Enterprise AI Memory

MindVault

Give your AI systems persistent, structured memory that grows smarter over time. MindVault combines semantic vector storage with a knowledge graph layer, enabling your AI to store facts, recall context, traverse relationships, and build institutional knowledge that persists across sessions and scales across teams.

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Key Features

Everything you need to integrate MindVault into your production systems.

Semantic Storage

Store knowledge as richly-typed entities with embeddings, metadata, and relationship links. Supports text, structured data, documents, and multimodal content with automatic embedding generation.

Graph Queries

Traverse knowledge relationships using graph queries. Find connections between entities, discover implicit relationships, and answer questions that require multi-hop reasoning across your knowledge base.

Contextual Recall

Retrieve the most relevant memories based on semantic similarity, recency, importance, and access frequency. Automatically surfaces the right context for any conversation or task.

Memory Analytics

Monitor knowledge growth, identify gaps, track usage patterns, and measure recall quality. Dashboards show what your AI knows, what it is learning, and where it needs more information.

API Endpoints

Production-ready REST API endpoints. All requests require a valid API key in the Authorization header.

POST
/api/v1/mindvault/store

Store a new memory with subject, content, metadata, and optional relationship links. Automatically generates embeddings and indexes for semantic search.

POST
/api/v1/mindvault/recall

Recall memories relevant to a query. Uses hybrid semantic and graph search to find the most relevant stored knowledge. Supports filters for time range, entity type, and confidence threshold.

POST
/api/v1/mindvault/graph

Execute a graph traversal query. Find entities connected by specific relationship types, discover paths between entities, and aggregate knowledge across relationship chains.

GET
/api/v1/mindvault/stats

Get memory statistics including total entities, relationships, storage usage, query volume, and knowledge growth trends.

Example Request

curl
curl -X POST \
  https://api.bolor.ai/api/v1/mindvault/store \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "Your input here",
  "options": {
    "max_latency_ms": 5000,
    "min_confidence": 0.8
  }
}'

Use Cases

See how teams are using MindVault in production today.

01

Enterprise Knowledge Management

Large organizations use MindVault as the memory layer for internal AI assistants. Every answered question, resolved ticket, and documented decision becomes searchable institutional knowledge that new team members can instantly access.

02

AI Assistant Memory

Customer-facing AI assistants use MindVault to remember conversation history, customer preferences, past issues, and resolution patterns. Each interaction makes the assistant more helpful for that specific customer.

03

Research Databases

Research teams store experimental results, literature findings, and hypotheses in MindVault. The graph layer connects related findings across papers and experiments, surfacing insights that would be missed in traditional document stores.

Start Building with MindVault

Get your API key and make your first call in under 5 minutes. Free tier includes 100 requests per hour.

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