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Talk to an Expert

Scale supervised datasets your models can trust,
without slowing your roadmap

Abaka delivers supervised learning training data with multi-layer QA, secure pipelines, and domain specialists — so your team ships reliable models faster across text, vision, and multimodal work.

Talk to an Expert

When supervised training data isn’t consistent, everything downstream breaks: model metrics swing, retraining cycles multiply, and teams burn weeks reconciling label noise. A single 5–10% shift in label quality can turn a promising offline score into a production rollback, while internal labeling pipelines quietly cap throughput (e.g., 500 files/day per annotator). The result is missed launches, rising cloud spend from repeated experiments, and delayed customer impact — especially when edge cases, long tails, and multilingual content keep expanding.

Abaka is your supervised learning data service provider for high-precision ground truth at scale. We combine vertically specialized annotators across 50+ countries with rigorous QA, clear rubrics, and secure, segregated workflows. Your team gets stable labeling guidelines, fast iteration loops, and outputs that slot cleanly into your MLOps stack — from JSONL classification to COCO segmentation. And because we never build models that compete with you, your datasets remain exclusively yours — never repurposed, resold, or shared.

The Supervised Learning Data Service Provider Bottleneck

01

Quality Decay

Supervised learning fails quietly when label drift creeps in: different annotators interpret edge cases differently, guidelines evolve mid-stream, and gold sets go stale. Even a 1–2% systematic labeling bias can skew decision boundaries and produce brittle models in production. Abaka prevents quality decay with rubric-first onboarding, layered QA, adjudication on disagreements, and periodic calibration against gold tasks. You get consistent ground truth across batches, versions, and teams — with explicit audit trails for what changed and why.

02

Volume Walls

Internal labeling teams hit a hard ceiling: throughput, recruiting, and review capacity rarely scale with product demand. At best, a single annotator tops out around 500 files/day, and review overhead grows as you add people. Abaka removes volume walls with elastic staffing and productionized workflows in Abaka Forge, so you can run parallel workstreams (new classes, edge-case mining, re-labeling) without stopping experimentation. That means fewer stalled sprints and faster dataset refresh cycles when your data distribution shifts.

03

Compliance Friction

Supervised datasets often contain sensitive content — user text, operational imagery, or regulated signals — and every handoff increases risk. Without strong controls, you face vendor sprawl, unclear IP provenance, and slow legal/security reviews that can add 2–4 weeks before work starts. Abaka is built for enterprise compliance: SOC 2, ISO 27001, GDPR, and CCPA readiness, strict NDAs, and segregated secure pipelines. You get clear access controls, traceability, and full IP provenance with 0% copyright risk on collected data.

01

Dataset scoping that matches your supervised objective

We translate your model goal into a labeling plan: task definitions, class taxonomy, edge-case strategy, and acceptance criteria. Whether you’re building fraud classifiers, medical triage routing, or retail product matching, we define rubrics and sampling so the dataset reflects real production distributions. Your team gets a clear spec for what “correct” means, what to do on ambiguity, and how to track drift. Outputs are designed to plug into training loops (e.g., JSONL, CSV, Parquet) with versioning discipline.

02

Annotator playbooks with calibrated gold sets

Abaka builds annotation guidelines that reduce disagreement and speed onboarding. We create examples, counterexamples, and explicit edge-case rules, then calibrate with gold tasks and adjudication. This is critical for supervised learning where small inconsistencies compound across epochs. Our scholar-network domains (e.g., medicine, law, mathematics, languages) help you separate “hard” from “ambiguous” cases and set realistic policies for abstain/unknown labels. The result is cleaner supervision and fewer wasted retraining cycles.

03

High-accuracy labeling across text, vision, and multimodal

We deliver supervised labels for classification, extraction, ranking, and perception tasks — including bounding boxes, polygons, keypoints, dense captions, and text annotations. Abaka supports complex real-world data: multilingual chat logs, scanned documents, e-commerce catalogs, and autonomous driving imagery. With 1M+ vertically specialized annotators across 50+ countries and a 99% accuracy target, we scale production while keeping outcomes consistent. Work is managed in Abaka Forge for standardized workflows and review.

