Meynos Technical Architecture & Solutions Guide
The comprehensive product definition and engineering blueprint for Meynos—the enterprise Data & AI Control, Management, and Governance Engine sitting between operational data infrastructure and Model Context Protocol (MCP) servers.
The Six Architectural Pillars
Core Capabilities1. The AI Data Highway (CDC + MCP)
Log-based Change Data Capture (WAL / Debezium) streams live table changes straight into Model Context Protocol (MCP) endpoints and SSE streams, completely eliminating query polling.
2. Visual Dataset Config & Versioning
No-code visual interface for security and business teams to define row filters, join rules, and column masking, auto-generating machine-readable JSON/YAML configs for MCP servers.
3. Universal Federated Sources
Protocol-compatible with Apache Iceberg REST catalogs (Polaris, AWS Glue), Delta Lake (via DuckDB delta_scan), Neon/Supabase Postgres, and Databricks Unity Catalog.
4. Zero-Trust Cross-Org Leases
Time-bound, authenticated MCP data leases for external vendors and partner AI agents with differential privacy noise and dynamic on-the-fly anonymization.
5. Agent Blast Radius Budgeting
Dynamic limits on max returned rows, LLM context tokens, and query recursion depth. Prevents unbounded SQL queries from saturating LLM context windows.
6. Cryptographic Proof & Time-Travel
Tamper-proof signed receipts binding prompt context, agent identity, dataset config version, and payload. Replay CDC state microsecond-by-microsecond for incident post-mortems.
Before vs. After Meynos: Unified Multi-Consumer Control Plane
Eliminate point-to-point spaghetti integrations, duplicated security rules, and stale batch dumps. Meynos serves as a single Data Access, Governance, and Exchange Control Plane across human analysts, applications, B2B partners, and autonomous AI agents.
Before Meynos: Fragmented Chaos & Security Gaps
- Scattered custom API gateways inspecting only HTTP headers without dataset awareness.
- Manual CSV exports and stale batch dumps causing compliance and security risks.
- Unmonitored MCP tool calls with no prompt sanitization or blast radius caps.
- Duplicated security rules scattered across databases, proxies, and applications.
After Meynos: Unified Multi-Consumer Control Plane
- Single Dataset Control Plane connecting relational DBs, Delta Lake, and Iceberg.
- Sub-millisecond CDC stream highway feeding AI Agents, Kafka, and partner clean rooms.
- Define once: Central Cedar ABAC and dynamic column masking applied globally.
- Tamper-proof cryptographic Merkle receipts for continuous audit readiness.
Architectural Strategy: "Modular Core / Smart Wrapper"
Rather than re-inventing low-level networking and metadata plumbing, Meynos wraps battle-tested Apache 2.0 components (Envoy, OpenMetadata, DuckDB) with our proprietary policy engine, MCP interception, and dataset versioning.
- Visual Dataset Builder & DVC Config Versioner
- MCP Runtime Interception & Side-Effect Guardrails
- Cryptographic Proof of Execution Merkle Chains
- Time-Travel Microsecond CDC State Replay Engine
- Envoy Proxy / Wasm — L7 traffic & SSE streaming
- OpenMetadata / Postgres — Catalog & entity lineage
- DuckDB / DataFusion — In-memory query rewriting & delta_scan
- Apache Iceberg REST Spec — Direct lakehouse interoperability
Universal CDC Dispatch Architecture: "Capture Once, Stream to All"
Meynos solves the "dual governance dilemma" where organizations write separate security rules for APIs, Kafka message buses, and lakehouse storage. Change events captured from PostgreSQL WAL, SQL Server CDC, or SentrySurface are evaluated once against the active dataset configuration version and broadcast uniformly to all downstream consumer channels.
Maintains persistent Server-Sent Events connections. Pushes progressive schema updates and fresh operational deltas directly to Claude, Cursor, and enterprise agents with automatic prompt token scoping.
Streams change events into Apache Kafka, AWS Kinesis, or GCP Pub/Sub topics with schema registry validation, partition key sharding, and inline column tokenization.
Executes continuous streaming appends into Apache Iceberg REST catalogs (Polaris, AWS Glue) and Delta Lake storage with cryptographic partition commit checksums.
