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The data team your business never got round to hiring.

Research, analytics and applied AI for East African companies that need answers without building a department.

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Trusted by a central bank
Led the AI upskilling programme for executive staff at the Bank of Tanzania.
Six years of consulting
Research, analytics and AI across healthcare, retail and financial services in East Africa.
Systems that run in production
Forecasting models, document pipelines and retrieval systems, built and handed over.

Every Decision Is Made Across a Gap

On one side sits your data. On the other sits something you have to do: enter a market, change a price, hire or hold. Almost none of it ever makes the crossing. There are three ways across, and which one you need depends entirely on where your gap sits.

From outside

Research & Monitoring

Surveys, feasibility studies, and evaluation that tell you what is actually happening, before you commit the budget.

  • Data Collection & Surveys
  • Feasibility & Landscape Studies
  • Customer & Stakeholder Research
  • Market & Competitive Research
  • Programme & Impact Evaluation

From inside

Analytics & Machine Learning

Dashboards, forecasts, and one agreed version of the truth, built from data you already have.

  • Customer Analytics
  • Supply Chain & Operations Analytics
  • Performance Dashboards
  • Diagnostic & Root Cause Analysis
  • Predictive & Prescriptive Modelling
  • Data Maturity Assessment

Straight through

Generative AI

Chatbots, automation, and AI agents that take repetitive work off your team, with control staying where it belongs.

  • Conversational AI & Chatbots
  • Workflow Automation
  • AI Agents & Agentic AI
  • AI Strategy & Roadmap
  • AI Training & Capacity Building
  • Custom AI Solutions

Problems Clients Arrived With

In their words. Names withheld by agreement.

Healthcare · Nairobi

“Our team spends half the day retyping information off documents.”

If your business runs on invoices, application forms, delivery notes or receipts, the same approach applies. The document changes; the pipeline does not.

Prescriptions and clinical visit notes were arriving as documents and leaving as manual data entry, several hours of it a day. We built a pipeline that reads them, pulls out the fields that matter, and passes them on structured, with review kept in place wherever a mistake would be expensive.

Retail analytics · Decision support

“We run promotions. We do not know which ones make money.”

Sales during a promotion and sales caused by a promotion are different numbers. The gap between them is often the entire margin.

Standard promotion reporting counts revenue that would have arrived anyway, which is how a campaign looks successful while quietly destroying margin. Separating the two means comparing against customers who never got the offer, not against the same customers' quieter months, since the people who redeem offers are the heaviest spenders to begin with. The result is a dashboard a marketer can drive without writing code.

Healthcare · Development funding

“We are behind on what we promised a funder, and cannot explain why.”

When a plan underperforms, the useful question is not how far behind it is. It is whether the mechanism it depends on was ever real.

A co-funded programme to create jobs for young healthcare workers was well into its second year and far behind target. Establishing what had actually happened meant rebuilding staff payment records from three unstructured ledgers before a single job could be counted. The finding was that the target had never been reachable: the assumed link between business growth and sustained employment did not exist, which the board received before more money followed the plan.

Every practice area page carries the rest, matched to the need it solves.

What happens next

How We Work

  1. 01

    Discovery Call

    Thirty minutes on the decision or bottleneck you are facing.

  2. 02

    Scope

    We define one task with one measurable return, and tell you if it is not worth doing.

  3. 03

    Build

    Tested against your real cases before anything touches live operations.

  4. 04

    Hand Over

    Your team is trained and owns the result. Support continues if you want it.

The Thinking Underneath

You are hiring judgement more than hours. Read some before you spend anything.

All insights
AI Strategy

Research, Analytics and AI Are Solving the Same Problem

The printing press, the internet, research, analytics and AI have all been trying to close one gap, and AI is the first to cross it

5 min read
Analytics

The Three Roles Data Plays in Your Business

Defensive, protective, offensive: each creates a different kind of value and carries a different way of going wrong

4 min readMeasurement

If You Cannot Measure It, You Cannot Improve It

How long does it take to deliver an order to your customer? Not the target: the actual number, for last month

2 min read

Questions We Hear Often

Honest answers to the things businesses want to know first: starting with the one nobody asks out loud.

The three real risks are a system confidently getting something wrong, sensitive information going somewhere it should not, and spending on the wrong use case first. We handle them in that order: human review sits at every step where a mistake would be costly, we are explicit about which tools process what data and your obligations under Kenya's Data Protection Act, and we scope to one task with a defensible payback before anything is built. If a use case does not survive that test, we say so.

Automations and chatbots show measurable impact within two to four weeks of launch. A strategy engagement delivers a roadmap in three. Every project starts with a pilot so you see results before committing to full scale.

No. We work with Google Workspace, WhatsApp Business, Zoho, QuickBooks, and whatever else you already run. We make your current stack work together rather than replacing it.

No. Every engagement transfers the code, data, and documentation to you, and your team is trained to run it. Clients stay because the work keeps paying, not because leaving is hard.

We will say so. If a use case has no defensible payback, or research is not the right spend for your decision, telling you early costs us a project and saves you a great deal more.

What Would You Fix First?

Thirty minutes on the problem in front of you. We will tell you what it would take to fix, or whether it is worth fixing at all.

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