# VisiFlo (Visi·Flo)

> Make AI visible. Make the next step clear. VisiFlo helps businesses validate AI ideas, evaluate AI systems and agents, and make workflows more reliable through discovery, testing, feedback and practical recommendations.

## Business identity
- Based in Melbourne, Australia.
- Audience: business owners, operations teams, AI agencies and engineering teams.
- Website: https://visiflo.ai
- Business contact: info@visiflo.ai
- Online enquiry: https://visiflo.ai/#early-access. Share the workflow or idea and the decision you need help making. Email is also available.

## Who can enquire
- Examples: retail and e-commerce teams, professional service businesses, property businesses, appointment-based businesses, AI agencies and software teams.
- Other industries and custom-built workflows are welcome. Scope and any industry-specific requirements are confirmed before work starts.
- Industry examples do not claim completed client engagements or industry certification.

## Scoped services
- Validate your idea: Find out whether your AI idea solves a useful business problem. Agree what a good result looks like before you invest in a build. A discovery assessment and a practical starting point.
- Review one AI workflow: Start with an important workflow. Agree what it should do, then check its answers, actions and failure handling where the setup allows. A clear report of checks, findings, evidence and recommended next steps.
- Improve with evidence: Find weak steps, missing checks and what happens when something fails. Get practical recommendations for a more reliable workflow, then agree how to check again. Prioritised recommendations and an agreed recheck plan.
- Tangible deliverables may include a discovery assessment, workflow view, test evidence, findings report or improvement plan, as agreed.
- Workflow reliability and hardening: check for inconsistent results, missed steps, repeated actions and failure handling. Recommend improvements and agree repeat checks; engineering fixes require separate scope.
- AI agent regression testing remains a focused evaluation service for prompt, model or tool changes.

## Custom-built systems and connected services
- Your system does not need to use a named platform. We help you understand whether the whole task works, including the connections between your AI and other software.
- Examples: Custom AI agents, APIs, Web services, Internal apps, Webhooks, Connected business systems.
- Tell us the job: Show what goes in, what should come out and the rules your business needs followed.
- Agree how to check: Use a test app or API to run checks, or review shared examples and records of what happened.
- Get clear findings: See what passed, what failed and what remains unknown. Get recommendations and a plan to check again.
- Checks can cover answers, information passed between systems, approvals, repeated actions and what happens when a connection fails.
- A named platform is not required. Reviews of shared material are distinguished from tests run against a system.

## Platforms and workflow types for assessment enquiries
- Connected business workflows: n8n, Make, Zapier, Microsoft Power Automate. Does each step finish correctly?
- Assistants that take action: OpenClaw, Hermes Agent (Nous Research). Does it act within your rules?
- Chatbots using your knowledge: Microsoft Copilot Studio, Dify, Flowise. Are its answers grounded in your information?
- Custom and multi-agent systems: LangGraph, CrewAI, OpenAI Agents SDK, Custom-built systems. Do the parts work well together?
- Workflows powered by OpenAI / ChatGPT, Claude or Gemini are also welcome for assessment.
- The named tools are examples, not an eligibility list. Custom-built and other platforms are welcome. We agree access and the checks we can perform before work starts.
- Platform names are assessment examples, not claims of native integrations, certification or vendor affiliation.
- Platform examples and enquiry: https://visiflo.ai/#platforms

## Scope and stage
- Enquiries are open. Fit, delivery capability, evidence access, data handling, scope, price and timing are confirmed before work starts.
- The software platform is in early development. There is no general self-service launch.
- Making AI visible means explaining observable behaviour, outcomes, evidence and uncertainty. It does not claim full access to internal model reasoning.
- Public reports and screenshots are illustrative, not completed customer evaluations or measured results.
- Document and trace reviews do not prove a live workflow completed. Executed tests require a suitable authorised environment.
- A scoped assessment does not certify universal safety, compliance or future performance.
- Engineering changes and ongoing monitoring require separately agreed scope. An enquiry requires no payment.

