Applied AI that reads Amharic properly.
Document intelligence, speech, vision and analytics built for Ethiopic script, Ethiopian naming conventions and the three languages your users actually work in — deployable entirely within your own infrastructure.
Why the language work matters
- Ethiopic scriptCorrect segmentation and normalisation for Amharic, treated as core engineering.
- Naming conventionsPatronymics and transliteration variants handled explicitly — a common source of false non-matches.
- Three languagesAmharic, Afaan Oromo and English in the same pipeline, including code-switched documents.
- Your infrastructureOn-premises inference, with signed model updates on a schedule you control.
Generic AI often misreads Amharic
A model that performs impressively in English will frequently mis-handle Ethiopic script, Fidel numerals, name transliteration and the code-switching that real Ethiopian documents contain. This is not a marginal accuracy issue — it is the difference between a system people use and a system people work around.
What we build for
- Ethiopic script handling. Amharic text processing that treats the script as a first-class citizen, including correct character segmentation and normalisation.
- Afaan Oromo and regional variation. Language coverage that reflects the populations your service actually serves, not the ones a training set happened to contain.
- Name matching. Ethiopian naming conventions, patronymics and transliteration variants handled explicitly — a major source of false negatives in identity systems.
- Domain vocabulary. Banking, healthcare and public-sector terminology configured for your context rather than left to a general model.
Where it earns its keep
The commercially sensible AI projects are unglamorous: fewer manual re-keys, faster document turnaround, and search that finds the record the operator was looking for. We scope against a measurable business process, not a technology demonstration.
Document intelligence
Extract structured data from scanned forms, IDs, invoices and correspondence — including mixed Amharic and English pages.
Speech and contact centre
Transcription and keyword spotting for Amharic call recordings, to make quality monitoring and dispute review tractable.
Conversational service
Assistants in Amharic, Afaan Oromo and English for routine public or customer enquiries, with a human escalation route that works.
Vision and inspection
Document authenticity checks, damage and asset inspection, and quality control where manual review is the bottleneck.
Data platform
Pipelines, quality controls and dashboards so that the insight survives the project that produced it.
Six rules we hold to
Applied AI in a regulated institution is a governance problem before it is a modelling problem. These are the commitments we are willing to be held to.
Human decision, always
Automated output is an input to a human decision, never the decision itself, wherever a person’s rights or money are affected.
Explain the refusal
If a system declines, defers or flags something, the operator can see why. Unexplained refusals are unusable in a customer-service context.
Measured on your data
Accuracy is established on a held-out sample of your own records, with the failure modes reported alongside the headline figure.
Your data, your custody
Training and inference can run entirely inside your infrastructure. We do not require your records to leave the country to make the system work.
Drift is monitored
Language, formats and behaviour change. Performance is re-measured on a schedule, and degradation triggers a review rather than a surprise.
No covert use
We decline scopes involving undisclosed monitoring of individuals, or any application where the lawful basis has not been established.
Sources
Core systems, scanned documents, call recordings, sensor and transaction data.
Ingest & prepare
Extraction, language detection, normalisation of Ethiopic script and mixed-language text.
Models
Language, document, speech and vision models — pre-trained, fine-tuned on your data, or both.
Consumption
APIs into your existing systems, or operator-facing tools where the workflow lives.
Indicative only. Model choice, hosting location and evaluation approach are agreed per engagement after a data review.
Questions we are asked in procurement
Answers written for the people who have to defend the decision internally.
Why is Amharic support such a differentiator?
Will you use our data to train models for other clients?
How do you measure whether it works?
Can it run without internet connectivity?
What if the model degrades after deployment?
Bring us the problem, not a specification.
The most useful first conversation is a technical one: what has to work, by when, and what has already been tried. We will tell you plainly whether it is something we should be doing.