Geralcus R&D Lab

Side projects & lab notes.

My personal lab for side projects, supply chain experiments, and applied AI. I share what I build and learn at ggcd.tech.

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A workflow, thoughtfully renewed

Keep the knowledge.
Upgrade the workflow.

Records and forms from a legacy VB6 system can connect to a modern workflow without discarding the knowledge behind them. This illustration keeps source references and human judgment in the loop.

Illustrative document

Explore a stage
Needs human review

An ambiguous document stays flagged. AI assistance does not clear the approval gate.

Legacy knowledge

Start with the knowledge already there.

Keep the original records, forms, and business context close. The operational system remains the source while a modern workflow takes shape around it.

Operations · Workflow analysis

Workflow impact.

Adjust volume, effort, and adoption to compare two workflows. Human review and corrections stay included.

Illustrative scenario. These editable assumptions are not measured results from my projects.

Balanced example · per month

Current effort

200h / month

400 documents · 30 min each

Modeled effort

125h / month

75% adoption · review included

Capacity released

75h / month

37.5% less handling time

Monthly handling effort

Same volume · hours per month

Current workflow200 h
With the new workflow125 h

Where the modeled time goes

Existing workflow
50 h
New workflow + review
75 h
Bars start at zero. Unadopted documents retain their existing effort.

Adoption sensitivity

Hours released / month

Current scenario 75% adoption75 h / month

Hours released at five levels of workflow adoptionAdoption varies from zero to one hundred percent while document volume and effort per document stay fixed. The vertical scale includes zero. Negative hours mean additional effort, not savings. This is a sensitivity chart of an illustrative model, not a history of measured results. The gold marker shows the current scenario: 75 percent adoption, 75 hours released per month.100500
  1. 0%0 h
  2. 25%25 h
  3. 50%50 h
  4. 75%75 h
  5. 100%100 h

Share using the new workflow

Static example. The five modeled values remain visible.

Assumptions vary by adoption, not by date. Negative values mean added effort; zero means no change.
Explore the value of that capacity

Time released is capacity, not automatically cash saved. Assign a value to that time and account for added running costs.

Estimated capacity value
IDR 3,750,000
Less added monthly costs
IDR 750,000
Capacity value after tool costs
IDR 3,000,000

At these assumptions, 80 documents / month cover the running costs in modeled time value. This is not cash ROI; actual savings require avoided spend. Monthly running costs are held fixed as volume changes. Ongoing maintenance time and one-off migration, training and financing costs are excluded.

Adjust the assumptions
30min
15min
400docs
75%

Count active staff time, including checking, corrections and fallback. Unsupervised agent runtime is not staff time. Documents outside the adoption share keep their current effort.

How I would turn estimates into evidence

Compare the same document types before and after, across representative working days. Track hands-on minutes, end-to-end turnaround and first-pass accuracy together. A faster draft only helps if the review and final delivery are better too.

The calculation: documents × adoption share × (current minutes − new minutes) ÷ 60 = hours released per month. Unchanged work retains its original time; negative results stay visible.

For an actual case study, report the sample size, period and variation alongside the result. Check exception rates, staff adoption and restore-test results separately. This calculator estimates active effort, not elapsed delivery time or a statistical confidence interval.

Research context, not a personal result. A 2025 QJE study (opens in a new tab) of 5,172 customer-support agents found 15% more issues resolved per hour on average with AI assistance. That is a different task and a throughput measure, not a cost-saving rate. METR’s 2026 update (opens in a new tab) explains why selection effects and concurrent agents complicate developer-productivity measurement. Neither study supplies the assumptions above; each workflow needs its own evidence.

Something on your mind?

Based in Jakarta. CV shared directly by email on request.