Business Analyst Tool Kit (AnalystKit)

One local-first Python package for preparing spreadsheets, answering business questions, managing requirements, and sharing repeatable reports. The analyst-facing interface uses upload → confirm → report. No external API key or hosted database is required for the core product.

Status: All 16 capabilities have initial local implementations and upload screens. These are tested with representative fixtures, but broader validation against diverse real business workbooks is still needed before production use.

Get started

Requires Python 3.10 or newer. Clone the repository and run:

python -m pip install -e '.[app]'
analystkit app

The command opens a local upload interface with all 16 tasks. Choose the task, upload the files, confirm the selected table and business fields, and review the result and supporting rows. Download HTML, Excel, or JSON reports. Your file is processed on your machine; the temporary uploaded copy is removed after the run. The interface does not send the file to an AI API.

Start with the synthetic files in examples/: use sales.csv and finance.csv for Reconcile two KPIs (key Invoice ID, value Net Sales), events.csv for Analyse a funnel, pricing.csv for Simulate pricing, experiment.csv for Compare experiment groups, and requirements.csv or meeting_notes.txt for the requirements tools. sales.csv also has 24 monthly periods for Forecast monthly values. These are demonstration records, not real company data.

Command-line and Python usage:

analystkit inspect sales.xlsx
analystkit profile sales.xlsx --sheet Orders --header-row 4 --output profile.html
analystkit prepare august.csv september.csv --date "Invoice Dt" --amount "Net Value" --duplicate-key "Order ID" --output prepared.xlsx
analystkit audit budget.xlsx --output audit.xlsx

from analystkit import inspect, profile

print(inspect('sales.xlsx').candidates)
result = profile('sales.xlsx', sheet='Orders', header_row=4)
result.save('profile.xlsx')  # .html and .json are also supported

from analystkit import prepare, audit, MetricCatalog
prepared = prepare(['august.csv', 'september.csv'], amount_columns=['Net Value'])
prepared.save('prepared.xlsx')  # Prepared rows, changes, warnings
audit('budget.xlsx').save('audit.xlsx')

catalog = MetricCatalog()
catalog.add(key='net_sales', name='Net sales', description='Sales after refunds',
            calculation='Invoice amount minus refunded amount', source='Invoices and refunds',
            grain='One row per invoice', owner='Finance', status='approved')
catalog.save('metrics.json')

from analystkit import reconcile, variance, forecast
result = reconcile('sales.xlsx', 'finance.xlsx', key='Invoice ID', amount='Net Amount')
result.save('reconciliation.xlsx')

forecast_result = forecast('monthly_sales.xlsx', date='Month', value='Sales', months=3)
forecast_result.save('forecast.html')

from analystkit import save_setup, run_saved
settings = dict(date='Date', amount='Sales', segment='Region',
                start_a='2026-01-01', end_a='2026-01-31',
                start_b='2026-02-01', end_b='2026-02-28')
save_setup('setup.json', task='variance', sample_files=['january_february.csv'], parameters=settings)
baseline = run_saved('setup.json', ['january_february.csv'])
baseline.save('baseline.json')
updated = run_saved('setup.json', ['new_export.csv'], previous='baseline.json')
updated.save('monthly_change.html')

Suggestions are not confirmed metric definitions. In particular, do not treat an inferred Amount field as revenue without choosing the correct business rule.

Product plan: one package, 16 capabilities

The requirements assistant will use deterministic extraction and templates in the API-free core. An optional local model could later improve drafting. Drafts always need analyst review.

Shared experience and rules

1. Upload and detect: Find sheets, tables, header rows, field candidates, and source row numbers. Support renamed/reordered columns and new columns. Identify ambiguous layouts instead of choosing silently.
2. Confirm meaning: Ask about grain (one row per order or item), dates, currency, metric definitions, joins, and repeated events when they affect the answer. Save confirmed choices as reusable profiles.
3. Preview the calculation: Show selected rows, exclusions, time range, and filters before a business result is calculated.
4. Explain and verify: Reports separate calculated facts from possible explanations and show the input cells or rows. Reconciliation components must sum to the full difference; forecasts must show backtested error.
5. Export and repeat: HTML for reading, Excel for verification, JSON for automation. Reuse profiles only if critical schema and grain checks still pass.

The local interface offers a Save this setup for next time download after a successful repeatable analysis. Upload that file in Rerun a saved analysis, choose the new table, and confirm any renamed critical column. For a previous-run comparison, also upload its JSON report.

The interface will contain Prepare, Analyse, Manage requirements, and Share and repeat sections. The Python API will call the same calculation functions as the interface. The initial implementation prioritizes file processing and correctness over a dashboard full of inactive controls.

Development gates

• Phase 1: Inspect varied workbook layouts; profile an uploaded file; export source-linked findings; implement cleaning previews, audits, and metric definitions. Initial implementation complete. Further workbook layouts and usability refinement remain.
• Phase 2: Exact reconciliation bridges, additive segment changes, and saved reruns with critical-field review. Initial implementation complete.
• Phase 3: Ordered stages, distinct-customer cohorts, and event-level waiting times. Initial implementation complete.
• Phase 4: Hand-checked scenarios, baseline forecast backtests, and group comparison uncertainty. Initial implementation complete.
• Phase 5: Labelled-note extraction, draft traceability, and source-preserving combined reports. Initial implementation complete.
• Release hardening: Real workbook trials, accessibility and upload walkthroughs, larger-file performance, additional edge-case fixtures, and packaged distribution. Remaining.

Run the current tests with python -m pip install -e '.[test]' followed by pytest -q.

Scope of the first release

The current importer supports .xlsx, .xlsm, and .csv; it does not run macros or recalculate formulas. Excel formula values may need to be recalculated and saved in Excel before numerical analysis. Data preparation combines rows, trims spaces, parses selected dates and amounts, aligns explicitly confirmed column names, and flags duplicate keys; it does not join tables. Ambiguous slash dates are set aside for review. The metric calculation is documentation, not executable SQL; attaching an approved definition records a reference and does not change the selected columns or calculation. The audit flags possible issues, not proven errors. Forecast ranges reflect past errors, not calibrated prediction intervals. Experiment output does not establish causal impact without a valid assignment design. Meeting extraction recognises explicit labels and leaves other notes unclassified; it does not invent acceptance criteria. Large workbooks, merged headers, multiple adjacent tables in one sheet, and legacy .xls files need further work.