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Private AI memory platform

H.I.V.E. Roadmap

Durable recall now, governed enterprise memory next.

H.I.V.E. already covers ingestion, storage, scoring, search, tenant isolation, and retrieval intelligence. The roadmap now shifts from proving the core to deepening enterprise recall, governance, and collective systems.

Explore H.I.V.E. See delivered capabilities
Private
Local-first memory handling
Scoped
Tenant-safe isolation
Layered
Hot, warm, and archive recall
Operational
Designed for long-lived deployment

Delivered platform

What H.I.V.E. already delivers

The platform already covers ingestion, storage, search, scoring, tenancy isolation, and retrieval intelligence without giving up deterministic control.

Complete

Core architecture

Phase 1: Foundation

Core Architecture

  • 4-Layer Architecture: Core, Domain, Infrastructure, Application
  • HIVE Orchestrator: Main facade coordinating subsystems
  • Cortex: Ingestion pipeline (Synthesizer → Extractor → Librarian)
  • Vault: Tiered storage (Hot/Warm/Glacier)
  • Oracle: Hybrid search (semantic + keyword)
Foundation

The system’s four-layer architecture and orchestrator established the baseline every later subsystem plugs into.

Complete

Intelligence systems

Phase 2: Memory Physics

Intelligence Systems

  • Heuristics: Gravity Engine for importance scoring
  • Dreamer: Background relationship discovery
  • Arbiter: Conflict resolution (AutoSupersede, LLMEvaluate)
  • Anchor Protection: Prevent critical memory decay
Innovation

Bio-mimetic memory physics: Score = (RecallCount × UtilityWeight) / (TimeSinceLastAccess + 1)^1.5.

Complete

Tenancy

Phase 3: Multi-Tenancy

Cartridge System

  • Cartridges: Hot-swap multi-tenant isolation
  • CLI Wizard: Interactive cartridge creation powered by Forge
  • Zero Cross-Contamination: Per-cartridge databases
  • Smart SQL Indexing: Auto-optimized queries
Game changer

The “game cartridge” model makes context switching instant while preventing data leaks between tenants.

Complete

Reliability safeguards

Phase 4: Reliability safeguards

Safer memory operations

  • State accuracy: Gravity Engine now applies tier changes correctly
  • Enhanced Arbiter: Swappable conflict-handling components
  • Anchor System: Floor protection prevents decay
  • Oracle Filtering: Exclude superseded memories
Stable
Safer rollouts
Trustworthy
State changes apply cleanly

Complete

Analysis layer

Phase 5: Analyst Subsystem

Background analysis

  • Multi-strategy analysis: Aggregation, pattern detection, and insight generation
  • Background jobs: Fire-and-forget workflows with progress tracking
  • Cost control: Pre-flight estimation and per-query limits
  • Result caching: Hash-based deduplication
Background
Continuous analysis
Controlled
Predictable spend

Complete

Flexible deployment

Phase 6: Flexible deployment

Bring H.I.V.E. into your stack

  • Operator controls: Direct administrative workflows when teams need them
  • Tunable behavior: Adjust prompts and memory behavior without rethinking the product
  • Database choice: SQLite, PostgreSQL, MySQL, SQL Server, or MariaDB
  • Portability: The same memory model can move across environments
Milestone

H.I.V.E. can fit the infrastructure your team already trusts instead of forcing a new backend bet.

Complete

Operational readiness

Phase 7: Operational readiness

Reliability for long-lived memory systems

  • Modular core: Safer upgrades and easier subsystem isolation
  • Release discipline: Reliability gates before rollout
  • Faster maintenance: Quicker iteration when memory behavior needs tuning
  • Predictable upgrades: Platform improvements without disrupting stored memory
Durable
Safer production use
Portable
Works across deployment styles
Maintainable
Changes stay isolated

Complete

Recall quality

Phase 8: Real-world search quality

Recall tuned for human-shaped questions

  • Adversarial query review: H.I.V.E. was evaluated against recall, semantic, temporal, negation, and cross-content questions
  • FTS5 Porter Stemming: “drones” now matches “drone” via tokenizer migration
  • Boolean Negation: “NOT safety” correctly excludes safety-related results
  • CHRONOS Temporal: “emails from December” maps to date range filters with future-month fallback
  • Confidence zoning: Results are grouped into clear confidence bands before an answer is trusted
Result

Validated against messy, ambiguous questions instead of toy prompts, so recall quality is grounded in real operator behavior.

