Automate the processes that currently require human judgment.

Most business processes that are "too complex to automate" are actually too complex to automate with rules — but not with AI.

Traditional automation (RPA, rule-based workflows) breaks on exception cases: the invoice with an unusual format, the customer inquiry that requires judgment about account history, the document that does not match the expected template. AI automation handles these exceptions because it understands context, not just patterns. The processes that most benefit from AI automation share common characteristics: they involve reading and interpreting documents, they require classification decisions across many categories, they have clear success criteria but variable inputs, and they currently consume significant human time on routine cases while the edge cases genuinely require judgment. PROPELOO designs AI automation that handles the routine cases autonomously and routes the edge cases to humans — with full context so the human decision is fast.

The AI automation stack.

System Layers

• Intake Layer: Document ingestion, form parsing, API webhooks, email parsing, queue management
• Intelligence Layer: LLM classification, extraction, summarisation, decision recommendation
• Workflow Layer: Multi-step orchestration, conditional routing, approval gates, SLA management
• Integration Layer: CRM/ERP connectors, API calls, database updates, notifications
• Review Layer: Human review queue, confidence thresholds, audit trail, feedback loop

Core Technical Capabilities

• Document Intelligence

  Intelligent document processing — PDF, Word, images, unstructured text. LLM-powered field extraction with validation, confidence scoring and exception routing for low-confidence extractions.

• Intelligent Classification

  AI-powered classification for tickets, emails, documents, leads or transactions — multi-class classification with confidence thresholds, routing rules per class and automatic escalation for ambiguous cases.

• Workflow Orchestration

  Temporal.io or n8n for durable multi-step workflows — LLM steps, conditional branching, parallel execution, retry logic, human approval steps and SLA monitoring.

• Human-in-the-loop

  Review queue UI for low-confidence AI decisions — context display, one-click approval/rejection, feedback capture for model improvement and SLA alerts for pending reviews.

• System Integration

  Connectors for Salesforce, HubSpot, Zendesk, SAP, custom ERPs — AI-processed data written to existing systems, webhooks for trigger-based automation, bi-directional sync.

• Automation Analytics

  Automation rate (% handled without human), accuracy per category, human review volume, time saved, cost per automated case and SLA compliance dashboards.

How we think about AI automation.

The goal of AI automation is not 100% automation. It is the right automation rate — high enough to create value, with human review for the cases where AI confidence does not justify autonomous action.

• Confidence thresholds are the design lever

  An AI classifier that routes cases with confidence > 90% automatically and sends everything else to human review will be highly accurate and have a manageable review queue. Lowering the threshold increases automation rate and decreases accuracy. Raising it decreases automation and increases accuracy. Calibrate thresholds against business requirements: what is the cost of an incorrect automated decision vs the cost of a human review?

  Axiom:

• Human review queues must be fast

  A human review queue that takes 30 minutes to process a case eliminates the time savings of automation for that case. Human review UX must present all context needed for the decision in one view, offer one-click approval/rejection, and route cases to the correct reviewer automatically. Fast human review makes the automation rate threshold less critical.

  Axiom:

• Feedback loops improve over time

  Human review decisions are training data. An AI automation system that captures reviewer corrections and periodically retrains the classification/extraction model improves over time. Without this feedback loop, the system performs at launch quality indefinitely.

  Axiom:

• Audit trails are not optional for regulated processes

  Automated decisions on loan applications, medical records, insurance claims or financial transactions require complete audit trails: what data was received, what AI decision was made and why, whether a human reviewed it, who approved it and when. Build the audit trail into the workflow architecture from day one.

  Axiom:

AI automation design decisions.

• Workflow orchestration tool?

  Impact: Temporal.io for complex, long-running workflows that must survive failures and restarts. n8n for simpler automations where visual workflow design matters.

  • Temporal.io — durable, code-defined, production-grade
  • n8n — low-code, fast to build, limits at complexity
  • Prefect/Airflow — data pipeline focused, good for batch
  • Custom (queues + workers) — full control, more engineering

• Document processing approach?

  Impact: LLM-only for digital documents and well-formatted inputs. Azure Document Intelligence or AWS Textract for scanned documents, invoices and structured forms where layout matters.

  • LLM-only (prompt + extract) — fast to build, variable accuracy
  • Specialised OCR + LLM — better for scanned documents
  • Document Intelligence API (Azure/AWS Textract) — managed, good for standard forms
  • Fine-tuned extraction model — highest accuracy, highest cost

• Classification approach?

  Impact: Zero-shot or few-shot LLM classification for 3-50 categories without labelled training data. Fine-tuned classifier when labelled data exists and accuracy requirements are high.

