What this solves
07
Orchestrated pipelines that make decisions, not just move data.
Your ops team is burning analyst hours on processes a well-wired pipeline could handle end-to-end — with better accuracy.
Get a quote Typical timeline: 2–6 wks
Process
How it works
01
Discover & Map
We audit your current processes and identify the highest-impact automation opportunities.
02
Design & Build
Most workflow automation stops at "if this, then that." We go further: multi-step pipelines with LLM decision nodes that classify, extract, route, draft, and escalate based on actual content — not just field values.
03
Monitor & Optimise
Your automation ships with monitoring, alerting, and a handover pack. We stay available for optimisation.
What's included
Most workflow automation stops at "if this, then that." We go further: multi-step pipelines with LLM decision nodes that classify, extract, route, draft, and escalate based on actual content — not just field values. We build on N8N, Make, and Python orchestration frameworks, integrating with your existing stack and wiring in GPT-4o, Claude, or open-source models where the task demands it. Every pipeline ships with structured logging, dead-letter handling, and a run-history dashboard so your team can audit every automated decision.
What you receive
- Process decomposition workshop output — step-by-step map of the current state with time-and-cost annotations
- N8N or Make workflow graphs (exported and version-controlled) or Python DAG code in a private repo
- Prompt engineering docs — system prompts, few-shot examples, and eval datasets for every LLM node
- Structured logging schema and run-history dashboard (Grafana or Retool depending on stack)
- Error handling playbook — dead-letter queue configuration, alert routing, and manual override procedures
- Handover session with your ops team including live walkthroughs of every trigger, node, and failure path
Typical outcomes
- 6–12 hours of manual analyst work eliminated per week per pipeline
- LLM classification accuracy >94% on domain-specific document types after fine-tuning
- Sub-60-second end-to-end processing for workflows that previously took 24–48 hours
- Zero-touch routing for 70–85% of incoming requests, with confident escalation paths for the remainder
- Full audit log of every automated decision with confidence scores and input snapshots
- Reduced error rate compared to human-handled steps (especially data extraction and transformation)
Technology we use
Ready to discuss your project?
Book a free sessionIntegrations
Tools & integrations we work with
N8NMake (Integromat)PythonLangChainOpenAI GPT-4oAnthropic ClaudeCeleryRedisPostgreSQL
We integrate with your existing stack — no rip-and-replace required.
Questions
Common questions about AI Workflow Automation.
Simple trigger-action automations break the moment a document is slightly different or an edge case appears. Our pipelines use LLM nodes to handle variability — classifying intent, extracting structured data from unstructured input, and making routing decisions that would require a human reading the content. Zapier can't do that.
Yes. We regularly onboard onto existing N8N or Make instances. We'll audit your current workflows, identify fragile points, and extend or harden them. We can also migrate Make workflows to N8N if self-hosted control is a priority.
Every LLM node in our pipelines outputs structured JSON with a confidence field. Below a set threshold, the item is routed to a human review queue rather than proceeding automatically. We also run evals on a held-out sample during build to establish a baseline accuracy before go-live.
All pipelines include retry logic with exponential back-off, circuit breakers for flaky upstream services, and dead-letter queues that preserve failed items for reprocessing once the service recovers. Your team gets alerted immediately via Slack or email.
For high-throughput use cases we move off N8N/Make and onto a Python-based architecture using Celery workers backed by Redis, deployed on your cloud of choice. This scales horizontally with no per-task pricing.