Klippa Template Training Requirements:
Why Zero-Training AI Is the Alternative

Klippa's DocHorizon platform offers two paths for bank statement processing. For major banks with high document volume, they maintain pre-trained models that work out of the box. For regional banks, credit unions, or any format not already in their training set, users must create custom templates through the Prompt Builder interface.

The custom template workflow starts in the DocHorizon Prompt Builder, where users either select an existing template or highlight and name fields from scratch. Each field — account holder name, account number, transaction date, amount, description — must be explicitly defined and mapped. Klippa's documentation confirms they are "open to training their machine learning models" for specific cases, which signals that out-of-the-box coverage is not universal.

Template-based extraction works well when your document universe is small and stable. Accounting firms process statements from dozens of banks across their client base — including regional institutions, credit unions, and international banks that Klippa's pre-trained models may not cover. Each uncovered format triggers another training cycle.

Banks periodically redesign their statements — new layouts, merged fields, changed column structures. A template trained on last quarter's format may silently misextract data after a redesign. Klippa's supervised learning approach means someone must catch these errors and retrain the model. For firms processing 50+ clients, this creates an ongoing maintenance burden that compounds month after month.

Zera AI was built specifically to solve the template problem. Instead of learning one format at a time through supervised training, it was pre-trained on 3.2M+ real financial documents — 2.8M+ bank statements, 420K+ invoices, and 847M+ individual transactions. This breadth of training gives it the ability to dynamically recognize any bank statement format without prior configuration or template creation.

Zera Books also processes four document types with the same zero-training approach — not just bank statements. It handles financial statements, invoices, and checks. Klippa's primary focus is on expense receipts and invoices, with bank statements as a secondary capability.

The template training overhead hits hardest for firms managing multiple clients. Each client potentially uses a different bank. Each bank has its own statement format. With Klippa, every new format that falls outside their pre-trained library adds days of setup work before the first statement can be processed. With Zera Books, the same statement is processed in seconds.

For a firm onboarding a new client whose bank isn't in Klippa's pre-trained set, the first month looks like this: identify the format gap, gather sample statements, build or request a custom template, test accuracy, fix misaligned fields, and only then begin routine processing. Zera Books skips all of that — the first upload works.

Moving from a template-based system to dynamic AI doesn't require migration planning or data transfer. Zera Books processes documents independently — you can start using it alongside Klippa and transition at your own pace.