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Why this shape

What this looks like when the system has to survive production

Most automation projects stall on 2 things: the step that needs a human-like judgment call, and the system that has no API. A rule-based bot breaks on the first and cannot reach the second. We put an LLM at the judgment step with a confidence gate and a review queue, and a computer-use agent at the portal, operating the browser like a trained clerk with a screenshot of every step.

Everything runs in your infrastructure. Self-hosted n8n means no per-operation pricing and no data leaving your network, and the workflows are documented so your own team can extend them. Delivered automation includes a revenue layer across 14 sales tools, support across 7 channels, and document intake that posts clean records straight to the core system.

What you get

Delivered as a working production system, in your cloud account

Self-hosted n8n

Deployed in your cloud account or on premises, with version control, environments and alerting around it.

AI at the judgment step

An LLM classifies, extracts or decides with a confidence score; low confidence goes to a person, high confidence proceeds.

Document intake

Vision-language extraction of scans, invoices and forms, validated field by field against schema and policy.

Computer-use agents

Browser automation for portals and legacy screens with no API, with approval on sensitive steps and a replay log.

Monitoring and exceptions

Every run logged, failures alerted, exceptions queued for review with the context attached.

Runbook and handover

Documented workflows your team can extend, plus a walkthrough with your engineers.

Architecture

Where AI sits in the workflow

n8n carries the fixed path. At the steps that need judgment, an agent proposes an action through a policy gate, and anything consequential waits for a person.

When this is the wrong tool

If a process is fully deterministic, it needs plain automation and no model at all. We say so, and we build it that way, because it is cheaper to run and easier to audit.

Triggerwebhook · schedule · chatPlannerLangGraph state machineTools via MCPCRM · DB · APIs · n8nPolicy gatestep limit · cost ceilingHuman approvalhigh-stakes actionsAction + traceLangfuse · audit log

Proof

Delivered systems closest to this work

Every figure comes from a system running in production for a real client.

Enterprise SaaSUSA and UK

An autonomous revenue layer that closed the gaps no rep could watch

1. Problem. 14 disconnected sales tools, no consistent lead qualification and shaky CRM data. Revenue was slipping through gaps that no single person could track across multiple regions at once.

2. Solution. A round-the-clock revenue layer on n8n and LangGraph. Real-time lead qualification, automatic CRM enrichment, personalized outreach across email and LinkedIn, predictive deal health scoring, and live pipeline reports delivered to leadership with no sales ops in the loop.

n8nLangGraphGPT-5.6HubSpot API

3. Impact

48%
Reduction in manual sales ops workload
1.8x
Increase in qualified lead throughput
$340K
Pipeline generated in the first 6 months
900+
Automated workflow triggers daily
E-CommerceUSA and UK

Support that handled growing volume with flat headcount

1. Problem. A fast-scaling US e-commerce brand had support infrastructure buckling under volume. Response times were slipping, agents were overwhelmed, and brand content across 5 social platforms was inconsistent. Hiring more people was the expensive fix.

2. Solution. An autonomous support platform across Instagram, TikTok, Facebook, LinkedIn, X, chat and email. Claude handles incoming queries with knowledge-grounded reasoning, processing refunds, routing tickets and resolving most issues without escalation. A sentiment layer escalates only what needs human judgment.

n8nClaude Opus 5.5GPT-5.6Zendesk API

3. Impact

61%
Queries resolved without escalation
44%
Reduction in average response time
23%
Improvement in audience engagement
24/7
Global coverage, 0 added headcount
Global EnterpriseUSA and Europe

Program governance that flags risk before it escalates

1. Problem. A global enterprise running programs across multiple regions had no real-time view of execution. Status updates were manual summaries from people with a stake in how they read, and risks surfaced only after they had escalated.

2. Solution. A program management layer that ingests live communication from Slack, email and ticketing. It tracks timelines and blockers as they develop, raises predictive risk flags before they escalate, and produces clean executive briefings on demand. A persistent decision-memory layer preserves context across leadership changes.

GPT-5.6LangGraphNeo4jSlack API

3. Impact

28%
Improvement in on-time delivery
Earlier
Risk identification across teams
Reduced
Manual overhead in status reporting
Live
Executive visibility across pipelines

Questions

AI automation agency: what buyers ask first

A focused n8n automation on 1 workflow with AI decision logic ships from $3,500 in 2 to 4 weeks. Multi-system automation with document intake, approvals and dashboards runs $15,000 to $60,000 over 8 to 16 weeks.