Software engineering evaluations
Prompt writing, bug reproduction, code review, refactoring tasks, test design, debugging scenarios, and model response scoring.
AI model training and evaluation
Managed technical teams for expert AI training work.
Datrick provides technical contributors for AI programs that need coding evaluations, data workflow tasks, rubric creation, golden answers, model output review, and quality-controlled delivery.
Delivery loop
Expert-reviewed
1
Task designCoding, SQL, analytics, debugging, migration, and workflow prompts.
Design
2
Rubric and answerExpected behavior, acceptance criteria, edge cases, and reference outputs.
Ground
3
Model reviewCorrectness, reasoning, safety, code quality, and usefulness scoring.
Evaluate
4
Quality passConsistency review, reviewer feedback, documentation, and delivery cadence.
Control
What we provide
AI labs and model-training vendors need technical judgment, not generic labeling.
SQL, BI, and analytics tasks
Database questions, query correction, metric interpretation, dashboard reasoning, data pipeline review, and reporting workflows.
Rubrics and golden answers
Clear acceptance criteria, high-quality reference answers, edge-case notes, grading instructions, and reviewer calibration.
Managed contributor teams
Named lead, contributor onboarding, quality checks, throughput reporting, confidentiality practices, and escalation for ambiguous tasks.
Why Datrick fits
Our operating work makes AI evaluation more practical.
Real technical domains
DBA, migration, BI, analytics, software delivery, and Claude workflow work gives reviewers context beyond abstract coding puzzles.
Team delivery discipline
Work is handled through a managed process with instructions, QA, status visibility, and clear ownership instead of one-off freelance output.
Confidentiality-first posture
We avoid using client-owned private materials unless ownership, permission, and permitted use are clear before any training work begins.
Fast expansion path
Start with a small evaluation queue, then expand into full-time reviewer capacity, task design, or adjacent Claude implementation work.
Engagement types
Three practical ways to start.
Evaluation Sprint
Small reviewer team for one task family, with rubrics, sample outputs, quality review, and delivery notes.
Best for Testing fit before assigning recurring queues.Managed Reviewer Pod
Dedicated technical contributors for coding, data, SQL, analytics, or workflow evaluation tasks.
Best for Ongoing full-time or near-full-time AI training work.Task and Rubric Buildout
Creation of technical prompts, expected answers, grading rubrics, edge cases, and reviewer instructions.
Best for Building new benchmark or training task categories.Start small
Send one task family and the quality bar you need.
We will reply with the contributor profile, review process, expected throughput, and what we need to scope the first delivery cycle.