04

Multi-layer QA with adjudication and auditability

Supervised learning needs reproducible ground truth, not “best effort” labels. Abaka runs multi-layer QA: spot checks, reviewer passes, and structured adjudication on disagreements, with rubric updates tracked over time. You get transparent metrics (agreement rates, error categories) and traceability from output labels back to guideline decisions. This supports fast debugging when model performance dips and lets you safely expand labelers without losing consistency. Deliverables include QA summaries and change logs aligned to your dataset versions.

05

Active learning loops for edge cases and long tails

To improve model performance efficiently, we help you target the samples that matter: uncertainty buckets, rare classes, failure clusters, and adversarial edge cases. Your team can feed candidate sets from model scoring, and Abaka will label, adjudicate, and return prioritized training data quickly. This reduces wasted labeling on redundant examples and accelerates performance gains per dollar. Abaka Forge supports iteration cycles so you can run weekly refreshes and keep your supervised dataset aligned with production behavior.

06

Custom data collection with IP-safe provenance

When public data is insufficient, Abaka can collect new supervised learning inputs via on-demand capture pods: text, image, video, LiDAR, and IoT sensor streams. Data arrives curated, timestamped, tagged, and pre-filtered — often yielding a 70% preprocessing time reduction for your team. Importantly, we maintain full IP provenance and 0% copyright risk on collected data, so you can train and ship with confidence. Collection pairs naturally with labeling for end-to-end dataset delivery.

07

Enterprise security, privacy, and compliant delivery

We’re built to support regulated and sensitive supervised data. Abaka follows SOC 2 and ISO 27001 practices, aligns with GDPR and CCPA, and uses strict NDAs plus segregated secure pipelines. We can constrain access by geography, role, and project, and support secure review flows for sensitive content. If your team needs IP cleanliness, we maintain provenance and ensure your datasets are exclusively yours — never repurposed or resold. This reduces vendor risk and accelerates procurement approvals.

08

Abaka Forge workflows for production-scale supervision

Abaka Forge is our all-in-one platform for collection, cleaning, annotation, and production delivery across text, image, video, 3D/4D point cloud, and RLHF. It enables standardized task templates, reviewer routing, audit logs, and large-model automation that can be up to 50x faster for appropriate steps (pre-labeling, normalization, triage). You get consistent outputs across modalities and teams, with version-controlled exports that fit your training pipeline. Forge also supports credit-based usage ($0.20 USD each).

Why Outsource Supervised Learning Data Service Provider Work

01

Faster Delivery

Outsourcing removes the ramp-up tax of recruiting, training, and building review processes. Abaka can stand up a supervised labeling pipeline quickly, then keep it stable across iterations. With elastic staffing and platform workflows, your team can move from spec to production delivery in weeks, not quarters — while maintaining consistent guidelines, auditability, and QA reporting.

02

Direct Savings

Building in-house labeling at scale often means hiring, tooling, management overhead, and rework when quality slips. Abaka provides predictable unit economics and reduces relabeling costs through rubric calibration and adjudication. You also avoid fragmented vendor stacks — one partner can cover text, vision, and multimodal supervision with consistent QA standards.

03

Risk Reduction

Supervised datasets touch IP, privacy, and model risk. Abaka is designed for enterprise requirements: SOC 2 and ISO 27001 practices, GDPR/CCPA alignment, strict NDAs, and segregated secure pipelines. We provide full IP provenance on collected data and ensure your data is never repurposed, resold, or shared — lowering vendor and legal exposure.

04

Elastic Scalability

Supervised learning workloads are spiky: launches, incident response, and new markets can multiply labeling needs overnight. Abaka scales capacity up or down without forcing your team into permanent headcount decisions. That elasticity is critical when you need to label long-tail cases, expand languages, or re-label after taxonomy changes — without stalling your roadmap.

05

Domain Expertise

Many supervised problems require subject-matter judgment, not just clicking boxes. Abaka draws from scholar-network domains such as medicine, law, mathematics, coding, science, and business to handle nuanced labels and edge cases. You get clearer guidelines, fewer ambiguous outputs, and data that better reflects real-world decision rules — especially in high-stakes verticals.

