Graph-augmented search
Semantic retrieval is combined with knowledge graph relationships and keyword search so recall reflects meaning, explicit terms, and connected concepts instead of relying on one signal alone.
Product walkthrough
Show the operator surface, not just the architecture.
Actual captures from the current HIVE Command Center. This is the proof layer: the command overview, AI briefing posture, transparent search progress, loaded cartridge control, and grounded agent workflow that make H.I.V.E. feel like a real intelligence console instead of a diagram.
These are real HIVE Command Center captures. Select a surface, then open it full size for a closer read on the loaded-state UI.
Memory primitives
Keep context local, ranked, and reproducible.
H.I.V.E. is designed for memory that needs to survive beyond a single session and remain inspectable enough for production, research, and secure deployment paths.
Gravity engine and tiered storage
Importance rises and falls based on recency, frequency, and utility. Hot, Warm, and Glacier tiers let frequently used memory stay fast while colder material remains available without bloating the working set.
Cartridge system
Every data source can run inside its own cartridge with dedicated databases, prompts, metadata, and conflict rules, keeping domain logic separated instead of collapsing everything into one giant memory pile.
AI-powered ingestion
Cortex turns raw text into structured intelligence through synthesis, entity extraction, and tagging. The same pipeline can target local Ollama models or major cloud providers depending on deployment policy.
Natural language analysis
Analyst runs long-form questions over memory data with background jobs, result caching, sampling controls, and cost estimation so expensive reasoning stays deliberate.
Knowledge graph building
Dreamer discovers links between memories over time and records confidence so the graph becomes a usable retrieval layer instead of decorative metadata.
Air-gapped capable deployment
SQLite, SQL Server, Azure SQL, Kusto, and Dataverse storage backends give teams a path from zero-config local installs to enterprise deployment while keeping the system usable in disconnected environments.
Deterministic recall and conflict resolution
Query caching, explainable ranking, and four conflict strategies—AutoSupersede, LLM Evaluation, Human Review, and Disabled—keep retrieval reproducible and contradictions visible instead of silently overwritten.
Architecture
A memory stack that stays modular under pressure.
H.I.V.E. keeps ingestion, storage, retrieval, scoring, and extension layers separate so new data sources and deployment constraints do not turn into one tangled subsystem.
<300
Lines per file
Sub-ms
Hot tier recall
6
LLM providers
∞
Cartridge capacity
Ingestion + storage
Cortex performs synthesis, entity extraction, and tagging. Vault stores the resulting memory through tiered storage and pluggable backends such as SQLite, SQL Server, Azure SQL, Kusto, and Dataverse.
Retrieval + ranking
Oracle combines semantic, keyword, and relationship retrieval. Heuristics applies gravity scoring, tier management, and ranking behavior so recall stays explainable.
Background reasoning
Dreamer grows the graph, Arbiter resolves contradictions, and Analyst handles higher-order analysis over the memory corpus without blocking the main retrieval path.
Extension layer
Cartridges isolate sources, prompts, databases, and metadata schemas. Cells provide the source-specific processors for Reddit, LinkedIn, CSV, sentiment, exports, and any custom domain adapter you need.
Deployment patterns
Memory infrastructure for builders, analysts, and secure teams.
H.I.V.E. can sit underneath agent systems, document workflows, research environments, and operational data streams without forcing them into the same shape.
AI agent memory
Give agent systems persistent memory that can survive across sessions and stay isolated per role, tenant, or objective.
- Cross-session context retention
- Automatic importance ranking
- Multi-agent isolation via cartridges
- Local or cloud inference options
Document intelligence
Turn legal contracts, research papers, technical specs, and policies into queryable knowledge rather than static archives.
- Cross-document semantic search
- Entity and concept extraction
- Conflict and contradiction detection
- Domain-specific cartridge prompts
Scenario analysis
Ingest incidents, support tickets, operations notes, or security events and let the memory layer surface comparable cases and hidden patterns.
- Pattern discovery across scenarios
- Similarity search for prior cases
- AI-assisted root cause analysis
- Custom metadata for domain context
Log and event analysis
Make unstructured logs more readable through synthesis, anomaly detection, and natural-language investigation without flattening everything into one static dashboard.
- Natural language log queries
- Anomaly pattern detection
- Cross-system correlation
- Time-aware importance scoring
Research knowledge bases
Support academic or institutional research by tracking hypotheses, surfacing related work, and making evolving knowledge easier to query.
- Citation and reference tracking
- Auto-discovery of adjacent research
- Hypothesis evolution over time
- Methodology comparison
Enterprise data integration
Unify Slack, Confluence, SharePoint, exports, and other internal systems while preserving per-source rules and access boundaries.
- Multi-source cartridge isolation
- Per-source custom processing
- Cross-source relationship discovery
- RESTful integration paths
Deployment planning
Plan a sovereign memory deployment path.
H.I.V.E. is in development alpha for teams that need memory infrastructure to remain private, inspectable, and portable across local, enterprise, and air-gapped environments.
