AI-Powered Automation
Integrating Large Language Models into business workflows for intelligent automation.
Beyond Rule-Based Automation
Traditional automation handles structured, deterministic tasks: "If X happens, do Y." AI automation handles the messy, unstructured work that previously required human judgment— parsing emails, extracting information from documents, routing requests, and making contextual decisions.
At Pure Logic Studio, we integrate Claude, GPT-4, and other LLMs directly into n8n workflows to automate tasks that traditional automation can't touch.
AI Automation Use Cases
Intelligent Email Processing
LLMs classify incoming emails by intent, extract key information (names, dates, requests), and route to the appropriate department or CRM field. Handles nuance that rule-based systems miss entirely.
Document Data Extraction
Extract structured data from unstructured documents—invoices, contracts, resumes, receipts. LLMs understand context and format variations that traditional OCR and regex cannot handle.
Lead Qualification & Scoring
AI analyzes inbound leads based on company size, industry, pain points expressed in forms, and website behavior to assign priority scores and routing logic. Adapts to your ICP over time.
Content Generation & Summarization
Automatically generate meeting summaries, proposal drafts, customer follow-ups, and internal reports. Summarize long threads, documents, or support tickets into actionable briefs.
Support Ticket Triage
Classify support requests by urgency, category, and required expertise. Suggest responses or auto-respond to common issues. Route complex cases to the right team member.
Data Enrichment & Normalization
Clean and standardize messy data inputs—company names, addresses, job titles. Enrich records with additional context by analyzing available information and making inferences.
How We Build AI Workflows
1. Task Analysis
We identify tasks currently requiring human judgment but following predictable patterns— email classification, data extraction, routing decisions. If a human can do it in 30 seconds without specialized knowledge, an LLM can probably automate it.
2. Prompt Engineering
We design and test prompts that consistently produce the desired output format and accuracy. This includes providing examples (few-shot prompting), defining output schemas, and implementing validation logic.
3. Integration with n8n
LLM API calls are integrated directly into n8n workflows using HTTP nodes or custom integrations. We handle rate limiting, error handling, retries, and cost optimization (using appropriate models for task complexity).
4. Human-in-the-Loop Design
For high-stakes decisions, we implement human review workflows. AI makes the initial classification or extraction, flags low-confidence results, and routes to humans only when necessary. This captures 80-90% of the efficiency gain while maintaining quality.
5. Monitoring & Refinement
We log all AI outputs, track accuracy metrics, and continuously refine prompts based on edge cases and failures. AI workflows improve over time as we feed corrections back into the prompt engineering process.
Model Selection
We're model-agnostic and choose based on task requirements, cost, and latency constraints.
Anthropic Claude (Sonnet/Opus)
Our default for complex reasoning, long-context tasks, and enterprise use cases requiring strong instruction-following. Excellent for document analysis and multi-step workflows.
OpenAI GPT-4
Used for structured data extraction, classification tasks, and scenarios requiring function calling or JSON output. Strong developer ecosystem and tool integrations.
GPT-3.5 Turbo / Claude Haiku
For simple classification, tagging, and high-volume tasks where cost matters more than reasoning depth. 10-20x cheaper than flagship models for 80% of use cases.
Open-Source Models (Llama, Mistral)
For self-hosted deployments with strict data privacy requirements or extremely high-volume tasks where API costs become prohibitive. We can deploy and fine-tune open models on your infrastructure.
Cost Considerations
AI automation introduces variable costs based on token usage. We design workflows to minimize these costs while maximizing value.
Model Tiering: Use cheaper models (GPT-3.5, Haiku) for simple tasks and reserve expensive models (GPT-4, Opus) for complex reasoning. This can reduce costs by 90%.
Context Optimization: Minimize unnecessary context in prompts. Don't send entire documents when summaries suffice. Use embeddings and retrieval for long-context tasks.
Caching: Cache LLM responses for identical or similar queries. Many classification tasks see 40-60% cache hit rates, cutting costs in half.
Batch Processing: For non-real-time tasks, batch API requests to take advantage of 50% cost savings on OpenAI and Anthropic batch endpoints.
Typical AI Automation Costs: $50-500/month for mid-market operations
(compared to $3,000-8,000/mo in labor costs for manual processing of the same volume)
Real-World Example: Support Ticket Automation
Scenario: Mid-market SaaS company receiving 200 support tickets per day. Two support managers spend 30 minutes each morning triaging and routing tickets.
• Average triage time: 1.5 hours after ticket submission
• Mis-routed tickets: ~15% requiring reassignment
• Manager reviews edge cases (30/day) in 10 minutes
• Cost: ~$150/month in LLM API calls + 10 min/day labor
• Average triage time: <2 minutes
• Mis-routing rate: ~3%
Results:
- • 85% reduction in manual triage time
- • $850/month labor savings (minus $150 AI costs = $700 net savings)
- • 98% faster ticket routing improving customer satisfaction
- • Managers redirected to complex customer issues instead of classification work
AI Safety & Quality Control
Structured Output Validation
All AI outputs are validated against expected schemas. If the LLM returns malformed data or unexpected results, the workflow automatically retries with a revised prompt or escalates to human review.
Confidence Scoring
For classification tasks, we ask LLMs to provide confidence scores. Low-confidence results are flagged for human review rather than automatically actioned. This prevents silent failures.
Audit Logging
Every AI decision is logged with full context—input, prompt, output, and timestamp. This creates an audit trail for debugging, compliance, and continuous improvement.
Feedback Loops
When humans override AI decisions, we capture the correction and use it to improve prompts or build fine-tuning datasets. AI workflows get smarter over time.
Ready to Automate Unstructured Work?
Book a Logic Audit to identify AI automation opportunities in your operations. We'll analyze your workflows and estimate the labor hours AI can reclaim for higher-value work.
Explore AI Automation Opportunities