Real Projects.
Real Results.

Every engagement is different. Here's a cross-section of the data challenges we\'ve solved — across industries, dataset sizes, and business types.

500+ Datasets delivered
98% Average accuracy rate
6 Industries in this portfolio
0 Data losses, ever
Finance

Microfinance Loan Portfolio Audit

A microfinance institution operating across 5 branches had accumulated 3+ years of loan records in separate Excel files — each with different column names, date formats and reference number conventions. Before a regulatory audit, they needed a single, clean, unified dataset.

Challenge 87,000 loan records across 5 inconsistent files
Scope Deduplication, date normalisation, reference ID standardisation
Timeline 4 business days
87K records unified 3,200 anomalies flagged Audit passed
✓ Regulatory audit passed first time
Healthcare

Health NGO Patient Registry Merger

An NGO running health programmes across 3 counties was migrating to a new EMR system. Their patient records existed in 3 different legacy databases, each built at different times with no shared schema. Merging them required deduplication across systems with no common unique identifier.

Challenge No shared patient ID across 3 legacy systems
Scope Fuzzy matching, deduplication, schema alignment, migration prep
Timeline 6 business days
42K patients merged Zero duplicates Migration-ready
✓ 3 legacy systems merged with zero patient data loss
Retail

Retail Chain CRM — 200K Customer Records

A retail chain operating across 8 African markets had a CRM with 200,000+ customer records — accumulated over 6 years with no cleaning. The records had duplicate contacts, inconsistent phone formats across 8 country codes, malformed emails, and blank mandatory fields.

Challenge 200K+ records, 8 country phone formats, 6 years of drift
Scope Duplicate removal, phone normalisation, email validation, blank imputation
Timeline 5 business days
18K dupes removed 99.2% accuracy 8 markets unified
✓ 99.2% record accuracy — highest in company history
Agriculture

Agri-Tech ML Training Dataset Preparation

An agri-tech startup needed to prepare soil composition and crop yield datasets for machine learning model training. Raw data from field sensors had noise, outliers, missing readings and unit inconsistencies that were degrading model performance.

Challenge Sensor noise, unit inconsistencies, 12% missing readings
Scope Outlier removal, unit standardisation, missing value imputation, feature engineering
Timeline 3 business days
34% accuracy gain Outliers removed ML-ready output
✓ ML model accuracy improved by 34% post-cleaning
HR

HR Data Migration — Legacy HRIS to Cloud

A 600-person company was migrating from a 10-year-old on-premise HRIS to a modern cloud platform. Employee records had grown inconsistent over a decade of manual entry — department names varied, job titles were unstandarisable, and contract types used 11 different labels.

Challenge 600 employee records, 10 years of manual entry drift
Scope Standardisation, taxonomy alignment, migration format preparation
Timeline 3 business days
600 records clean Zero data loss On-time go-live
✓ Zero data loss. Went live on schedule.
Hospitality

Hotel Group Inventory Reconciliation

A hospitality group managing 12 properties needed to reconcile inventory data ahead of a group-wide ERP rollout. Each property had its own spreadsheet — 12 different product code formats, inconsistent supplier names and no unified category taxonomy.

Challenge 12 properties, 12 incompatible inventory spreadsheets
Scope Product code standardisation, supplier name deduplication, category taxonomy creation
Timeline 5 business days
12 properties merged Supplier deduped ERP-ready
✓ 12 properties unified into a single inventory master

What Clients Say After the Project

"

Before Chui, our CRM was a nightmare — 50,000 records with thousands of duplicates. They cleaned it in under a week and our sales team finally trusts what they're working with. We saw a direct improvement in outreach conversion rates.

A
Amara Osei Head of Sales, RetailCo Ghana
"

The team was incredibly thorough. They caught errors our internal staff had missed for months. Our financial reports are now accurate and we sailed through our regulatory audit. Worth every shilling.

P
Priya Nair CFO, Fintech East Africa
"

Professional, fast and communicative throughout. They didn't just clean our data — they explained exactly what was wrong and how to prevent it recurring. That education piece alone was invaluable.

D
Daniel Kimani Operations Manager, AgroTech Kenya

Your Business Could Be Next

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