INTERACTIVE PROJECT STORIES
Follow a record.
Change the outcome.
Run a billing workflow, compare employee research, and trace a research claim. Three working examples, built with fictional data.
Find the exception.
Give the time back.
Every month, the same spreadsheet cleanup stood between an analyst and the real work. I built an Alteryx workflow that consolidated vendor data and put discrepancies in view.
Workflow design, vendor and month controls, data preparation, reconciliation, and analyst enablement.
Follow the invoice.
| Invoice | Vendor | Source period | Billed | File |
|---|---|---|---|---|
| NORTH_CO | Jan-26 | $960 | vendor.xlsx | |
| Juniper LLC | JAN 2026 | $1,080 | export.csv | |
| ALDER-co | 2026/01 | $1,200 | billing.xlsx | |
| NORTH_CO | Jan-26 | $2,140 | vendor.xlsx | |
| Juniper LLC | JAN 2026 | $1,440 | export.csv | |
| ALDER-co | 2026/01 | $1,560 | billing.xlsx | |
| NORTH_CO | Jan-26 | $1,680 | vendor.xlsx | |
| Juniper LLC | JAN 2026 | $1,800 | export.csv | |
| ALDER-co | 2026/01 | $1,920 | billing.xlsx | |
| NORTH_CO | Jan-26 | $2,040 | vendor.xlsx | |
| NORTH_CO | JAN 2026 | $1,680 | export.csv | |
| ALDER-co | 2026/01 | $2,280 | billing.xlsx |
The difference is the next action.
The billed amount is $160 above the ledger. Check the vendor rate before approving the invoice.
See the transformation logic
vendor = title_case(trim(replace_separators(raw.vendor)))
period = parse_month(raw.period)
key = (vendor, period, raw.invoice_id)
expected = ledger.lookup(key)
delta = raw.billed - expected.amount
review_if(duplicate(key) or delta != 0 or missing(account))Original simplified example. My separate MetLife timing result came from Power Query; Alteryx supported later selected workflows.
12 fictional rows demonstrate the process. The separate reported MetLife result was about nine hours to twenty minutes per month using Power Query.
A rating is a number.
A comparison gives it meaning.
A score alone cannot tell a team where to focus. I connected research, source-specific parsing, Power BI comparisons, and reporting so the context traveled with the numbers.
Python research tools, metric definitions, dashboard design, product offerings, and executive presentations.
A score with context.
- Captured rating
- 3.2 / 5
- Source reference
- research-sample / cedar / Q1-2026
- Missing distribution
- Unavailable · not inferred from a mean
Career Opportunities
Inspect the ratings and calculation
| Company | Career Opportunities | Included |
|---|---|---|
| Cedar Works | 3.2 / 5 | Focus |
| Juniper | 3.9 / 5 | Selected peer |
| Alder | 3.7 / 5 | Selected peer |
| Maple | 3.8 / 5 | Selected peer |
peer_average = mean(selected_peers.same_scale_ratings)
gap = company_rating - peer_average
report.chart_values = checked_values
report.observation = draft_for_analyst_reviewThe peer average is an unweighted mean of the selected company ratings. It is not an industry benchmark.
Fictional reconstruction of the earlier research-rating workflow. The full case study explores taxonomy-level sentiment and the later Voice Lab installation and training handoff.
Follow the work
behind the answer.
More agents can mean more unsupported claims. I build systems where research, critique and review have different jobs—and every finding can be traced back to a source.
Product interfaces, Python workflow contracts, role-based orchestration, evidence ledgers, and review states.
Follow the claim.
Data + code + design
Validate the calculation before it becomes a claim. A useful answer separates what is known from what still needs review.
The guide improved onboarding.
The initial claim is plausible, but it needs testing against the source material.
The release adds an onboarding guide.
06 May · Fictional research evidenceSee how a finding keeps its source
{
"claim": "Guide adoption in sample: 60%",
"evidence": ["E-02"],
"calculation": "18 / 30",
"review": "supported",
"unknown": "effect on completion time"
}A fictional research example informed by TradeLab and local orchestration work. It runs entirely in this page.
Try something in your browser.
Useful tools and small experiments. Explore the Browser Lab.