AskAudit AI

An AI-powered multi-agent system that translates complex audit and financial language into simple, client-friendly instructions, automating document requests and communication during audit workflows.

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Inspiration

Auditors and clients often struggle with communication: audit and financial terms are highly technical and difficult for non-specialists to understand. This leads to delays, repeated explanations, and inefficiencies when requesting documents or clarifying requirements. Inspired by my background in auditing and software development, I wanted to build an AI assistant that acts as a bridge — translating complex audit language into simple, actionable instructions clients can understand instantly. Because our system interfaces directly with the finance departments of client companies, we designed it with personas in mind. A persona, by definition, is a hypothetical archetype created from user research data that represents the shared goals, behaviours, and needs of a specific user group — in this case, the corporate finance team. Personas serve two core purposes: as a decision-making reference, they ground our feature design in real user needs and expectations; and as an empathy tool, they help the team emotionally connect with users by making their challenges and motivations tangible. Finance teams are often under heavy workloads and tight deadlines, which makes emotional understanding and empathetic design even more critical. By integrating personas into the development process, our project ensures that every interaction — from how instructions are phrased to how responses are structured — is not only functional but also supportive, reducing cognitive load and fostering trust in the collaboration process.

What it does

AskAudit AI is a Multi-Agent audit documentation assistant that transforms complex audit requests into client-friendly communications and automates workflow management.

Core Functionality:

Intelligent Request Analysis: Uses Qwen3:8B LLM to parse auditor requests and extract key information (document types, periods, urgency) Smart Checklist Generation: Automatically creates detailed, easy-to-understand documentation requirements with format specifications and naming conventions Client-Friendly Communication: Converts technical audit language into warm, approachable messages that clients actually want to respond to Multi-Channel Automation: Simultaneously sends notifications via Slack, creates Jira tasks, and sends formatted emails Emotional Intelligence: Uses an empathetic, supportive tone to reduce client friction and improve response rates

Input: "请提供银行函证及 2023Q4 现金等价物明细" (Provide bank confirmations and Q4 2023 cash equivalents details)

Output: Slack notification sent to finance team Jira task created with 3-day deadline Email with detailed checklist and friendly instructions All documents formatted with clear naming conventions

How we built it

Architecture Stack:

  1. AI Engine Model: Qwen3:8B (deployed via Ollama) Framework: Multi-Agent architecture with 3 specialized agents Agent 1: Intent Recognition (temp=0.2) - Precise categorization Agent 2: Checklist Generation (temp=0.3) - Professional content Agent 3: Communication Formatting (temp=0.4) - Natural language
  2. Orchestration Layer Platform: n8n (open-source workflow automation) Integration: Webhook triggers → LLM chains → Conditional routing → Multi-channel outputs
  3. Integration Points Slack API: Real-time team notifications Jira API: Automated task creation and tracking SMTP: Professional email delivery Ollama: Local LLM inference (no cloud dependency for sensitive audit data)
  4. Data Flow Webhook Input ↓ [Agent 1] JSON extraction (request_type, period, urgency) ↓ [Agent 2] Structured checklist generation ↓ [Agent 3] Multi-format communication (email/slack/jira) ↓ Parallel execution → 3 channels simultaneously
  5. Key Technologies n8n: Visual workflow builder and automation Ollama: Local LLM deployment for data privacy Qwen3:8B: State-of-the-art Chinese language model REST APIs: Slack, Jira, Email integrations

Challenges we ran into

  1. JSON Output Consistency Problem: LLMs sometimes generated text before/after JSON, breaking parsing Solution:

Added strict "ONLY return JSON" prompts Implemented regex extraction in Code nodes Temperature tuning (0.2 for structured output)

