Build a Voice-Powered Expense Reporting System
An AI consultancy builds voice AI for expenses by transcribing audio files into structured data. The system uses a language model to extract vendor, date, amount, and category from the text.
The build complexity depends on audio quality and the number of required fields. A system for a team of traveling sales reps using clear mobile recordings is a 2-week build. Integrating with a legacy ERP system or handling noisy background audio adds a week of development.
We built an expense tool for a 15-person construction company. Their project managers recorded expenses on-site, a process that took 5 minutes per report manually. The new system processes audio memos in 8 seconds and syncs directly to their QuickBooks account.
What Problem Does This Solve?
Off-the-shelf apps like Expensify offer voice entry, but it is a closed system. It requires users to open the app and follow a specific prompt format. If a project manager wants to record a quick memo like "bought 15 2x4s at Home Depot for 123.50 for the Elm Street project," Expensify's rigid structure often fails or requires manual correction. It also cannot handle custom fields like a "Project ID" without a higher-tier plan.
The alternative is a manual process where an admin listens to voice memos and enters them into accounting software. For a team of 10 sales reps each submitting 20 expenses a month, that is 200 reports. If each manual entry takes 3 minutes, an admin spends 10 hours a month just on data entry. Transcription errors, like hearing "fifty" as "fifteen," cause accounting headaches.
A technical team might try to connect Google Drive to OpenAI's Whisper API via a tool like Make.com. This works for transcription, but then they hit a wall. Extracting structured data like `{"vendor": "Home Depot", "amount": 123.50}` requires another LLM call with complex prompting and validation logic. This approach is brittle and expensive in a per-operation model, and it frequently breaks on edge cases.
How Does It Work?
We set up a dedicated endpoint where users can upload audio files (m4a, mp3, wav) from their phones. The files are stored temporarily on AWS S3. An AWS Lambda function triggers on new uploads and calls an audio processing API to transcribe the audio to text. This transcription step is optimized for financial terms and completes in under 3 seconds for a 30-second audio clip. We use Python with the Boto3 library to manage the S3 and Lambda interactions.
The transcribed text is sent to the Claude API. We use an engineered prompt that instructs the model to act as an accountant and extract specific fields: vendor, total amount, transaction date, and expense category. We implement Pydantic for data validation, ensuring the amount is always a float and the date is in ISO 8601 format. If validation fails, the entry is flagged for manual review. This extraction and validation step takes about 4 seconds.
Once the data is structured and validated, a Python script using the httpx library posts it to the client's accounting software API, such as QuickBooks or Xero. We handle authentication using secure secrets management in AWS. The system creates a new expense entry, attaches the original transcription as a note, and archives the audio file. We log every step using structlog to a Supabase database table, providing a full audit trail for all 500+ monthly reports.
The entire system is deployed as a serverless application using FastAPI and AWS Lambda, which costs under $30 per month for typical volumes. This avoids server management overhead. We set up CloudWatch alarms that send a Slack notification if the API error rate exceeds 2% or if processing latency for any single file goes above 15 seconds. This monitoring ensures issues are caught before they impact accounting.
What Are the Key Benefits?
Process Expenses in 8 Seconds, Not 5 Minutes
From audio upload to a confirmed entry in your accounting software in under 10 seconds. Eliminate the 4-6 minute delay of manual data entry for every single expense.
Pay for Usage, Not for Seats
A one-time build cost and low monthly cloud fees based on actual processing volume. Avoid the $10-$25 per-user monthly fees of commercial expense management tools.
You Get the Keys to the Code
We deliver the complete Python source code to your company's GitHub repository. You have full ownership and can modify the system without vendor lock-in.
Errors Flagged, Not Ignored
Built-in data validation via Pydantic means ambiguous entries are sent for human review, not silently entered incorrectly. We provide a simple review dashboard.
Connects Directly to QuickBooks and Xero
Native API integrations push structured data directly into your existing accounting platform. Your finance team works in the tool they already know.
What Does the Process Look Like?
Week 1: System Scoping & API Access
You provide sample audio expense memos and grant us developer access to your accounting software's sandbox environment. We define the exact data fields to be extracted.
Week 2: Core Logic & Model Build
We build the transcription and data extraction pipeline using AWS Lambda and the Claude API. You receive a demo link to test the system with your own audio files.
Week 3: Integration & Deployment
We connect the pipeline to your live accounting system and deploy the full application. Your team begins submitting real expenses through the new workflow.
Week 4+: Monitoring & Handoff
We monitor the system for 30 days, fine-tuning prompts and handling any edge cases. You receive the full source code, documentation, and a runbook for maintenance.
Frequently Asked Questions
- How much does a custom voice expense system cost?
- The cost depends on three factors: the number of custom fields, the complexity of the target accounting system's API, and the need for a user-facing review dashboard. A system that extracts four standard fields and posts to QuickBooks is a straightforward build. Integrating with a custom ERP or handling multiple languages increases the scope. We provide a fixed-price quote after a 30-minute discovery call.
- What happens if the transcription is wrong?
- The system is designed for failure. If the Claude API cannot confidently extract a required field like 'amount' or if the total is $0.00, the entry is automatically flagged. It appears in a simple review queue with the original audio and transcription for a human to correct in 15 seconds. This prevents bad data from ever reaching your accounting system.
- How is this better than just using the Expensify app?
- Expensify is a great general-purpose tool, but it is rigid. It forces your team into a specific app workflow and struggles with custom business logic, like linking expenses to internal project codes. Our build is workflow-native; your team can use any voice memo app they prefer. We build the logic around your process, not the other way around. You also own the system, avoiding per-seat SaaS fees.
- What kind of audio quality is required?
- The system works well with standard smartphone microphone recordings in a relatively quiet environment, like inside a vehicle or office. It can handle moderate background noise, but a recording from a loud construction site may have lower accuracy. We can process common audio formats like M4A, MP3, and WAV. During scoping, we test with your real-world audio samples to set expectations.
- Does my team need to speak in a specific way?
- No, the system is designed for natural language. A user can say "Just paid for lunch with the Acme team at The Daily Grill, it was sixty-two dollars and forty cents on my Amex" and the system will extract the vendor and amount. We tune the extraction prompt to match your team's typical phrasing and vocabulary, which we learn from your sample recordings.
- Who handles the ongoing cloud hosting costs?
- The system is deployed to your company's AWS account, so you pay for hosting directly. This ensures you have full control and ownership. For a team processing up to 1,000 expense reports per month, the combined cost for AWS Lambda, S3, and API calls is typically under $50. We provide a detailed cost breakdown and help you set up billing alerts.
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