  • Zero-shot LLM classification — no training data needed, good for many categories
  • Few-shot (examples in prompt) — improves accuracy for specific categories
  • Fine-tuned classifier — highest accuracy, requires labelled data
  • Traditional ML (sklearn) — fast, interpretable, needs feature engineering

• Human review integration?

  Impact: Custom review UI for high-volume review queues. Existing ticketing system for lower-volume reviews where reviewer familiarity with the tool reduces training overhead.

  • Slack/email notification — simple, slow response
  • Custom review UI — purpose-built, fastest review workflow
  • Existing ticketing system (Zendesk/Jira) — familiar to reviewers, general-purpose
  • Low-code tool (Retool/Appsmith) — fast to build review UI

• SLA management?

  Impact: Full SLA tracking for any automation in a customer-facing process. Visibility into queue depth, time-in-queue and overdue cases is required operational infrastructure.

  • No SLA tracking — automation has no performance visibility
  • Timestamp-based alerts — alert when case exceeds age threshold
  • Priority queuing — high-value cases processed first
  • Full SLA tracking — time-in-queue, escalation, dashboard

• Integration with existing systems?

  Impact: API integration where available. File-based integration for legacy systems. iPaaS (Make/n8n) for rapid integration of standard SaaS tools where custom API integration is not justified.

  • API integration (REST/GraphQL) — standard, works for most modern systems
  • Database integration (direct write) — faster, tighter coupling
  • File-based (SFTP, email) — for legacy systems without APIs
  • iPaaS (Zapier/Make) — managed connectors, per-operation cost

What PROPELOO automates.

• Invoice Processing Automation

  AI extracts invoice data (vendor, amount, line items), validates against PO, routes exceptions to AP team with one-click approval.

• Customer Support Triage

  Classify incoming tickets by category, sentiment and urgency — route to correct team, draft suggested response, escalate high-priority automatically.

• Contract Review Pipeline

  Extract key terms, identify non-standard clauses, flag risks, route to legal team with annotated contract and recommended response.

• Lead Qualification

  AI qualifies inbound leads against ICP criteria, enriches with company data, scores and routes to correct sales rep with qualification summary.

• Onboarding Verification

  KYC document processing, identity verification, completeness check — auto-approve passing cases, route exceptions to compliance team.

• Expense Report Processing

  Receipt extraction, policy compliance check, category assignment — auto-approve within policy, route exceptions to manager with policy violation highlighted.

The AI automation stack.

• Orchestration

  Stack: Temporal.io, n8n, Prefect, AWS Step Functions, Custom queue workers

• AI / LLM

  Stack: OpenAI GPT-4o, Anthropic Claude, Azure Document Intelligence, AWS Textract, Instructor (structured output)

• Queue & Events

  Stack: AWS SQS, Redis (BullMQ), Kafka, RabbitMQ

• Review UI

  Stack: Retool, Custom React app, Appsmith

• Integrations

  Stack: Salesforce API, HubSpot, Zendesk, SAP BAPI, Custom REST/GraphQL

• Monitoring

  Stack: Datadog, Custom automation dashboard, Temporal Web UI, PagerDuty

AI automation security for business-critical processes.

• Data isolation

  Documents and data processed by AI automation are often sensitive. Encryption at rest, access controls per workflow, data retention limits and deletion capability for GDPR compliance.

• Audit trail

  Every automated decision must be logged: input data, AI output, confidence score, whether human reviewed it, who approved and when. Required for regulatory compliance and dispute resolution.

• Human approval gates

  High-value or high-risk automated decisions must require human approval regardless of AI confidence. The automation cannot bypass these gates even if confidence is 100%.

• PII in AI prompts

  Document contents often contain PII sent to LLM APIs. Data processing agreements with AI providers, PII redaction before external API calls, and self-hosted models for regulated data.

• Integration security

  Credentials for CRM/ERP integrations must be stored securely (Secrets Manager), scoped to minimum required permissions and rotated regularly.

• Error handling

  Failed automations must not silently drop cases. Dead letter queues, error alerts and manual recovery workflows ensure every case reaches resolution even if the automation fails.

From manual process to automated workflow.

1. 01. Process Mapping

   Document current process, identify automation candidates, define exception criteria and success metrics.

2. 02. AI Model Design

   Classification/extraction model selection, prompt engineering, confidence threshold calibration.

3. 03. Workflow Development

   Temporal/n8n workflow, conditional routing, approval gates, SLA management.

4. 04. Integration

   Source system ingestion, destination system writes, notification infrastructure.

5. 05. Review UI

   Human review queue with context display, one-click actions and feedback capture.

6. 06. Testing & Calibration

   Accuracy measurement on representative sample, threshold calibration, edge case handling.

7. 07. Launch & Monitoring

   Automation rate dashboard, accuracy monitoring, SLA tracking, continuous improvement.

Frequently Asked Questions