Recruit & calibrate.
Define expertise, language, schedule and task readiness. Test against representative work before production.
RLHF & human feedback services
RLHF and human feedback services for AI teams.
Managed RLHF and human feedback teams for preference ranking, response comparison, critique, scoring and domain-expert review.
AI services and deliverables for your model or data program.
What should be defined first?Define the unit of work, expertise required, acceptance criteria, escalation path, expected volume and quality target before deciding how many people you need.
Baaraku’s role can range from recruiting specific domain experts to building a dedicated managed workforce with team leads, calibration, QA and reporting. Your methodology and data remain the governing source of truth.
How Baaraku recruits, trains and operates the human workforce.
Run the queue.
Set throughput expectations, assignment logic, escalation routes and a clear source of truth for ambiguous cases.
Measure quality.
Use sampling, secondary review, disagreement analysis and calibration loops suited to the task.
How quality is reviewed, measured and escalated.
For complex AI programs, the right quality layer may include gold-standard tasks, inter-rater agreement, reviewer tiers, blind secondary review, domain escalation and ongoing calibration. Baaraku scopes those controls around the client’s methodology rather than making one quality formula fit every project.
How to scale from a pilot to a larger AI workforce.
Engagements can begin with a small expert group and expand as task definition stabilizes. For larger programs, the operating model can include team leads, workforce scheduling, QA layers and reporting. Baaraku’s Nigeria delivery operation provides physical capacity that can complement distributed remote teams.
How access, data handling and security requirements are scoped.
Data classification, device requirements, account provisioning, least-privilege access, confidentiality, retention rules and client-specific controls should be defined before production. Any required certifications, controls or client-specific security conditions are confirmed and documented before production begins.
Human feedback & preference-data operations
Build human-feedback programs with clear rubrics, comparison logic and quality controls.
Preference data becomes useful when reviewers understand the task, apply a consistent rubric and escalate ambiguity instead of guessing.
Systems we can match for
custom RLHF / evaluation platformsLabel Studio / Labelboxreviewer dashboardsQA sampling toolssecure client portalsspreadsheet-based audit logs
Tell us the exact systems you use. We screen for relevant experience and learning ability rather than assuming every candidate knows every product.
Terms and workflows the role should understand
- pairwise preference
- ranking
- rubric scoring
- helpfulness / harmlessness / relevance
- RLHF
- expert review
- consensus
- adjudication
- reviewer calibration
- quality drift
How AI can support the work
- structured review of model outputs
- preference and rubric-based scoring
- domain-expert escalation
- feedback-loop QA and reviewer recalibration
We separate throughput from quality: reviewer calibration, auditability and escalation rules matter as much as task volume.
Hire around the real workflow
Send us the tools, workload and outcome you need someone to own.
Use the consultation brief for a structured requirement, or book a 15-minute discovery call now. We’ll match the role to your stack, operating context and accountability level.
Scope the AI workforce around the task.
Tell us what the model needs humans to create, judge, verify or challenge. We’ll work backward from the unit of work and quality standard.