Technovole

AI that does real work, on your real data.

Practical AI, built into the way your business already runs, with people in control of the decisions that matter.

Find where AI fits
4 steps
From idea to adoption, with a test on your data before you commit
10
Industries of domain experience to draw on
PDPO
Registered with Uganda's Personal Data Protection Office

Start where AI will actually help.

Most businesses don't need a moonshot. They need the invoice read, the question answered and the report written, correctly. That's where we start.

  • Generative AI integrations

    Add assistants, search and writing help to the software you already use.

  • Custom AI agents

    Agents that complete multi-step work across your systems, and check with a person when it counts.

  • LLM fine-tuning

    When prompts and retrieval aren't enough, we tune models on your data for your language and domain.

  • Predictive analytics

    Forecast sales, fee collection, stock or yield from the records you already keep.

Why it works here.

  • Your languages

    Tested with how your customers really write, including mixed English and Luganda or Swahili.

  • Your connection

    Designed around slow networks, and costs you can predict.

  • Your team

    Training included, so people use it with confidence.

Careful with what matters.

  • Data protection

    We work within Uganda's Data Protection and Privacy Act, and are registered with the Personal Data Protection Office.

  • Your data stays yours

    We agree where data goes, what's stored and what a model may see, before we start.

  • People in the loop

    Decisions that affect people get human review.

  • Tested for failure

    We test against your real cases, including the ones most likely to go wrong.

We agree the number before we build.

Every AI project starts with one measurable goal, and we check it during the prototype. If it doesn't hold up, you find out early, for a fraction of the cost.

How Technovole measures the value of AI
What to measureHow we work it out
Time savedHours spent on the task before, minus hours after
Errors avoidedMistakes per 100 records, before and after
Money returnedThe value of time saved and errors avoided, minus what it costs to run

From idea to adoption.

  • Step 1

    Find the opportunity

    We identify where AI can make a practical difference.

  • Step 2

    Prototype and validate

    We test a focused prototype on your real data and goals.

  • Step 3

    Build and integrate

    We develop it into your systems, testing at each stage.

  • Step 4

    Adopt and improve

    We help your team adopt it, and keep improving it.

Questions.

Do we need a lot of data to start?
Often not. Many useful features work with your documents and a good model. We'll tell you honestly what your data can support.
Will AI replace my staff?
We build it to take on repetitive work, so your people can focus on the decisions and relationships that matter.
Does our data train public models?
We agree this with you up front, and choose providers and settings that fit your requirements.
What will it cost to run?
We estimate running costs during the prototype, so there are no surprises.
What else can you help with around AI?
ML pipelines, robotic process automation, SQL and NoSQL tuning, and cloud and cybersecurity on Azure, AWS and GCP.

Let's find where AI fits in your business.

Talk to our AI team