African-founded / global AI evaluation
Models move fast. Judgment has to hold.
Imbazo builds and runs African-based evaluation teams for ongoing model work—evaluation, red teaming, multilingual review, and technical reasoning. Start with a five-day pilot; scale into a dedicated pod when the work proves its value.
Which response is more useful, accurate, and appropriately scoped?
Answers the request directly, names the uncertainty, and avoids adding unsupported detail.
PreferredSounds complete, but introduces a claim the source material does not support.
ReviewPreference is recorded with a reason, not just a click.
Workflow illustration — not client data.
Start: prove fit on one task
Scale: recurring managed delivery
Extend: add domain or language depth
The work / configured
Choose the workstream.
These are the capabilities Imbazo delivers in a pilot and at ongoing scale. Choose the model decision; we configure the people, calibration, and quality loop around it.
01 / Evaluate
Find the signal between two plausible answers
A calibrated pod compares model outputs, applies your rubric, records the reason for each judgment, and routes disagreements for review.
- Best for
- Preference ranking, SFT review, reward-model signals
- Pod
- Task-tested evaluators + a quality reviewer
- You receive
- Scored outputs, reviewer notes, disagreement patterns
The real question
What fails when the prompt leaves the lab?
Benchmarks can tell you that a model passed. Human reviewers tell you where the answer became misleading, culturally wrong, unsafe, or simply unhelpful.
Imbazo makes that judgment legible. Every engagement is designed around a rubric, independent review, disagreement handling, and evidence your team can inspect.
Your first engagement / 5 days
Prove one task. Then scale the team.
The pilot is the first paid engagement—not the limit of the service. It gives both teams evidence of reviewer quality, useful notes, and program economics before ongoing delivery begins.
- 01
Define the decision
One task, one rubric, clear acceptance criteria.
- 02
Calibrate the room
A small batch reveals ambiguity before volume begins.
- 03
Run independent review
Trainers judge; quality reviewers inspect disagreement.
- 04
Return the evidence
Sample outputs, reviewer notes, error patterns, and a QA report.
African-founded / talent-first
Built by Africans. Built around African talent.
Imbazo was founded to make African talent a first-choice partner in the global AI economy—not an invisible layer in someone else’s supply chain.
That purpose creates a practical buyer advantage. Operating from Africa lets us assemble skilled reviewer pods at a more competitive cost than equivalent teams in higher-cost markets. Task-specific screening, calibration, independent review, and QA evidence protect the standard.
Our network includes vetted graduates and professionals whose education includes the University of Cape Town, Wits University, and the University of Zimbabwe.
Education is a talent signal, not a claimed university partnership. Every trainer is separately screened, task-tested, calibrated, and quality-reviewed.
See how trainers joinStart a conversation / built around the work
Bring us one decision your model needs humans to make.
We will reply with the questions, team shape, and first engagement that make sense—whether that is a five-day pilot, specialist review, or an ongoing pod.