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Generative AI

Custom AI Solutions

When nothing off the shelf fits your process.

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Generic Tools Don't Solve Specific Problems

Your workflow is particular: how you process applications, how field teams quote, how support navigates technical documentation. Nothing quite fits.

Bending a general tool to a specific process often costs more than building the right thing in the first place.

Built For Your Workflow. Owned By You.

Document processing, voice-to-data, image recognition, and decision support: designed around how your business actually operates.

You own the code, the data, and the documentation. If you want to take it in-house later, you can.

What you get

  • Deep discovery into your workflow, constraints, and edge cases
  • A technical design and working prototype you approve first
  • The complete solution, built for your specific process
  • Integration with your existing tools and systems
  • Testing against real scenarios from your business
  • Training, documentation, and full source code ownership

How We Work

  1. 01

    Discover

    We observe the manual workflow, map constraints and edge cases, and confirm technical feasibility before anything is built.

  2. 02

    Prototype

    A working prototype on your real data, approved by you before full development begins.

  3. 03

    Build & Test

    Iterative, with real scenarios and exception handling. You are involved throughout.

  4. 04

    Deploy & Hand Over

    Deployed, documented, and monitored through the first weeks. Ownership transfers to you.

What to Expect

Hours of work finish in minutes
Without the errors that repetition brings when a person does it by hand.
Scales without proportional cost
Ten times the volume on the same team.
Consistent at scale
Your criteria applied identically every time. No fatigue, no drift.
Complete ownership
Code, data, and documentation are yours. No lock-in by design.

Where This Has Been Needed

What clients came to us 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.

Construction technology · Kenya

“We want to use AI, but we do not know where to start or what could go wrong.”

Building the wrong thing first is the most common way AI budget disappears. A ranked plan costs a fraction of the first mistake it prevents.

A growing startup had more AI ideas than it could fund and no way to rank them. We mapped where AI would return most across client matching, onsite support and back-office work, sequenced it into what to build first, second and third, and set a lightweight risk and governance framework alongside it so the question of what could go wrong was answered before anything was built.

Central banking · Bank of Tanzania

“Our people have heard of AI. They do not actually use it.”

Training that runs on generic examples does not transfer. If it is built on the work your team did last week, it is being used the following morning.

We led the AI upskilling programme for executive and personal assistants at the Bank of Tanzania, built on how adults actually learn rather than on tool demonstrations. It ran on their real work: correspondence, diaries, complex travel, document drafting, and taught a working mental model for what to delegate, how to direct it, and when to check it.

Applied AI · Built on public clinical guidance

“The answer is somewhere in our documents. Nobody can find it.”

Contracts, policy manuals, SOPs, product catalogues. Wherever knowledge is locked in long documents that only two people know how to navigate.

Built against Kenya's national diabetes guidelines: chosen because a wrong answer in a clinical setting is dangerous rather than merely annoying: the assistant answers questions in plain English and attaches a citation that jumps straight back to the source page. It declines the questions it should not answer instead of guessing at them.

Common Questions

Three tests: does it repeat often enough, can AI learn the rules, and do you have examples to learn from. We will tell you honestly if something simpler would do.

You approve a working prototype on your real data before full development. You are validating throughout, so you are not seeing it for the first time at the end.

We build for the volume you expect, not just today's. If you process 200 records monthly and might need 2,000, we design for that.

You do: code, data, documentation, and any models trained on your data. You can hand it to another developer or take it in-house.

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