From Raw Data to Delivered Labels. Here Is Exactly How.
A transparent look at our end-to-end pipeline. From the moment your data arrives to the moment QA-verified labels land back in your hands — every stage, every owner, no black box.
Every Project. Every Time. Same 8 Stages.
Whether it is a 200-item LLM eval pilot or a 50,000-frame 3D LiDAR production run, every project moves through the same pipeline. No shortcuts, no exceptions.
Receive
Your data arrives. We verify format, sign NDAs, and confirm dataset scope and requirements before anything is touched.
Keylian · CEOScope
Custom rubric or annotation guidelines built for your data. Object classes, evaluation criteria, edge case rules, and format specs locked before production starts.
Keylian · CEOTrain & Calibrate
Raters are trained on your specific rubric and run calibration tasks before touching production data. Minimum 95% calibration score required for access.
Ibrahim · COO & QA LeadAssign
Raters selected and deployed based on project type and requirements. Team size scaled to your timeline — from 6 to 50+ raters per project.
Ibrahim · COO & QA LeadProduction
Annotation or evaluation runs in parallel across your dataset. Real-time spot-checking active. Progress tracked and reported.
Annotation Teams4-Layer QA
Self-review, peer check, lead audit, client-spec validation. Every item reviewed before delivery. Disagreements flagged with rationale, not just a score.
Ibrahim · COO & QA LeadDeliver
QA-verified output exported in your format with a full accuracy report. KITTI, nuScenes, COCO, JSONL, CSV, or custom schema on request.
Keylian · CEOIterate
Your feedback refines the rubric and guidelines. Quality compounds over time. Edge case definitions deepen with every batch delivered.
Full TeamPilot projects follow the same pipeline, scoped smaller. 200 to 500 items, 48hr delivery, full accuracy report. Zero cost, zero commitment. You see the quality before any contract is signed.
The Pipeline Adapts to Your Work Type
The same 8-stage pipeline runs across both service lanes. What changes is the tooling, the training, and the QA checks specific to each type of work.
LLM Evaluation & Human Judgment
For RLHF preference labeling, agent benchmarking, audio CSAT scoring, LLM output labeling, multilingual evaluation, and red-team adversarial testing.
- 1You share your evaluation rubric and sample prompts or tasks
- 2We build rater training materials and run calibration sessions on your criteria
- 3Free pilot: 200-500 items on your actual data, 48hr delivery
- 4Inter-annotator agreement measured and reported before scale
- 5Production delivery in JSON, JSONL, CSV, or your custom schema
- 6Pricing locked for 180-day project window — no mid-pipeline surprises
3D LiDAR & Computer Vision
For 3D cuboid annotation, sensor fusion, point cloud segmentation, AV edge cases, robotics 6DoF pose, and 2D image and video annotation.
- 1You share sample frames, format requirements, and object class taxonomy
- 23D annotators complete 120+ hours of dedicated training before production access
- 3Free pilot: up to 500 frames on your actual data, 48hr delivery
- 4IoU validation and position/orientation accuracy checks on every 3D batch
- 5Production delivery in KITTI, nuScenes, Waymo Open, COCO, YOLO, or custom
- 6Iterative guideline refinement — edge case definitions compound over time
The 4 Layers Before Anything Reaches You
QA is not a single review at the end. It is four distinct stages built into the pipeline at every batch. Every disagreement is traceable. Every error has a named owner.
Self-Review
The annotator reviews their own work against the project rubric before submission. First filter for obvious errors and guideline drift.
Peer Check
A second rater reviews 20-30% of annotations. Checks for consistency, class accuracy, and edge case handling. Discrepancies flagged for resolution.
Lead Audit
Ibrahim Ouma runs final validation on every batch — guideline adherence, inter-annotator consistency, format compliance. For 3D: IoU validation, position accuracy within 10-30cm, orientation within 5-10 degrees.
Client-Spec Validation
Final check against your specific delivery requirements — format schema, naming conventions, metadata fields. Nothing leaves until it matches your spec exactly.
The Complete TechAI Remote Pipeline
Watch how your data moves through our system — from client intake, through annotation and 4-stage QA, to delivery. 2 minutes, 8 stages, no fluff.
No Surprises. No Black Boxes.
Every project runs the same way. Here is what you can count on from first contact to final delivery.
NDA Before Anything
Non-disclosure agreement executed before any data is transferred. No exceptions, no “we’ll sort it later.” Your IP is protected from day one.
Rubric Built for You
We do not hand raters your brief and wish them luck. Every project gets custom training materials built for your specific data, criteria, and edge case definitions.
Free Pilot First
200 to 500 items on your actual data, no cost, no commitment. You see the quality before any contract is signed or any volume is committed.
Accuracy Report on Delivery
Every delivery comes with a QA accuracy report. Not just a score — inter-annotator agreement, flagged disagreements with rationale, and batch-level metrics.
Pricing Locked at Start
Per-task rates confirmed before production begins and locked for the 180-day project window. No mid-pipeline renegotiations, no surprises on the invoice.
Quality Compounds Over Time
Your feedback on each batch refines the rubric. Edge case definitions deepen. The longer we work together, the fewer errors reach QA review in the first place.
Same Pipeline. Zero Commitment.
Send us your data or book a call. Free pilot, 48 hours, full accuracy report. You see the quality before spending anything.