Generate, clean, and label multimodal training data using Abaka Forge plus vertically specialized annotators—so your team ships models faster without sacrificing quality or governance.
When training data generation is ad hoc, model performance plateaus for reasons you can’t debug—silent label drift, incomplete coverage, and inconsistent reviewer standards. Teams lose 4–8 weeks reworking datasets after a failed evaluation run, and it’s common to spend 30–50% of ML time on data triage instead of iteration. The result is delayed launches, brittle production behavior, and growing compliance overhead as datasets sprawl across vendors, tools, and spreadsheets with unclear provenance and audit trails.
Abaka turns training data generation into a governed production system—collection, filtering, labeling, and evaluation-ready outputs—managed in Abaka Forge. You get multi-layer QA, scholar-network domain reviewers, and modality coverage across text, RLHF, image, video, and 3D/4D data. We operate with SOC 2 and ISO 27001 controls, GDPR/CCPA alignment, strict NDAs, and secure segregated pipelines—so your team can scale volume without trading away traceability, ownership, or accuracy.
As you scale, quality drops first—definitions diverge, edge cases get mislabeled, and reviewer feedback becomes inconsistent. Without calibrated rubrics and multi-pass QA, even a 3–5% error rate can dominate downstream metrics and create weeks of churn. Abaka enforces gold-set calibration, reviewer adjudication, and stratified sampling checks so you can sustain 99% accuracy workflows and keep model failures attributable to real capability gaps—not dataset noise.
In-house teams hit throughput limits quickly: onboarding, training, and supervision become the bottleneck. Even high-performing annotators cap out around 500 files/day, and projects stall when you need new languages, new edge cases, or new modalities at once. Abaka provides elastic staffing across 50+ countries and uses Abaka Forge automation to accelerate repetitive steps—so you can ramp volume without rebuilding your pipeline every time the scope changes.
Training data generation often collapses under governance: unclear IP provenance, scattered access controls, and missing audit trails slow approvals and increase risk. A single compliance escalation can delay a release by 2–6 weeks while your team reconstructs lineage and permissions. Abaka runs secure, segregated pipelines with strict NDAs and documented provenance, helping you keep 0% copyright risk on collected data and produce artifacts your security and legal teams can actually sign off on.
Capture and source text, image, video, and sensor data using on-demand collection pods—curated, timestamped, pre-filtered, and tagged for your use case. We design collection specs around your target distribution (edge cases, geos, environments) and deliver datasets with clear lineage. This is ideal for robotics, automotive perception, retail vision, and safety datasets where coverage matters as much as volume.
Prepare data for training with structured cleaning steps: schema validation, de-duplication, PII removal guidance, and content policy filtering. In Abaka Forge, you can standardize acceptance criteria and QA gates across teams and vendors. Outputs are delivered in consistent formats so your data engineers can spend less time on rework and more time on model iteration.
Generate training labels for images, video, and 3D/4D point clouds with multi-layer QA and specialized reviewers. Use cases include road lanes, object detection, tracking, segmentation, and 3D cuboids for autonomous systems and robotics. We structure guidelines and gold sets, then run reviewer adjudication to prevent drift as volume scales across regions and teams.
Build RLHF datasets with expert task design, instruction following, pairwise preference ranking, and safety-focused rubrics. Abaka supports scholar-network domains like mathematics, coding, medicine, law, and business—useful for frontier model labs and enterprise copilots. Outputs integrate cleanly into your SFT and preference-tuning pipelines with consistent metadata and versioning.
Create hard reasoning corpora: competition-style math, chain-of-thought-style scaffolding (when appropriate for your internal policy), and domain-specific problem sets. We staff specialized annotators—mathematics, coding, science—and apply rubric-based grading so you can measure improvements reliably. Deliverables include structured Q/A, rationales when requested, and evaluation-ready splits.
Generate evaluation datasets aligned to a 6-dimension framework: accuracy, robustness, efficiency, safety/bias, tool/function calling, and user interaction. We can produce objective benchmarks, model-as-judge setups, and human evaluation protocols—then deliver scored outputs and failure taxonomies your team can act on. This supports release gating for agents, copilots, and multimodal assistants.
Run end-to-end data generation in Abaka Forge—collection, cleaning, annotation, and production delivery—across text, RLHF, image, video, and 3D/4D. Large-model automation can accelerate repetitive steps, while your team controls guidelines, review stages, and acceptance criteria. Credits-based usage makes it easy to scale work across projects without rebuilding tooling.
Receive training data in consistent schemas with versioning, dataset cards, and QA summaries—so experiments are reproducible and rollbacks are possible. We deliver common ML formats (JSONL, CSV, COCO-style JSON, mask PNGs, MP4 + annotations, point cloud formats) and can align to your internal data lake conventions. This reduces integration time and keeps your pipeline stable as the dataset evolves.
