Data entry ops · Production

Lido reduces Smoker CPA's data-entry time from 6–7 hours to 2 hours per return

The problem

Smoker CPA's clients — many of them Amish and Old Order Mennonite — delivered financial records as thick stacks of handwritten pages with no totals or structure, forcing interns to spend six to seven hours per return on manual data entry and pushing returns past the April 15 deadline.

First attempt

The firm went digital but the core manual burden remained — staff scanned documents into Adobe and still stamped totals onto pages by hand.

Workflow diagram · grounded in source
1
Handwritten documents arrive
Trigger
Clients' handwritten financial records arrive as thick stacks of pages with no totals, no structure, and often barely legible penmanship.
▾ source quote
“financial records arrive as thick stacks of handwritten notepad pages — farm books, asset lists, income summaries — with no totals, no structure, and often barely legible penmanship”
2
Lido extracts numbers and descriptions
Ai action
Lido pulls numbers and descriptions from handwritten client documents with near-perfect accuracy, even on the worst penmanship.
▾ source quote
“Lido pulls numbers and descriptions from Smoker's handwritten client documents with near-perfect accuracy — even the worst penmanship. No more guessing whether a handwritten seven is actually a zero.”
3
Classify headers and sort line items
Ai action
Lido's AI classifies headers across 60+ payroll document formats and sorts line items into the right tax categories — federal, state, and local — automatically.
▾ source quote
“Lido's AI to classify headers across 60+ payroll document formats and sort line items into the right tax categories — federal, state, local — automatically”
4
Staff receive clean structured data
Output
Staff get clean, structured data they can work with right away.
▾ source quote
“Staff get clean, structured data they can work with right away”
5
Partner notes digitized
Output
Partners write handwritten meeting notes, run the pages through Lido, and get clean digital summaries back.
▾ source quote
“Partners write handwritten meeting notes, run the pages through Lido, and get clean digital summaries back”
Reported outcome

Within the first two weeks of tax season, intern data-entry time fell from six to seven hours per return to two, at least tripling their speed.
The firm filed significantly fewer extensions than the prior year, and Andrew estimates 99.9% extraction accuracy even on the worst handwriting.

Reported metrics
Intern data-entry time per return — beforesix to seven hours per return
Intern data-entry time per return — aftertwo hours per return
Intern processing speed improvementat least tripling their speed
Extraction accuracy99.9%
Show all 6 reported metrics
intern data-entry time per return — beforesix to seven hours per return
intern data-entry time per return — aftertwo hours per return
intern processing speed improvementat least tripling their speed
extraction accuracy99.9%
tax extensions filedsignificantly fewer extensions than the prior year
outside vendors replaced by Lidofour outside vendors
Reported stack
LidoAdobe
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Source
https://trylido.com/customers/smoker-cpa
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Within the first two weeks of tax season, intern data-entry time fell from six to seven hours per return to two, at least tripling their speed.

What tools did this team use?

Lido, Adobe.

What results were reported?

Intern data-entry time per return — before: six to seven hours per return; Intern data-entry time per return — after: two hours per return; Intern processing speed improvement: at least tripling their speed; Extraction accuracy: 99.9% (source-reported, not independently verified).

What failed first in this deployment?

The firm went digital but the core manual burden remained — staff scanned documents into Adobe and still stamped totals onto pages by hand.

How is this data entry ops AI workflow structured?

Handwritten documents arrive → Lido extracts numbers and descriptions → Classify headers and sort line items → Staff receive clean structured data → Partner notes digitized.

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