Better AI needs the right human judgment.
AI processes enormous volumes of data, makes predictions at speed and automates increasingly complex tasks. But real world data is rarely perfect.
Adept operates the human layer inside your AI workflow: curation, annotation, validation, evaluation and exception handling, delivered from Nairobi.
production AI data
Real world data is rarely perfect.
Images are unclear and objects partially hidden.
Labels are inconsistent and language is ambiguous.
Model outputs are uncertain, and new situations appear that training data never covered.
As AI becomes embedded in real world use, maintaining data quality, validating outputs and managing exceptions becomes increasingly important. Adept provides the human layer within AI workflows, preparing and curating data, creating structured training datasets, validating outputs and resolving the cases that require judgment.
The objective is to put human judgment where it creates the greatest impact.
Where human judgment supports your AI lifecycle
Data curation
Cleaning, classification, metadata enrichment and ground-truth preparation before data enters the workflow.
Annotation & labelling
Image, video, text, LiDAR and sensor annotation to defined taxonomies and quality standards.
Validation & QA
Output validation, ground-truth verification, edge-case review and exception escalation.
GenAI human evaluation
Response scoring, ranking, factuality and safety review, and structured human feedback.
Trained teams working to your taxonomy, not a generic one.
Every programme starts with your labelling rules, edge-case definitions and acceptance criteria. Reviewers are trained and calibrated against them, and output is sampled continuously.
Experience built through real AI data operations
Adept's HITL capability is built on long term operational experience. Since 2019, Adept has supported KITRO, an AI enabled food waste technology company, through annotation, data curation and validation workflows. As the client's technology and requirements have evolved, so has the work.
The engagement has required teams to work with evolving datasets, apply defined quality standards and support validation activities as requirements changed.
This experience provides the operational foundation for Adept's broader Human-in-the-Loop capability. We are building on that foundation through increased team capability in areas such as LiDAR and 3D annotation, Generative AI evaluation and broader model review workflows.
Explore our AI data experience ↗
Annotation · Curation · Validation
Over thirty million images annotated to date, with curation, review and validation running continuously since 2019.
Why Kenya for AI data operations?
Kenya has developed a strong base of digital talent with experience supporting international AI and data operations.
For AI companies this means access to an English speaking workforce that can be trained for structured, quality-controlled workflows across data types and use cases.
Adept combines that talent environment with managed delivery from Nairobi and experience operating long running international AI data programmes, as a social enterprise providing dignified work to youth and women.
The result is not simply access to people. It is the ability to build a managed human layer around an AI workflow.
There is no single HITL model.
The right operating structure depends on what the AI is doing, where uncertainty occurs and what happens when the model gets something wrong. A practical HITL operation starts by defining eight things.
What requires human review
What can remain automated
When human review is triggered
What counts as an acceptable result
How reviewers are trained and calibrated
How quality is measured
How exceptions are escalated
How reviewer decisions are captured
Adept provides the operating structure around that human layer through trained teams, defined workflows, quality controls, supervision and performance management. This allows AI and product teams to focus on the technology while maintaining visibility and control over the human dependent parts of the workflow.
Your AI may not need more manual work. It may need better-directed human judgment.
Whether you are preparing training data, scaling annotation, validating model outputs, handling recurring exceptions or deciding how human review should fit into an AI workflow, we can help design and operate the human layer around it.
Thank you, your brief is with our team.
We will come back within one working day with questions about your data, volumes and quality requirements.
Human-in-the-Loop AI
HITL is an approach in which people participate at selected points within an AI or machine-learning workflow. Their role can include preparing and annotating data, reviewing outputs, handling exceptions, validating results or providing structured feedback for future improvement.
No. Data annotation is one important part of HITL, particularly during model development. HITL extends across the AI lifecycle through data curation, quality control, model evaluation, exception handling and production output review.
No. Human review is most useful where a model has low confidence, encounters an unusual case, produces an output requiring contextual judgment, or where the consequence of an incorrect result warrants additional oversight.
Yes. Human reviewers can evaluate generated responses against defined criteria such as relevance, accuracy, factuality, instruction following and safety. They can also compare responses and create structured human-feedback datasets.
Adept's established experience is strongest in image-based annotation, curation and validation. HITL workflows also extend across video, text, audio, 3D and sensor data, and we are developing further capability in LiDAR, 3D annotation and Generative AI evaluation.
Yes. Production AI can use human review for low-confidence predictions, unusual cases, exception handling, quality monitoring and the creation of new ground truth and feedback for future improvement.