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Generative AI

AI Agents & Agentic AI

AI that decides what to do next: then does it.

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Scripts Handle Steps. Most Work Needs Judgement.

Automation follows a fixed path, and works right up until something varies: an unfamiliar invoice format, a different currency, a CV that does not fit the template.

Then it stalls and someone steps in. Your team ends up supervising the automation instead of being freed by it.

Agents That Carry a Goal to the End

An agent is given a goal, not a script. It breaks the work into steps, picks the right tool for each, and adapts mid-task instead of stopping.

It can receive an invoice, match it to the purchase order, spot a quantity mismatch, check the delivery note, and either resolve it or route it with the reasoning attached. One run, three systems.

What you get

  • An agent scoped to one task with one measurable return
  • Connections to your CRM, email, spreadsheets, accounting, and messaging
  • Multi-step execution that adapts when conditions change
  • Human checkpoints at every step where an error would be expensive
  • Sandbox runs against real historical cases before launch
  • Full activity logs: what it did, and why it decided to

How We Work

  1. 01

    Scope One Task

    Frequent, spanning several systems, currently needing a person to carry it. Narrow scope separates delivery from science project.

  2. 02

    Map the Decisions

    We document every judgement a person makes today, including the unconscious ones. That sets the agent's boundary.

  3. 03

    Sandbox

    It runs against your real historical cases, including the messy ones, before it can affect anything live.

  4. 04

    Supervise, Then Widen

    Launch with review on every run and loosen as it proves itself. You set the pace and can tighten again anytime.

What to Expect

Multi-step tasks finish end to end
Work spanning systems completes in one run, instead of queueing at every handover.
Judgement calls handled mid-task
It adapts when something varies rather than stalling unnoticed.
Output scales without headcount
The constraint moves from people to process.
Full audit trail
Every action and its reasoning logged. Reviewable, not a black box.

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

Automation follows a path you define: cheaper, and right for most repetitive work. An agent works out the path itself, which matters when inputs vary. We recommend automation when it fits, which is often.

Checkpoints at every costly step, escalation to a named person when confidence is low, and full logging so you review decisions rather than discover problems by accident.

Sandbox first, against real historical cases. Permissions are scoped deliberately: an agent that only needs to read never gets write access.

No. Agents work through your existing email, spreadsheets, CRM, and accounting tools. The point is to work across your stack, not add to it.

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