Home/Work/AI bookkeeping assistant

AI bookkeeping assistant

A bookkeeping product for small businesses that turns photos and email receipts into categorised, GST-ready entries, and explains its own numbers when you ask.

Client
Bookkeeping firm turned SaaS startup
Industry
Accounting SaaS
Year
2025–26
Location
Singapore
Timeline
14 weeks to beta
Team
1 designer, 4 engineers
Services
Product strategy, SaaS, AI features, Mobile app
AI-assisted build
Yes, see below
Receipts inbox: AI suggests category, GST and confidence; finance staff approve in bulk. The client’s name, branding and web addresses have been changed in all screens.
The challenge

AI bookkeeping assistant that reads receipts and prepares GST returns.

The client started as a two-person bookkeeping firm drowning in shoeboxes of receipts from their SME clients. They wanted to productise what they did by hand, without building something accountants wouldn’t trust.

The hard part wasn’t reading receipts. It was making the system honest about uncertainty, so a human could check the 6% that mattered instead of the 100% that didn’t.

What we did

Our approach

  1. Confidence as a first-class featureEvery extracted field carries a confidence score. Low-confidence items go to a review queue; high-confidence items can be approved in bulk.
  2. Local rules, not generic onesCategory suggestions learn from each company’s history, and GST checks validate registration numbers and the 9% rate before anything is filed.
  3. Ask the ledgerA chat panel that answers questions about the books using only the company’s own ledger, always showing the receipts behind each answer.
  4. Mobile captureA camera flow that reads the receipt before the shutter sound ends, so the entry is done before you leave the shop.
Snap a receipt; fields, GST and category are filled in before the shutter sound ends.
AI-assisted

AI in the product, and in how we built it

Receipt extraction and the assistant run on a commercial LLM with structured outputs, wrapped in validation rules that reject anything that doesn’t add up. During the build we used AI coding tools for scaffolding, test generation and migration scripts. Every model prompt and evaluation set was written by our engineers and reviewed with the client’s accountants.

  • Evaluation set of 2,400 real, anonymised receipts
  • Extraction accuracy of 97.8% on totals and 91% on categories
  • No customer data used to train third-party models
In hindsight

What we’d do differently

Our first version of the assistant answered too confidently. In the beta it told one user their August fuel spend was “normal” while a duplicated receipt was inflating it. We added a rule that every answer must cite its records, and the assistant now says “I’m not sure” far more often.

Results
40 minMonthly bookkeeping per client, down from six hours
94.2%Of receipts auto-matched to bank lines
210Paying companies four months after beta
Client
“Sampan pushed us to show the AI’s doubt instead of hiding it. That one decision is why accountants are willing to sign off on what it produces.”
“
Founder, accounting SaaSBookkeeping firm turned SaaS startup
Built with
Next.jsPythonFastAPIPostgreSQLLLM structured outputsSwift

Services on this project: Product strategy, SaaS, AI features, Mobile app.

Have an idea?
Let's ship it.

Tell us what you're building. A real person replies within one business day, Singapore time.