Selected case studies and the outcomes we delivered

Problem

Motor premium calculations involve many rules, rates, limits, and validations. Done manually, the process was slow and prone to inconsistent or incorrect quotations.

Solution

We built a private comprehensive motor calculator with structured inputs, validation rules, and encoded premium logic — checking fields such as year of manufacture, sum insured, and applicable rating rules before producing a quote.

Outcome

Premium computation became consistent and repeatable, manual checking dropped, and staff get a cleaner, faster experience preparing motor quotations.

Problem

Most small and mid-sized businesses run on spreadsheets — exports from point-of-sale systems, accounting tools, and field teams. The same dataset arrives with duplicate customer records, missing payment dates, five different date formats, phone numbers with and without country codes, and currency values stored as text. Cleaning it manually in Excel is slow, error-prone, and impossible to audit — and every report built on top of it inherits the doubt.

Solution

We built an AI-assisted data quality platform that turns cleanup into a guided four-step workflow. A Data Workspace profiles any uploaded CSV or Excel file — rows, columns, missing values, duplicates, and per-column statistics. Quality Analysis scores the data 0–100 across four dimensions, with a severity-ranked issue breakdown and a plain-language AI summary of what to fix first. A Cleaning Studio applies configurable, rule-based fixes — whitespace, capitalization, date and phone formats, duplicates, missing values — each with a full before/after preview before anything is applied. Validation & Export then produces a before/after comparison, a change log, and the cleaned dataset plus a professional quality report. The AI layer is optional and privacy-first: it receives only summarized metadata and a few masked examples — never raw records — and the product works fully offline with deterministic local logic.

Outcome

Data cleanup becomes a transparent, minutes-long workflow instead of an afternoon of manual Excel edits. Every change is previewed, reversible, and logged, so finance and operations teams get datasets — and quality reports — they can stand behind. Because the cleaning engine is deterministic, the same input always produces the same result: no surprises, no silent edits.