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Practical data, AI and automation solutions.
From data strategy to intelligent automation, we help organisations across East Africa turn data into measurable value.
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Solutions for East Africa's core sectors
Practical data, AI and automation solutions shaped by real operating environments.
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Case studies
Real engagements. Measurable results. See how we help organisations across East Africa turn data into higher performance, greater efficiency and lasting impact.
Banking / SACCO
31%
Fewer bad loans within 6 months
A tier-2 bank cut default rates while expanding access to first-time borrowers through an explainable ML model.
"We can now make faster, fairer credit decisions with data we trust."
NGO / Automation
2,100 hrs
Saved per year on reporting
An international NGO replaced monthly manual consolidation with a self-updating Power BI pipeline.
Retail / Ops
14 to 1
Spreadsheets consolidated
A growing retailer unified store, inventory and sales data into a single source of truth.
Insurance / GenAI
45%
Faster claims triage
Claims handlers get answers from approved policy documents, always with the source cited.
Agriculture / Vision
94%
Crop disease detection accuracy
Field officers identify tea and maize diseases from a single photo, even offline.
SACCO / Analytics
18%
Lower loan arrears
A 60,000-member SACCO unified core banking, M-Pesa and contribution data.
Health / Forecasting
35%
Shorter outpatient waits
Daily demand forecasts per clinic now shape rosters and appointment slots.
Manufacturing / MLOps
30%
Less unplanned downtime
Sensor data on critical lines now predicts failures days in advance.
Banking / MLOps
60%
Fewer false positives
Automated retraining and drift monitoring keep a bank's fraud model sharp every week.
Banking / Training
320
Staff trained in 6 months
A commercial bank trained leaders, analysts and branch operations staff in four tailored tracks, with every cohort delivering a working project.
No published story for this sector yet. Ask us about our work in it
Banking / SACCO
Manual appraisal and a scorecard built years earlier meant slow decisions, rising arrears and very few approvals for first-time borrowers with thin credit files.
Discuss a similar project"We can now make faster, fairer credit decisions with data we trust."
NGO / Automation
Five country offices sent indicator spreadsheets in different formats every month, and the M&E team spent a week consolidating them for each donor.
Discuss a similar project"Our country teams now spend their time on programmes, not spreadsheets."
Retail / Ops
Fourteen weekly spreadsheets from POS, ERP and M-Pesa never agreed, so stock decisions were made on gut feel and week-old data.
Discuss a similar project"Every store manager now starts the day looking at the same numbers."
Insurance / GenAI
Claims handlers searched hundreds of pages of policy wording for each query, slowing triage and producing inconsistent answers.
Discuss a similar project"Handlers trust it because every answer shows where it came from."
Agriculture / Vision
Diseases were spotted late by a small team of agronomists covering thousands of smallholder farms.
Discuss a similar project"Our officers assess a farm in minutes instead of a full morning."
SACCO / Analytics
Member data sat in three systems, arrears were spotted late, and SASRA returns took a week of manual work.
Discuss a similar project"The board finally sees one version of the truth every month."
Health / Forecasting
Twelve clinics planned staff from fixed rosters, leaving long queues on peak days and idle teams on quiet ones.
Discuss a similar project"We staff for the patients who will come, not the ones who came last year."
Manufacturing / MLOps
Unplanned breakdowns on two critical lines stopped production several times a month.
Discuss a similar project"Breakdowns became planned maintenance windows."
Banking / MLOps
A fraud model retrained by hand twice a year slowly lost accuracy and buried analysts in false alerts.
Discuss a similar project"Our analysts chase real fraud now, not false alarms."
Banking / Training
Leadership wanted AI in day-to-day work, but staff ranged from executives with no hands-on experience to analysts stuck in spreadsheets, and earlier one-off workshops had not changed how people worked.
Discuss a similar project"Our people stopped asking whether AI was for them and started shipping things with it."
Our standard
We agree how success will be measured before any work starts, then track it with you after go-live. No vanity metrics, no claims we cannot evidence.
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