06

Innovation Velocity

When your team isn’t tied up managing labeling operations, you can focus on model architecture, evaluation, and deployment. Abaka supports active learning loops, dataset versioning, and rapid change requests, so you can iterate on supervision as your product evolves. The result is faster experimentation and a smoother path from prototype to production.

Industries We Serve

Automotive

Train perception and planning systems with supervised labels for lanes, drivable areas, traffic participants, and edge-case scenes. Abaka supports road lane labeling priced per km when needed, plus image/video annotation with rigorous QA. We help you build consistent taxonomies across geographies and conditions (night, rain, construction) and deliver formats that integrate cleanly into your training pipeline for ADAS and autonomy programs.

GenAI / Foundation Models

Build supervised datasets that improve instruction following, safety, and domain competence. Abaka supports text labeling, preference data, and rubric-driven evaluations to create reliable supervision signals for frontier LLM and multimodal teams. With specialized annotators across 50+ countries, we help you expand multilingual coverage and maintain consistent standards across large, evolving corpora — without compromising data ownership or confidentiality.

Embodied AI / Robotics

Robots need supervised data that reflects real environments: object states, affordances, spatial relationships, and task outcomes. Abaka labels images, video, and 3D/4D point clouds, and can support RL environment design when supervision interacts with agent learning. We help you build datasets for grasping, navigation, pick-and-place, and warehouse automation with clear rubrics for ambiguous scenes and robust QA.

Healthcare

Support clinical and operational ML with careful supervision: medical text triage, coding support, imaging labels, and safety-focused review. Abaka applies strict NDAs, segregated secure pipelines, and compliance-aligned workflows (SOC 2, ISO 27001, GDPR, CCPA) to reduce risk while producing high-quality ground truth. Scholar-network expertise in medicine helps define guidelines and adjudicate edge cases responsibly.

Retail

Improve search, recommendations, and catalog quality with supervised labels for product attributes, category mapping, duplicate detection, and visual similarity. Abaka can label product images and text at scale, build consistent taxonomies, and support active learning loops that focus on the long tail (new brands, seasonal items). Outputs integrate into your feature stores and training pipelines for measurable lift in ranking and relevance.

Finance

Train supervised models for fraud detection, KYC document understanding, customer support routing, and risk classification. Abaka provides text and document labeling with strong auditability, plus quality processes that reduce label noise in rare-event datasets. Our security posture (SOC 2, ISO 27001) and privacy alignment (GDPR/CCPA) supports sensitive workflows while keeping your data exclusively yours.

Geospatial

Create supervised ground truth for mapping and Earth observation: land-use classes, building footprints, road features, and change detection. Abaka can label imagery and video, and support LiDAR + camera fusion workflows for high-fidelity scene understanding. With scalable production and consistent QA, you can refresh datasets frequently and maintain stable labeling standards across regions and sensors.

Security / Defense

Enable supervised models for threat detection, ISR analysis support, and anomaly classification with secure delivery and strict access controls. Abaka uses segregated pipelines and NDA-backed workflows, and can scale text, image, and video labeling while maintaining auditability. We help your team handle long-tail edge cases and ambiguous scenes with adjudication processes designed for high-stakes decisions.

Agriculture / Industrial

Train supervised vision systems for crop monitoring, equipment inspection, defect detection, and process safety. Abaka labels images and video for segmentation, counting, and condition classification, and can support sensor-fusion workflows when multiple signals are involved. With elastic scale, you can label peak-season data fast, then maintain steady refreshes as fields, factories, and operating conditions change.

How It Works

1) Day 0–3 — Scope, taxonomy, and success criteria

We align on your supervised objective, label definitions, and acceptance thresholds, then design the sampling plan and QA approach. You’ll share representative data and edge cases; we respond with a rubric, escalation rules, and output specs (e.g., JSONL, COCO). We also confirm security requirements, access controls, and delivery cadence so implementation starts cleanly and avoids rework.