Launch in days, not months. With pre-built workflows in Abaka Forge and ready-to-ramp specialist teams, you can start producing usable batches in Week 1 and iterate weekly—without waiting for internal hiring cycles.
Reduce hidden costs from rework and stalled training runs. Multi-pass QA and calibrated rubrics cut relabeling loops, while automation reduces repetitive steps—often lowering total project cost versus patchwork tools and contractors.
Avoid governance surprises. Abaka operates with SOC 2 and ISO 27001 controls, GDPR/CCPA alignment, strict NDAs, and segregated pipelines—helping your team maintain auditability and clear provenance as you scale.
Scale up for spikes and scale down when experiments shift. Access a global workforce across 50+ countries and rapidly add new languages, geos, or modalities without rebuilding training and oversight infrastructure.
Match tasks to experts. Use scholar-network specialists across coding, mathematics, medicine, science, law, and business to generate datasets that reflect real user standards—especially for RLHF and reasoning-heavy use cases.
Move from data firefighting to model progress. With stable dataset releases, consistent QA, and evaluation-ready outputs, your team can run faster ablations, diagnose failures, and improve capabilities with each iteration.
Generate perception and mapping training data: road lane labeling ($3/km), object detection/segmentation, and multi-sensor scenarios for ADAS and autonomy. We handle edge-case targeting, QA calibration, and versioned releases so your evaluation signals stay consistent across geographies and weather conditions.
Scale text and RLHF data generation for instruction following, preference ranking, safety audits, and domain expertise (math, coding, medicine, law). Abaka’s workflows support high-precision guidelines, adjudication, and evaluation-ready splits—so you can iterate quickly without dataset drift.
Build multimodal datasets for robotic perception and agent learning: image/video annotation, 3D/4D point clouds, and task-driven instruction data. We can also support RL environment design inputs and structured evaluation sets to measure improvements in navigation, manipulation, and safety behavior.
Create governed training data for clinical language, imaging workflows, and patient-facing assistants—focused on accuracy, safety, and traceability. Abaka supports domain expert review, dataset documentation, and secure handling practices to help your team manage compliance requirements without slowing delivery.
Generate vision and language data for search, recommendations, and in-store analytics: product taxonomy labeling, attribute extraction, shelf imagery annotation, and customer support RLHF. Versioned dataset releases help you track performance changes across seasons and catalog shifts.
Produce high-precision datasets for document understanding, risk and compliance assistants, and customer operations. Abaka supports strict access controls, audit trails, and expert review for tasks that require consistent interpretations—like policy QA, form extraction, and tool-calling evaluations.
Build training data for satellite and aerial imagery: segmentation, change detection labeling, feature extraction, and multi-temporal dataset curation. We deliver consistent schemas and QA reports so analysts and ML teams can align on ground truth and model failure modes.
Generate data for detection, monitoring, and analyst-assist systems across text, image, and video—under strict NDAs and segregated secure pipelines. We focus on provenance, controlled access, and evaluation-ready outputs to support robust testing before deployment.
Create datasets for inspection, yield estimation, and anomaly detection using image, video, and sensor-aligned labels. Abaka helps you scale collection and annotation across sites while keeping labeling rules consistent—so models generalize beyond a single facility or season.
We align on your task definition, edge cases, and what “good” looks like with measurable acceptance tests. You provide sample data and target formats; we propose labeling/eval rubrics, QA stages, and delivery schemas. Security requirements and access controls are finalized up front.
We run a pilot batch in Abaka Forge to validate instructions, inter-annotator agreement, and reviewer workflows. You get early outputs, QA findings, and guideline refinements. This stage de-risks scale-up and ensures the dataset matches the behavior you want to train.
After calibration, we ramp throughput with specialized teams and automation-assisted workflows. Multi-pass QA, adjudication, and sampling audits maintain consistency as volume grows. Deliveries are versioned so your training runs remain reproducible and comparable.
As your model improves, the data needs change. We maintain a backlog of high-value failure modes, add new languages/modalities, and refresh rubrics to match evolving requirements. Your team gets a stable pipeline rather than one-off dataset drops.
Every week, you receive dataset releases with QA summaries, drift checks, and open questions for fast decisions. We review failure clusters with your ML leads and adjust collection and labeling strategies—keeping the pipeline aligned to your evaluation outcomes.
