
HIVE FORENSICS AI INC. researches open and responsible AI technology for public safety, fraud prevention, cybercrime analysis, and trustworthy access to knowledge.
CCRNet is the company’s cybercrime research and public knowledge infrastructure. Knolo is a broader open-source project for investigations, education, archives, knowledge systems, local government, and sensitive or offline environments.
About Hive Forensics AI Inc. and CCRNet R&DR&D PREVIEW
See the direction of our technical work.
The planned CCRNet API is part of ongoing research into report ingestion, deterministic search, connection review, and LLM-assisted cybercrime workflows.
Read the technical R&D documentationCCRNet and its API are not generally available. There is no expected launch date, and the documented architecture and interfaces may change as research progresses.