Architecture

From Restaurant Data to Autonomous Operations

A four-layer stack that turns raw, fragmented restaurant data into autonomous action. Each layer builds on the one below — and each is available independently via API.

LAYER 4

Restaurant Automation

Operations, marketing and growth executed end to end.

OperationsMarketingGrowthDecisions
ACTIONS EXECUTED
LAYER 3

AI Agent Engine

Autonomous employees that monitor, analyze and act.

Claw ManagerClaw MarketingClaw Analytics
AGENTS DEPLOYED
LAYER 2

Restaurant Intelligence Platform

APIs that expose restaurant intelligence to any application.

KnowledgeMenuFood GraphAnalyticsAgent APIs
INTELLIGENCE EXPOSED
LAYER 1

Food Intelligence Graph

Menus, dishes, ingredients, nutrition and preferences — connected.

GraphSemanticsRelationships
DATAINTELLIGENCEAGENTSACTIONS
Data Ingestion

Connect every data source.

ClawPOS sits alongside your existing systems — no replacement required. We connect to the data you already have and turn it into intelligence.

POS Systems
Orders & Sales
Customers & CRM
Inventory
Reviews & Reputation
Locations
Menus
Operations
Design Principles

Built on three non-negotiables.

PRINCIPLE 01

Intelligence, Not Just Data

We don't store and display — we structure, connect and compute. Every data point becomes a node in a living graph.

PRINCIPLE 02

Composable, Not Monolithic

Every layer is independently accessible via API. Use the graph, the platform, or the agents — mix and match as you need.

PRINCIPLE 03

Human-in-the-Loop

AI employees recommend and execute, but humans stay in control. Every action is auditable, reversible and configurable.

Layer Deep Dive

Under the hood of every layer.

Each layer is independently engineered, composable via API, and built on proven infrastructure. Here is the technology that powers each tier.

LAYER 1

Food Intelligence Graph

A property-graph knowledge base modeling the entire food universe — ingredients, dishes, cuisines, nutrition, preferences and their relationships. Built on graph database technology with vector embeddings for semantic similarity search, and continuously enriched by NLP pipelines that parse menus, reviews and culinary text at scale.

Graph DatabaseVector EmbeddingsNLP PipelinesEntity ResolutionOntology EngineSemantic Search
LAYER 2

Restaurant Intelligence Platform

A unified API gateway exposing five intelligence surfaces — Knowledge, Menu, Food Graph, Analytics and Agent APIs. Built on a microservices architecture with API-first design, each service independently scalable. Request routing, authentication, rate limiting and caching handled at the gateway; business logic in dedicated services that query the graph and analytics engines below.

API GatewayMicroservicesREST + GraphQLRedis CacheEvent BusOpenAPI Spec
LAYER 3

AI Agent Engine

An agent orchestration runtime that powers Claw Manager, Marketing and Analytics — and lets you build custom agents. Each agent combines a language model with restaurant-specific tools (query the graph, run analytics, execute actions), a memory layer for context persistence, and a planning module for multi-step task execution. Agents run as isolated workloads with configurable autonomy levels and full audit trails.

LLM OrchestrationTool Use FrameworkAgent MemoryPlanning EngineGuardrail LayerAudit Runtime
LAYER 4

Restaurant Automation

The action layer where intelligence becomes operational change. Connects to POS systems, scheduling tools, marketing platforms, inventory management and delivery aggregators via certified integrations. Actions are executed through a permissioned action bus with human-in-the-loop checkpoints, idempotent execution and rollback capability. Every action is traceable back to the agent insight that triggered it.

Integration HubAction BusWebhook EngineIdempotent ExecutorRollback ManagerPermission Layer
Data Pipeline

From raw data to intelligence in minutes.

Every data point follows the same five-stage pipeline — ingest, validate, enrich, compute and serve. Latency from POS event to queryable intelligence: under 5 minutes for streaming sources.

STAGE 01
Ingest

Connect POS, delivery, inventory, CRM and reviews via APIs, webhooks or batch uploads. Streaming and batch modes.

STAGE 02
Validate

Schema validation, deduplication, anomaly detection and data quality scoring. Bad data is quarantined, not propagated.

STAGE 03
Enrich

NLP parses menus and reviews. Entity resolution links dishes to the food graph. Customer identity stitching across sources.

STAGE 04
Compute

Analytics engine runs forecasts, margin models, segmentation and anomaly detection. Results materialized for low-latency serving.

STAGE 05
Serve

Intelligence exposed via five APIs, agent tools and webhooks. Cached at the edge for sub-100ms response on hot queries.

Security & Privacy

Security is architected in.

Not bolted on. Every layer — from data ingestion to agent execution — is designed with zero-trust principles, tenant isolation and least-privilege access.

Tenant Isolation

Every restaurant's data is logically isolated at the database, cache and agent runtime levels. Cross-tenant querying is architecturally impossible.

Zero-Trust Access

Every service-to-service call is authenticated and authorized. No implicit trust within the network. mTLS for all internal communication.

Immutable Audit Log

Every data access, API call and agent action is logged to an append-only audit store. Tamper-evident, exportable, retained for 7 years.

Privacy by Design

Customer PII is tokenized at ingestion. Analytics run on pseudonymized data. Data minimization — we never collect more than the intelligence requires.

Agent Guardrails

AI agents operate within strictly defined tool permissions and budget limits. No agent can access data or execute actions outside its authorized scope.

Data Residency

Choose your data region: US, EU or APAC. Enterprise customers can opt for dedicated infrastructure and customer-managed encryption keys.

Scalability & Deployment

Built to scale from one store to 10,000.

The same architecture powers a single-location café and a global enterprise chain. Elastic infrastructure, multi-region deployment and 99.95% uptime SLA on Enterprise.

Cloud-Native

Containerized microservices on Kubernetes. Auto-scaling based on request volume and agent workload. Multi-AZ within every region.

KUBERNETES · AUTO-SCALING · MULTI-AZ

Multi-Region

Deployed across US, EU and APAC regions. Global load balancing with latency-based routing. Cross-region disaster recovery with RPO < 1 minute.

3 REGIONS · GLOBAL LB · RPO < 1MIN

High Availability

99.95% uptime SLA on Enterprise plans. Redundant every layer — database replicas, cache clusters, API gateway instances, agent runtimes.

99.95% SLA · N+2 REDUNDANCY

Performance

API p95 latency under 120ms for cached queries, under 400ms for graph traversals. Agent response time under 3 seconds for typical tool-use tasks.

p95 < 120MS · AGENT < 3S

Edge Caching

CDN-backed edge cache for hot API responses in 300+ locations. Graph query results cached with intelligent invalidation on data updates.

300+ EDGE LOCATIONS

Enterprise Options

Dedicated VPC, private endpoints, customer-managed keys, SSO/SCIM, custom SLAs and a named solutions engineer. Available on Enterprise tier.

DEDICATED VPC · CUSTOM SLA
Build on the Stack

Every layer is available via API.

Start with the Food Graph, deploy an AI agent, or integrate the full stack. The architecture is yours to compose.