Engagement Letter data extraction

Firm, client, hourly rates, retainer, scope, conflicts disclosures — structured.

Why Ordalis

100% required-field F1

Measured monthly on our public benchmark. Beats Azure Form Recognizer, AWS Textract, and Google Document AI on the engagement letter fixture.

Drop-in Excel output

Cover sheet, Summary, line-item detail, real =SUM() formulas, and a Metadata sheet with source citations. Bookkeepers love this.

Batch + API ready

Use the REST API, the Python SDK, or the Node SDK. Zapier + Slack integrations coming soon.

Example

# Python
from ordalis import Ordalis
o = Ordalis()
result = o.convert('engagement-letter.pdf', template_id='engagement_letter', output='xlsx')
o.download(result['download_url'], out='out.xlsx')

# Extracted fields
- firm_name
- client_name
- effective_date
- scope
- hourly_rates[]
- retainer
- conflicts_disclosed

FAQ

Does Ordalis support handwritten engagement letters?

Yes. Ordalis uses a multi-parser chain (PyMuPDF → Docling → Workers AI) so handwritten or scanned documents fall back to vision-AI OCR automatically.

Can I customize the extracted fields?

Absolutely. The built-in Engagement Letter template is one starting point, but you can chat with our agent to add, remove, or rename columns. Or upload your own JSON Schema / CSV / XLSX header row to define a custom template.

What's the pricing?

Free tier includes 100 conversions/month. Plus ($29), Pro ($99), and Business ($299) scale up from there. Enterprise is custom. See pricing.

Do you train on my data?

No. Your documents are never used to train any model. See security for details.

Ship the output your finance team actually wants

Every extraction comes out as a styled Excel workbook with real formulas, named ranges, and source citations. Not a CSV blob.

Get started free