This page is a field report, not a product page. It describes what Nambi is, how it runs, where it is deployed, and what remains unresolved.
§ 01What Nambi is
Nambi is an offline AI tutor. A student talks to it in ordinary language and it teaches, asks questions and adapts to the answers. The difference from most AI tools is the assumption underneath: Nambi does not expect the internet to be there.
It is used as a supplementary ICT module in vocational training, teaching the practical digital skills employers ask for. The same engine is subject-agnostic; the curriculum is what changes.
§ 02How it runs
The model runs on the device itself. There is no API call, no cloud service and no subscription after setup. A session looks like this:
Because everything is local, the tutor keeps working when the connection drops, when data runs out, and when there is no power to charge a router. That is the environment it was designed for.
§ 03Hardware
The design target is the cheapest hardware that still gives a usable tutor. Keeping the model small and the retrieval local is what makes that possible.
§ 04Offline architecture
The model is a custom build, fine-tuned from an open Qwen base for this setting. Nambi combines it with a local curriculum store and retrieval: the curriculum provides the knowledge, while the model provides reasoning, explanation and dialogue. When a student asks something the model is unsure of, the system falls back to the lesson material rather than inventing an answer.
Progress is written to a local database on the device. Reports are generated without a network request and exported for programme reporting.
§ 05The learning system
Learning is conversational. Students ask questions, get explanations in plain language, answer checkpoints, and move at their own pace. The tutor adapts the level of the explanation to the learner and flags where someone is struggling.
- Practical ICT skills: word processing, spreadsheets, email, digital filing and data entry
- Short, conversational exchanges rather than passive video
- Automatic logging of sessions, quiz results and topic coverage
§ 06Classroom deployment
Nambi is deployed at Nurture Africa Vocational Training Centre in Nansana, Uganda, across a pilot of 154 vocational students. Sessions run on shared tablets; no internet connection is used.
Outcome evaluation is in progress. Metric definitions, measurement periods and methodology are documented and shared with funders and research partners on request, and published on the pilot page as they are confirmed.
§ 07Current limitations
- The model is small. It is a good tutor for structured skills, not a general-purpose assistant.
- Coverage is strongest in English, with Luganda support. Swahili and Acholi are planned, not shipped.
- Nambi needs a functioning device. It removes the internet requirement, not the hardware requirement.
- Long-term outcome data is still being collected. We publish what is verified, not what is hoped for.
§ 08Research questions
- How well does a small, local model teach a practical skill compared with a cloud model, once connectivity is removed from the equation?
- What can African-language interaction data, collected in the field, contribute to models that are not built from English internet text?
- What is the real cost of sustaining offline learning infrastructure over several years, including hardware failure and maintenance?
- How does learning change when the tool does not depend on a connection, a subscription, or a vendor staying online?