## Public pages
- [Illustrative refund approval test](https://visiflo.ai/#refund-approval-test): A support agent receives an AUD 240 refund request under a sample policy requiring recorded human approval above AUD 100. Check the approval record, tool calls and final refund state. This is an example, not a customer result.
- [Refund approval test matrix](https://visiflo.ai/insights/test-ai-agent-refund-approvals#boundary): Seven cases covering the approval threshold, approval outcomes and retries, with expected tool actions and evidence to verify.
- [Black box to business clarity diagram](https://visiflo.ai/#ai-clarity): Agree the expected result, follow actions and handovers, compare observed outcomes, then improve and repeat checks. Conceptual service diagram; no universal outcome guarantee.
- [Homepage](https://visiflo.ai): Overview, assessment approach, service choices and enquiry form.
- [AI validation services](https://visiflo.ai/services/ai-validation): Discovery, idea validation, system evaluation and improvement recommendations.
- [Testing and validation use cases](https://visiflo.ai/#validation-examples): Simple process diagrams and illustrative screenshots for invoice idea discovery, support answer testing and booking retry validation. Authored evidence, not client evaluations.
- [About VisiFlo](https://visiflo.ai/about): Business identity, purpose and current stage.
- [Agent release testing](https://visiflo.ai/services/agent-release-assurance): Focused regression testing and release review for agencies.
- [Free AI agent testing resources](https://visiflo.ai/#resources): Checklists, an example report and a client handover guide.
- [AI workflow testing checklist](https://visiflo.ai/resources/ai-workflow-testing-checklist): Use eight checks to review answers, actions, human approvals, duplicate requests and recovery. Includes an editable CSV for recording your results.
- [Sample AI agent release report](https://visiflo.ai/resources/ai-agent-release-report): See how an illustrative refund approval failure is documented, what evidence supports holding the release and what to check after a fix.
- [AI agent client handover checklist](https://visiflo.ai/insights/ai-agent-client-handover-checklist): Give clients a record of what was tested, which limitations remain, who owns follow-up and what to recheck after changes.
- [VisiFlo Insights & Articles](https://visiflo.ai/blog): Practical AI testing, chatbot validation and business workflow guides, with sourced incident analysis.
- [Air Canada’s Chatbot Got the Policy Wrong. Would Yours?](https://visiflo.ai/insights/air-canada-chatbot-policy-validation): Explore the Air Canada chatbot case and the 2024 ruling, with practical lessons for testing AI customer-support answers against refund policies and exceptions.
- [Azure AI Outage Lessons for Testing Agent Retry Budgets](https://visiflo.ai/insights/azure-ai-outage-agent-retry-budget-testing): Use Microsoft’s Azure AI outage as context for testing shared retry limits, staff handovers and safe queue recovery in AI workflows.
- [AI Customer Data Access Testing: Lessons from Amadeus](https://visiflo.ai/insights/amadeus-airline-breach-ai-customer-data-access-testing): Test AI customer data access after the Amadeus airline breach: check record permissions, unnecessary data exposure, changed booking references and handovers.
- [Proactive AI Workflow Validation: Find Blind Spots Before Launch](https://visiflo.ai/insights/supermarket-refrigeration-cyberattack-ai-action-validation): Find AI workflow blind spots before launch. Lessons from supermarket refrigeration attacks for testing permissions, stale data, approvals and operational actions.
- [AI Credential Recovery Testing: Lessons from NVIDIA Brev](https://visiflo.ai/insights/nvidia-brev-incident-ai-access-recovery-validation): Test AI workflow recovery after credential exposure. Use the NVIDIA Brev incident to examine revoked keys, dependent tools, permissions and verified restart.
- [AI Incident Response Validation: Lessons from Sony So-net](https://visiflo.ai/insights/sony-so-net-data-breach-ai-incident-response-validation): Validate AI-assisted incident response using the Sony So-net breach as context: identify affected records, separate facts from gaps and verify protective actions.
- [AI Agent Monitoring Validation: Lessons from OpenAI](https://visiflo.ai/insights/openai-ai-agent-monitoring-validation): Test AI agent monitoring coverage, missed alerts and response handovers. OpenAI’s update provides context for checking whether recorded activity leads to action.