Recall-first
Hard queries handled
Temporal-aware
Dates and ranges
Negation-safe
Fewer false positives

Complete

Oracle layer

Phase 9: Oracle Intelligence Layer

Reasoning on top of deterministic search

  • LLM Query Expansion: Pre-search rewriting that bridges human questions to embedding vocabulary
  • LLM Result Validation: Post-search reranking that demotes noise and lifts stronger matches
  • Multi-Pass Search Agent: Confidence-aware escalation when the first pass is not enough
  • Fail-Open at Every Stage: LLM failures fall back to deterministic search
  • LLM JSON Sanitizer: Handles malformed local model output cleanly
Innovation

Deterministic search remains the foundation while LLM reasoning sharpens ranking and interpretation on top.

Reasoning-assisted
Smarter ranking
Confidence-aware
Escalates when needed
Deterministic
Fallback stays intact

Next capabilities

What comes next

The roadmap from isolated memory to governed enterprise intelligence.

In progress

Enterprise search

Enterprise Retrieval Engine

Closing the Gap with Enterprise Search

  • Cross-Encoder Reranking: Dedicated reranker models for 10–20% precision gains
  • Multi-Model Embedding Ensemble: Fused similarity scores across embedding models
  • Learned Relevance Feedback: Improve retrieval based on actual recall behavior
  • Reciprocal Rank Fusion: Replace fixed hybrid score weights with RRF
  • Query Intent Classification: Route abstract and factual queries differently
The gap

These upgrades close the remaining distance between HIVE’s recall pipeline and enterprise products like Elastic, Vespa, or Pinecone + Cohere stacks.

Conditional

Schema reasoning

Semantic Query Engine

Oracle Reasoning Over Custom Schemas

  • Custom Schema Definition: User-defined tables and columns
  • Cortex Semantic Extraction: Hybrid verbatim and LLM extraction
  • Oracle Query Planning: LLM-driven SQL generation
  • Security: Query validation and injection prevention
The big idea

Oracle should understand “Find angry emails from john@example.com,” generate SQL, and reason over custom data structures safely.

Schema-aware
Custom data
Human-language
Natural queries

Priority

Enterprise readiness

SOC2 Compliance

Enterprise Readiness

  • Audit Logging: All operations tracked with seven-year retention
  • RBAC: Role-Based Access Control
  • Encryption Enforcement: At rest and in transit
  • Security Monitoring: Failed login detection and rate limiting
Impact

Unlocks Fortune 500 sales plus healthcare, finance, and government-adjacent markets that need formal controls.

Auditable
Control surface
Trusted
Enterprise procurement

Vision

Multi-model intelligence

The Swarm

Multi-Model Intelligence

  • Cortex (Fast): Gemini Flash or Haiku for JSON extraction
  • Oracle (Smart): GPT-4o or Claude 3.5 for reasoning
  • Dreamer (Deep): Gemini Pro 1.5 with 2M context for whole-vault reading
The big idea

Route reflex tasks to fast models and deep thought to smart models so cost and capability stay balanced.

Vision

Network of Hives

The Federation

Network of Hives

  • HIVE.Work: Pure professional memory
  • HIVE.Personal: Family, hobbies, and medical data
  • HIVE.Auto: Mechanics, physics, and project specs
The big idea

The briefcase model: switch contexts instantly and let persona plus memory access change automatically.

Vision

Cognitive telemetry

Cognitive Telemetry

Psychometrics for Software

  • Drift Detection: Is Oracle becoming aggressive or lazy?
  • Decay Velocity: Are vital memories fading too fast?
  • Graph Health: Find lonely nodes with too few connections
The big idea

Do not just log errors—log cognition, and instrument why the system struggled instead of only whether it failed.

Vision

Collective systems

The Egregore

Collective Consciousness

  • Graph-Based Access Control: Query others’ public memories
  • Sanitized Recall: Extract wisdom without revealing secrets
  • Distributed Infrastructure: Redis hot storage and S3 glacier layers
The big idea

Ten agents, one mind: an Egregore-like system that can know more than any single user while preserving boundaries.