Training data generation isn’t one modality. Abaka supports consistent workflows across text, RLHF, vision, video, 3D/4D, sensor fusion, and audio—delivered in production-ready formats your pipeline can ingest.
| Modality | Annotation Types | Tools | Output Formats |
|---|---|---|---|
| Text | classification; extraction (entities/fields); long-form QA; safety policy tagging; multilingual normalization | Abaka Forge | JSONL; CSV; Parquet; UTF-8 TXT; dataset cards (PDF/MD) |
| LLM RLHF | pairwise preference ranking; rubric scoring; instruction following checks; red-team prompts; tool/function-calling grading | Abaka Forge | JSONL (prompt/response); preference pairs JSON; rubric score tables CSV; eval reports PDF |
| Image | bounding boxes; polygons; semantic segmentation; dense captioning; image editing and cleanup tasks | Abaka Forge | COCO-style JSON; Pascal VOC XML; mask PNG; YOLO TXT; image+metadata CSV |
| Video | frame-level boxes; temporal segments; object tracking; action labeling; spatial reasoning tags | Abaka Forge | MP4 + JSON annotations; frame sequences; COCO-VID style JSON; CSV timecodes; H.264 proxies |
| 3D/4D Point Cloud | 3D cuboids; point-wise segmentation; trajectory tracking; scene-level taxonomy; 4D temporal linking | Abaka Forge | PCD; LAS/LAZ; JSON labels; KITTI-style label text; CSV attributes |
| LiDAR + Camera fusion | cross-sensor object IDs; calibration checks; fused 2D/3D boxes; occlusion tags; sensor-sync validation | Abaka Forge | timestamped JSON; CSV sync tables; image annotations + point cloud labels; per-scene manifests |
| Audio | transcription; speaker diarization; intent tagging; quality scoring; multilingual TTS scripts | Abaka Forge | WAV/MP3 + JSON; TextGrid; SRT/VTT; CSV segments; JSONL utterances |
The team needed a dependable training data generation solution for a customer-facing assistant spanning tool calling, policy compliance, and domain-heavy queries. Their existing pipeline mixed contractors and internal reviewers, producing inconsistent rubrics and frequent relabeling. Each iteration took weeks, and evaluation results were hard to attribute—was the model failing, or the dataset shifting? They also needed stronger governance: provenance, access control, and repeatable dataset releases suitable for security review.
Abaka designed a rubric-first workflow in Abaka Forge: task definitions, gold sets, reviewer adjudication, and versioned releases. We staffed domain specialists from the scholar network for coding and mathematics tasks, and set up RLHF preference ranking and rubric scoring for safety and tool calling. Weekly deliveries included QA summaries, drift checks, and a prioritized backlog of new edge cases derived from the team’s evaluation failures. The pipeline stayed stable while the dataset evolved.
Within 3 weeks, the customer moved from ad hoc batches to a repeatable release cadence and could run cleaner ablations with consistent data versions. Multi-layer QA reduced relabeling cycles, and the team accelerated iteration by focusing on the highest-value failures rather than re-litigating guidelines. The assistant’s evaluation pass rate increased by 18% on internal task suites, and time-to-dataset-release dropped from 4–6 weeks to 7–10 days with the same internal headcount.
We needed a training data partner that could match our internal standards and still scale. The Abaka team helped us tighten rubrics, set up reviewer adjudication, and deliver versioned releases we could trust for repeatable experiments.
The difference was governance. Secure pipelines, clear provenance, and consistent reporting made it easier for security and legal to approve the work. We stopped losing weeks reconstructing what changed between dataset versions.
Our biggest issue was quality drift at scale. Abaka’s multi-pass QA and calibration approach kept the labeling consistent as we added new geographies and new edge cases, and our evaluation results became much more actionable.
We used Abaka for RLHF and evaluation data. The team understood how to structure rubrics and preference tasks so results were stable, not noisy. Weekly releases helped us iterate faster without burning out internal reviewers.
Abaka combines specialized human intelligence with Abaka Forge so you can generate training data across modalities without losing accuracy, auditability, or speed. You get rubric-driven workflows, multi-layer QA, and versioned dataset releases that keep experiments reproducible. Our compliance posture (SOC 2, ISO 27001, GDPR, CCPA) and strict NDAs support secure delivery, while our principle is simple: we never build models that compete with you—your data remains exclusively yours.
Your model is only as strong as the data behind it. We invest in domain expertise (math, coding, medicine, law, business) and rigorous review so the dataset reflects real user standards, not generic labeling.
We never reuse, resell, or share your data. There’s no VC pressure to monetize your datasets—just a long-term focus on being your trustworthy data partner and protecting your IP provenance.
Run collection, cleaning, annotation, and delivery in one platform. Standardize guidelines, review stages, and acceptance tests across projects—then scale volume without changing the underlying process or schema.
Support text, RLHF, image, video, and 3D/4D without stitching together separate vendors and tooling. A single QA philosophy and reporting cadence reduces drift and makes cross-modal evaluation easier to interpret.
Most teams don’t fail because they lack data—they fail because they can’t iterate on it reliably. Abaka delivers versioned releases with QA summaries and drift checks so you can connect dataset changes to model outcomes. When scope shifts, we adapt rubrics, add new edge cases, and keep delivery formats stable—so your training pipeline stays predictable while your model capabilities expand.
Label the Present. Train the Future.