- [AI Workflow Fallback Testing: Lessons from Google Cloud](https://visiflo.ai/insights/google-cloud-outage-ai-workflow-fallback-testing): Test AI workflow fallbacks, timeouts and retries using the Google Cloud outage as context. Verify booking outcomes, duplicate prevention and human handovers.
- [AI Workflow Approval Testing: Lessons from Binance](https://visiflo.ai/insights/binance-incident-ai-workflow-permissions-validation): Use the Binance incident as context for AI workflow approval testing: match permissions to actions, recheck changed details and verify stopping and recovery.
- [AI System Validation: Test the Workflow and Human Review](https://visiflo.ai/insights/ai-system-validation-human-review): AI system validation checks answers, actions and human review against business requirements. Learn how to test approvals, final records and decision quality.
- [AI Prompt Injection Testing: Untrusted Files and Approvals](https://visiflo.ai/insights/ai-agent-untrusted-files-approval-testing): Test indirect prompt injection in AI agents reading files and attachments. Check whether outside instructions can bypass permissions or authorise tool actions.
- [AI Agent Accuracy and Consistency Testing: Beyond One Good Run](https://visiflo.ai/insights/ai-agent-testing-accuracy-consistency): Test AI agent accuracy, unsupported answers and consistency across repeated runs. Check facts, tool actions and final records against the same business rules.
- [AI Disaster Recovery: A Business Continuity Checklist](https://visiflo.ai/insights/ai-disaster-recovery-business-continuity): Plan AI disaster recovery and business continuity: stop harmful actions, restore records, verify workflow behaviour and rehearse a controlled restart.
- [AI Agent Audit Trails: Verify Tool Actions and Final Records](https://visiflo.ai/insights/ai-agent-action-audit-trail-validation): Validate AI agent audit trails by matching requests, approvals and tool actions to final records. Includes an executed local customer-record demonstration.
- [How to Test n8n AI Workflows Before Launch](https://visiflo.ai/insights/how-to-test-n8n-ai-workflows): A practical n8n AI workflow testing checklist covering routing, connected tools, duplicate events and final records. Recheck important cases after changes.
- [RAG Chatbot Validation: Test Answers, Sources and Access](https://visiflo.ai/insights/how-to-validate-rag-chatbot-answers): Validate RAG chatbot answers for source support, retrieval quality, current policies and access rules. Test unsupported claims and missing information.
- [How to Validate an AI Business Idea Before Building](https://visiflo.ai/insights/validate-ai-business-idea-before-building): Validate an AI business idea before building: define the problem, measure the current process, compare alternatives and set success criteria for a small pilot.
- [AI Agent Permissions Testing: Tools, Access and Approvals](https://visiflo.ai/insights/ai-agent-permissions-prompt-injection-testing): Test AI agent permissions across connected tools, customer records and approval rules. Include prompt injection attempts and inspect the resulting actions.
- [Custom AI API Workflow Testing: Retries and Final Outcomes](https://visiflo.ai/insights/custom-ai-api-workflow-testing): Test custom AI and API workflows for data mappings, permissions, retries and partial failures. Verify the final business record, beyond HTTP success.
- [How to Test AI Agent Failures, Retries and Recovery](https://visiflo.ai/insights/ai-agent-failure-recovery-testing): An AI agent failure recovery checklist for timeouts, duplicate actions, stale approvals and unavailable tools. Verify retries against the actual final state.
- [AI Agent Regression Testing: Check Actions Before Release](https://visiflo.ai/insights/ai-agent-regression-testing): A practical AI agent regression testing guide: compare prompt, model and tool changes against approval rules, tool arguments and final workflow outcomes.
- [AI Agent Refund Approval Testing: A Practical Checklist](https://visiflo.ai/insights/test-ai-agent-refund-approvals): Test AI agent refund approvals at policy boundaries. Check missing, mismatched and changed approvals, actual tool actions and final refund state before release.
- [AI Agent Client Handover Checklist for Agencies](https://visiflo.ai/insights/ai-agent-client-handover-checklist): An AI agent client handover checklist for agencies: document changes, tested rules, evidence, unresolved findings, owners and the human release decision.
- [FAQs](https://visiflo.ai/#faq): Scope, services, deliverables and how to start.
- [Sitemap](https://visiflo.ai/sitemap.xml): Public indexable pages.

Other consulting service pages describe legacy offers and do not establish current platform features or pricing.