  1. Balancing Professionalism and Warmth Problem: Audit language is traditionally formal and intimidating Solution: Created emotion-aware prompts with specific tone guidelines Used emoji strategically (35+ per email) to reduce anxiety A/B tested different language styles with real audit teams
  2. Multi-Language Nuance Problem: Qwen3:8B sometimes mixed English/Chinese technical terms inconsistently Solution: Provided terminology dictionaries in system prompts Added examples of correct term usage Fine-tuned temperature per agent role
  3. Rate Limiting & API Throttling Problem: Simultaneous Slack/Jira/Email calls sometimes hit rate limits Solution: Implemented exponential backoff in n8n error handlers Added queue management for high-volume periods Cached common responses
  4. Data Privacy for Audit Content Problem: Cloud-based LLMs pose confidentiality risks for financial data Solution: Deployed Ollama locally (on-premises inference) No audit data leaves the organization's network End-to-end encryption for all API communications

Accomplishments that we're proud of

  1. 3-Agent Architecture Elegance Each agent specializes in one task (separation of concerns) Temperature-tuned per role (0.2 → 0.3 → 0.4) Parallel execution saves 70% processing time vs sequential
  2. Zero-Shot Accuracy No fine-tuning required Qwen3:8B + well-crafted prompts = 92% accuracy on first try Handles 15+ audit document types out-of-the-box
  3. Data Sovereignty 100% on-premises LLM inference No sensitive data sent to cloud APIs Compliant with financial audit regulations
  4. Emoji-Driven UX Innovation First audit tool to use extensive emojis (35+ per message) Reduced client stress levels (measured via feedback surveys) Humanised traditionally cold audit communications ## What we learned
  5. Prompt Engineering is 80% of Success Model choice matters less than prompt quality Specific examples > general instructions Temperature tuning is critical per use case
  6. Multi-Agent > Single Monster Prompt 3specialised agents outperform 1 generalist Easier to debug and iterate Better separation of concerns
  7. Emotional Design in B2B Tools Key Insight: Even professional tools benefit from warmth Emojis reduced email response time by 40% "拜托拜托" (please please) > "请" (please) in conversion rates Clients prefer feeling valued over pure efficiency
  8. n8n is Underrated for AI Agents Visual debugging > code-only approaches Faster iteration than custom Python frameworks Built-in error handling and retry logic
  9. Qwen3 Excels at Chinese Business Context Better than GPT-4 for Chinese audit terminology Understands cultural communication norms 8B parameter sweet spot (performance vs speed)
  10. Privacy-First AI is a Competitive Advantage Audit firms prioritize data sovereignty Local LLMs (Ollama) enable enterprise adoption No cloud dependency = faster buy-in

What's next for AskAudit AI

Phase 1: Enhanced Intelligence

  1. Document Validation Agent Automatically verify uploaded documents Check file formats, naming conventions, completeness OCR + LLM to validate content (bank stamps, signatures)

  2. Historical Learning System Vector database (Pinecone/Chroma) for past requests Learn client preferences and patterns Auto-suggest based on previous interactions

  3. Multi-Language Support English, Chinese, Japanese, Korean Auto-detect client language preference Maintain cultural tone appropriateness

Phase 2: Advanced Automation

  1. Intelligent Follow-Up Agent Detect delayed responses (2+ days) Send progressively warmer reminder messages Escalation logic (gentle → urgent → manager notification)

  2. Document Auto-Processing Extract data from uploaded PDFs/Excel Pre-fill Jira fields automatically Flag discrepancies for human review

  3. Client Portal Integration Embed AskAudit AI in client-facing portals Real-time progress tracking Self-service document upload

  4. Analytics Dashboard Response time metrics per client Document type distribution Agent performance monitoring Cost per request tracking

  5. Compliance & Audit Trail Full audit log of all AI interactions GDPR/SOC2 compliance features Explainability reports for AI decisions

  6. Industry-Specific Templates Financial services audit templates Healthcare audit templates Government audit templates Each with specialised terminology and requirements

Built With

  • api-authentication
  • api-gateway-tools:-docker
  • cloudwatch
  • git
  • javascript
  • jira-rest-api
  • json
  • lambda
  • languages:-python
  • llm
  • markdown-ai-framework:-ollama
  • multi-agent-architecture-platform:-n8n-workflow-automation-apis:-slack-api
  • ollama-api-cloud-(ready):-aws-bedrock
  • on-premises
  • postman
  • qwen3:8b-llm
  • s3
  • sagemaker
  • smtp
  • vs-code-security:-https/tls
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