Araron
Business Operations Layer
Araron is not another piece of software – it is an operations layer that sits on top of your existing systems (CRM, project tools, sheets, chat) and removes friction exactly where work actually gets stuck: handoffs, recurring tasks, and scattered information.
The Challenge
In growing organisations work increasingly disappears between systems: tasks slip between sales, delivery and support, updates scatter across chat, sheets and tools, and recurring work has no clear owner. The classic answer – "let's introduce yet another platform" – just builds another silo.
The Solution
Araron rolls out in three steps: (1) Diagnose – we map where work breaks inside your existing stack; (2) Solve One Pain Point – one focused module inside your current tools removes a concrete recurring bottleneck; (3) Scale Across Your Stack – we solve more operational problems step by step, without forcing your team into a new platform.
Why different from another SaaS?
No migration. Your existing tools stay – Araron sits on top of them.
One pain, one module. You don't get 200 features – just the one that actually hurts right now.
Operational, not analytical. It doesn't report on the problem – it solves it.
AI-assisted, human-decided. Automation comes with an owner and context – not as a black box.
Results in Numbers
Key Features
Architecture principles
Context-aware AI
Decisions are based on your own documents, tickets and conversations – not a generic LLM, but one that knows how your company actually works.
Graph-based model of work
It sees who owes what to whom and where handoffs break – so it knows where work gets stuck before you notice.
Builds on your existing stack
Two-way integration with CRM, project tools, chat, sheets and email – no need to move your team onto a new platform.
EU hosting, GDPR-compliant
Your data is stored in Frankfurt or Amsterdam and never leaves the EU. Encrypted both at rest and in transit.
No-training guarantee
Your data is never used for third-party model training. Tenant-level isolation, context isolated per customer.
Auditable decisions
Every AI step has an audit trail: who triggered it, with what context, how long it took, what the outcome was. Not a black box.
Why this stack matters
Most "AI tools" are just a chat window in front of an LLM. Araron is meaningfully more: AI is only useful to an organisation if it knows who is who, what depends on what, and where things usually break – and that can only be modelled with a graph- and context-driven architecture.
Technical details for engineers
Technology Stack
AI & Reasoning Layer
From model routing to guardrails – this is where the AI actually decides, with context and control.
- LLM orchestration – OpenAI GPT-4o / Claude Sonnet, with task-type based model routing
- RAG – company context (docs, tickets, conversations, sheets) is fed to the model in real time, minimising hallucinations
- Embedding pipeline – OpenAI text-embedding-3-large + a custom re-ranking layer
- Agent framework – LangGraph-based, stateful agent orchestration (tool calling, human-in-the-loop, retry logic)
- Guardrails – prompt-injection filtering, PII redaction, schema-validated outputs
Knowledge & Context Layer
A live model of how your company actually works: who owes what to whom – and where it usually breaks.
- Graph database (Neo4j) – models the relationships between people, teams, tasks, systems and handoffs; this is where broken work becomes visible
- Vector database (pgvector / Qdrant) – semantic search across the company knowledge base
- Event store (PostgreSQL + temporal tables) – every operational event timestamped with a full audit trail
- Knowledge-graph builder pipeline – entity recognition and relation extraction from incoming data streams
Integration & Sync Layer
Toward your existing stack: bidirectional, low-latency, with conflict resolution.
- Bidirectional connectors – CRM (HubSpot, Pipedrive, Salesforce), project (Jira, Linear, Asana, ClickUp), chat (Slack, Teams), sheet (Google Sheets, Airtable), email (Gmail, Outlook)
- Webhook + polling hybrid – low latency where it matters, cost-efficient where it doesn't
- Schema mapping engine – maps entities from different systems into one unified operational model
- Conflict resolution – CRDT-based approach for updates coming from concurrent sources
Application & Runtime Layer
- Backend – Node.js (NestJS) + TypeScript, event-driven microservices
- Job queue – BullMQ + Redis (scheduled and reactive workflows)
- API – GraphQL (internal) + REST (external integrations) + WebSocket (live updates)
- Frontend – Next.js 15 (App Router), React 19, Tailwind CSS, Framer Motion
- Auth – NextAuth + RBAC, SSO (SAML / OIDC) for enterprise integration
Infrastructure & Observability
- Hosting – Docker + Kubernetes, multi-region deployment
- Database – PostgreSQL (primary) + Redis (cache/queue) + Neo4j (graph) + Qdrant (vector)
- Observability – OpenTelemetry, Grafana, Sentry – every LLM call traced (tokens, cost, latency, output quality)
- LLM cost & quality monitoring – Langfuse: prompt versioning, A/B testing, evaluation
Security & Compliance
- EU hosting (Frankfurt / Amsterdam), GDPR-compliant data processing
- Tenant isolation – separation at both data and model level
- Encryption – at rest (AES-256) and in transit (TLS 1.3)
- No-training guarantee – customer data is never used for third-party model training
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