Governed context infrastructure for AIThe runtime
The runtime
underneath your
AI agents.
Models reason. Zdravo remembers, governs, and proves what happens next. Give Claude, OpenAI, Ollama, and your own agents persistent context, organizational policy, MCP tools, and auditable provenance — without locking your intelligence to one model.
zsh — zdravo mcp
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- Governed context
- Hash-chain provenance
- Model-agnostic
- MCP-native
AI without shared context fails at scale
Stateless models are cheap to deploy and expensive to operate at enterprise scale. Without one governed memory layer, every agent reconstructs context, duplicates work, and loses accountability.
Without a shared context layer
- Every agent re-derives the same organizational context
- Decision history is fragmented across tools and chat threads
- No single audit trail for retrieval, access, or reasoning
- Context windows are consumed by repetition instead of insight
- Model and tenant boundaries become harder to govern
With Zdravo
- Agent retrieves exactly what it needs in one query
- Context window reserved for reasoning, not re-reading
- New team members inherit the org's full memory from day one
- Knowledge Continuity preserves everything when someone leaves
- Token costs drop as memory grows — inverse scaling
How persistent context works
The system that keeps enterprise AI grounded, traceable, and operational across every tool and workflow.
01
LLM
Model input and output
02
Retriever
Finds relevant context
03
Memory graph
Connects decisions and facts
04
SQL + vectors
Stores structure + semantics
05
Audit
Logs retrieval and usage
06
MCP
Connects tools and agents
07
API
Powers custom workflows
Capabilities
The context layer for modern AI systems
Retrieval, governance, and decision history that keep every agent aligned with the same institutional truth.
01
Shared Memory Layer
Every agent reads from the same governed source of truth instead of reconstructing context from scratch.
02
Context APIs
Remember, recall, search, timeline, and decision endpoints let your systems retrieve exactly what they need.
03
Knowledge Graph
Connect product decisions, customer signals, technical context, and policy changes into a structured memory graph.
04
MCP-Native Retrieval
Plug into the tools and agents you already use without rebuilding retrieval logic for every new workflow.
05
Decision Lineage
Version important choices, preserve rationale, and distinguish current truth from historical context.
06
Governed by Default
Access controls, audit logs, PII handling, and model-agnostic retention policies stay in the infrastructure layer.
How Zdravo Works
One diagram. Every enterprise engineer understands it in 15 seconds.
LLM (Claude / GPT / Gemini)
↓
Retriever (semantic search)
↓
Memory Graph (relationships)
↓
Postgres + pgvector
↓
Audit Log (hash-chain)
↓
Embedding Layer (Ollama / OpenAI)
↓
MCP Server
↓
REST API + CLI + SDK
The API is the product
Developers adopt infrastructure through APIs, not screenshots. Every Zdravo capability is available as a first-class REST endpoint.
Base URL: https://www.zdravo.ai/api/v1
POST
/memoriesStore a memory with full contextPOST
/recallSemantic retrieval with trust scoringPOST
/searchFull-text + vector searchPOST
/contextRetrieve governed context chainPOST
/decisionLog an AI-assisted decision receiptGET
/decision/:idRetrieve a decision receiptGET
/timelineProvenance chain for a memoryGET
/auditTamper-evident audit logBuilt for Enterprises That Can't Afford to Get Memory Wrong
Auditable by Design
Every memory, every retrieval, every agent decision — logged in a tamper-evident hash-chain audit log. Show your auditor exactly what your AI knew and when it knew it.
Compliance-Native
PII detection on every ingestion. Row-level security on every table. Your data is never used to train models. GDPR right-to-erasure built in. SOC 2-ready security architecture (audit logging, encryption, access controls).
Model-Agnostic
Bring your own models, your own keys, or use our self-hosted Ollama inference. When the next frontier model drops, your memory stays. The data layer is the product, not the model.
The persistent memory implementation for MCP
Production-hardened MCP server. Every major AI development tool — Claude, Cursor, Windsurf — can read from and write to your org's memory in one config line.
Works Where You Work
One memory layer. Every platform. CLI, IDE, browser, CI/CD — save and recall from anywhere.
M
MCP Server
Claude, Cursor, Windsurf
N
NPM CLI
Terminal power users
C
Chrome Extension
One-click browser capture
V
VS Code
Editor integration
G
GitHub Actions
CI/CD memory
D
Docker MCP
Self-hosted containers
Pricing
Simple pricing, real value
Start free, upgrade when you need more. No credit card required to start.
Free
For individuals exploring AI memory.
$0
Start Free- 15 memories/month
- 1 user
- Keyword + semantic search
- Browser extension
- Email support
Pro
For power users who live in AI tools.
$19/mo
Start Free Trial- Unlimited memories
- 1 user
- Advanced semantic search
- Memory graph visualization
- API access
- Export (JSON, Markdown, PDF)
- Priority support
Most Popular
Team
Your team's shared knowledge graph.
$49/mo
Start Free Trial- Everything in Pro
- Up to 5 users
- Shared memory spaces
- Team analytics
- Admin controls
- SSO ready
- Dedicated support
All plans include a 14-day free trial. No credit card required to start.
Estimate your savings
Enter your real monthly LLM spend. The two assumptions below are estimates you control — adjust them to match your own workflow.
Est. monthly savings
$70
Est. annual savings
$840
Formula: spend × redundant-context % × recall-effectiveness %. An estimate based on the assumptions above, not a guarantee — move the sliders to match your own usage.
Frequently asked
Quick answers to common questions.
Is my data private?
Yes. All memories are encrypted at rest using AES-256. We use server-side encryption for processing and your data is never used to train models. Every access is logged in a tamper-evident audit trail.
Does it work with ChatGPT?
Yes — the bookmarklet captures any ChatGPT conversation in one click.
Is there a free plan?
Yes. Free tier includes 15 memories/month, basic search, and the browser extension.
Can I export my data?
Export in JSON, Markdown, PDF, or Obsidian-compatible format anytime.
Why not just train a model on our data?
Model training on org data has three problems: it's slow (weeks to deploy), it's a black box (hard to audit what the model learned), and it creates GDPR/HIPAA complexity (training on PHI requires extraordinary legal review). Zdravo uses retrieval-based memory instead — your data stays in your database, every access is logged, and you get full auditability without the compliance risk.
What about SOC2?
Our security architecture is built to SOC 2 Type I standards — server-side AES-256 encryption, row-level security on all database tables, PII detection, and a complete audit log. SOC 2 Type I certification is in progress. Contact us for our security documentation and trust report.