We embed LLM-based decision logic directly into n8n workflows — automated qualification, AI-assisted routing, document generation, and intelligent content delivery. Every agentic flow we build includes human-in-the-loop controls, structured prompt constraints, output validation, and full audit logging.
Production-grade agentic flows — not demos. Every workflow is tested, documented, and designed to operate reliably without constant intervention.
LLM evaluates inbound leads against your ICP criteria, assigns a score, routes to the appropriate sequence, and flags edge cases for human review — all within the n8n flow.
AI extracts structured data from unstructured documents — contracts, CVs, forms — and routes it to the correct system. Includes confidence thresholds and human review triggers.
AI generates personalised outreach, follow-up messages, or reports based on CRM data, then routes through your preferred delivery channel — email, Slack, or direct API.
We design approval gates and review queues for low-confidence AI decisions. The workflow pauses, notifies the right person, and resumes once the decision is confirmed.
Not every task needs the same model. We route to GPT-4o, Claude, or Gemini based on task type — with fallback logic if a model is unavailable or returns an invalid response.
Every AI decision is logged — input, model used, output, confidence, and downstream action taken. Full audit trail for compliance, debugging, and model performance review.
Designed and built an n8n-based AI outreach orchestration layer. LLM decision logic handles lead qualification and appointment routing within structured constraints — high-confidence decisions proceed automatically, low-confidence decisions pause for human review before continuing. Connected HubSpot, Salesforce, and custom backend systems in a single coherent flow.
An n8n agentic workflow uses LLM-based decision logic to make routing, qualification, or content generation decisions within an automated flow. Unlike simple if/else branching, the AI evaluates unstructured inputs — lead data, documents, messages — and determines the next action. Human-in-the-loop checkpoints are inserted where edge cases require review.
We build structured prompt constraints, output validation layers, and fallback routing into every agentic flow. Human-in-the-loop controls catch low-confidence decisions before they cause downstream errors. Full logging records every AI decision for audit and debugging. We use Jest-based testing to validate flow behaviour before deployment.
We primarily use OpenAI (GPT-4o), but also integrate Claude (Anthropic) and Gemini depending on the task. Model selection depends on what you're doing: GPT-4o for general routing and generation, Claude for long-document analysis, Gemini for multimodal inputs. We also build multi-model flows where different nodes use different models.
Yes — and we do this frequently. We scope what the AI needs to decide, design the prompt structure and validation layer, and integrate the node into your existing flow without rebuilding the whole thing. Most AI additions take 1–2 weeks.
Tell us what decisions you want to automate and we'll design the right AI flow for it.
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