Automated Metadata Discovery & Dataset Configuration Flow
Instead of manual schema definitions or hand-coded API wrappers, Meynos uses automated metadata crawlers to discover source catalogs, infer relationships, and let users configure governed, versioned datasets visually or via code.
Auto-Scan Catalogs
Crawler queries information_schema and lakehouse manifests to discover tables, data types, primary keys, and foreign keys.
Pick Entities & Fields
Users pick discovered entities in the Visual Studio, renaming columns (e.g. cust_usr_id ──► customer_id) and setting aliases.
Inject Security & Masking
Apply column-level dynamic masking (HASH, REDACT, TOKENIZE, DIFFERENTIAL_PRIVACY) and Cedar ABAC policies with role unmask entitlements.
Assign Version & Publish
Tag dataset with a version (v1.0.0). Ingestion pipelines, REST endpoints, and MCP tools deploy instantly.
Single Source per Dataset
Each dataset definition maps to entities residing within a single database or lakehouse catalog (e.g. joining orders and customers within Postgres).
- Native database join pushdown (sub-millisecond execution).
- Zero in-memory compute overhead in the gateway proxy.
- Direct checkpoint tracking for CDC streaming.
Multi-Source Federated Datasets
A single dataset configuration combines and joins entities across heterogeneous storage engines (e.g., SQL orders + MongoDB user profiles + Snowflake analytics).
- Embedded Apache Arrow & DataFusion vector execution kernel.
- Cross-source streaming joins executed safely in-memory.
- Federated pushdown query planner with predicate pushup.
Polyglot Ingestion: Beyond CDC-Only Operations
Relying exclusively on CDC introduces schema-coupling risks, excludes SaaS platforms (Stripe, HubSpot), and burns streaming compute on slow-moving tables. Meynos decouples the ingestion pattern from the dataset definition.
| Ingestion Pattern | Core Mechanism | Primary Use Case | Meynos Implementation |
|---|---|---|---|
| Log-Based CDC | Postgres WAL / MySQL Binlog / Delta log | Real-time transactional replication | Sub-15ms normalized streaming events |
| Webhooks & APIs | Inbound HTTP POST Event Triggers | SaaS platforms (Stripe, HubSpot, Salesforce) | Webhook proxy with HMAC check & DLQ |
| Event Streaming | Kafka / Redpanda / Kinesis Pub/Sub | Domain events (OrderPlaced, UserSignedUp) | Avro/JSON schema validator & consumer |
| Micro-Batching | Polling / Windowing (1–5 min cursor) | High-volume tables lacking binary logging | Timestamp-cursor incremental queries |
| Hybrid Load (Bulk + CDC) | Bulk Parquet Snapshot ──► CDC Handoff | Historical backfills & initial syncs | Zero-lock baseline copy + offset sync |
Caches queries to conserve database compute and avoid query thrashing.
Instant CDC & Webhook delivery to give LLM agents fresh contextual state.
Appends immutable audit logs to S3/ADLS Gen2 cold storage.
Commercial Packaging & Market Solutions
How Meynos solves concrete enterprise operational bottlenecks across cybersecurity and public sector advisory.
Package 1: 'The AI Data Highway' (MCP + CDC Engine)
Stop writing brittle custom database tools for AI agents. Meynos pairs CDC with MCP endpoints so agents get live operational context safely without database polling.
Package 2: 'Zero-Trust External Partner Data Sharing'
Share live dataset subsets with external vendors or auditors without copying databases or managing brittle ETL jobs. Issue time-bound, self-destructing data leases with differential privacy.
Dual Domain 1: SentrySurface (Threat Intelligence)
Powers real-time threat intelligence feeds. When SentrySurface detects an attack surface change or finishes a URL sandbox scan, Meynos captures the delta via CDC and feeds it directly to SOC AI agents.
Dual Domain 2: Vertex Core Group (Enterprise Advisory)
Delivers fixed-fee 'AI Data Readiness Assessments.' Uses Meynos as proof-of-concept software to demonstrate how enterprise databases can be safely queried by LLM agents with deterministic audit trails.