2) Week 1–2 — Pilot labeling + calibration

Abaka runs a pilot batch to validate guidelines, measure agreement, and identify ambiguous classes. Reviewers adjudicate disagreements and refine rubrics, then we lock the production process. You’ll receive pilot outputs plus a QA summary so your team can train quickly, inspect failure modes, and request targeted adjustments before scaling to full volume.

3) Week 2–3 — Scale production with multi-layer QA

We ramp capacity while keeping quality stable through layered review and audit logs in Abaka Forge. Workstreams can run in parallel (new classes, relabeling, edge-case mining) so your roadmap doesn’t stall. Outputs are delivered in your required formats with consistent schemas, version tags, and clear change notes for what shifted between dataset drops.

4) Ongoing — Active learning and dataset refresh cycles

As your model evolves, we keep supervision aligned with reality: label new edge cases, expand classes, add languages, and refresh gold sets. Your team can send uncertainty buckets or failure clusters; we label and adjudicate quickly to improve performance on the long tail. This makes supervised data a continuous capability rather than a one-off project.

5) Weekly — Quality reviews and change management

Each week we review QA metrics, disagreement categories, and rubric updates with your stakeholders. Change requests are documented, approved, and rolled into the next version so your experiments stay reproducible. You get a predictable cadence for updates, clear auditability, and a stable interface between data operations and model development.

Modality & Format Coverage

Supervised learning spans more than labels — it requires consistent rubrics, scalable QA, and export formats that match your training stack. Abaka covers multimodal annotation and delivery through Abaka Forge.

ModalityAnnotation TypesToolsOutput Formats
TextClassification (single/multi-label), NER, sentiment, intent, document taggingAbaka ForgeJSONL, CSV, TSV, Parquet
LLM RLHFPreference ranking, instruction following checks, safety policy labeling, rubric-based scoringAbaka ForgeJSONL, conversation transcripts, pairwise preference tables, eval reports
ImageBounding boxes, polygons/segmentation, keypoints, dense captioning, attribute taggingAbaka ForgeCOCO JSON, YOLO TXT, Pascal VOC XML, PNG masks
VideoObject tracking, temporal events, action labels, frame segmentation, scene understanding notesAbaka ForgeCOCO-video JSON, frame-level masks, MP4 timecode logs, JSONL
3D/4D Point Cloud3D boxes, point-level segmentation, instance IDs over time, object attributesAbaka ForgeJSON, PCD/PLY with labels, KITTI-style JSON, protobuf exports
LiDAR + Camera fusionSensor-aligned 3D boxes, track IDs, calibration-aware labeling, multimodal consistency checksAbaka ForgeSynchronized JSON bundles, per-sensor annotations, calibration files, export manifests
AudioTranscription, speaker diarization, intent tags, keyword spotting labels, QA scoringAbaka ForgeTextGrid, JSON, CSV, SRT/VTT

Success Story

A leading enterprise ML platform team
Challenge

The team operated multiple supervised models across customer support, risk scoring, and document workflows, but label quality varied across internal teams and vendors. Taxonomies drifted, edge cases were handled inconsistently, and retraining cycles were delayed by relabeling and unclear change history. They needed a single supervised learning data service provider that could unify rubrics, scale volume across modalities, and satisfy strict security requirements — without forcing the ML engineers to become labeling operations managers.

Approach

Abaka re-scoped tasks into a consistent taxonomy, produced rubric-first guidelines, and launched a pilot to calibrate annotators and reviewers on ambiguous cases. We implemented multi-layer QA with adjudication and maintained audit logs for every guideline revision and output drop. Work was managed in Abaka Forge so the customer had one workflow for text, image, and document labeling, plus a clean export interface into their training pipelines. Weekly reviews kept changes controlled and reproducible.

Results

The customer stabilized supervision across teams and reduced relabeling churn by locking guidelines early and tracking changes explicitly. Dataset refreshes moved to a predictable cadence, enabling faster iteration on active learning candidates and long-tail failure clusters. With elastic scaling, they increased labeled volume without sacrificing consistency, and shipped updated supervised models on schedule. Outcomes included 99% accuracy targets on QA gates, a 2–3 week path from scope to scaled delivery, and faster iteration on edge cases with fewer rollbacks.

99%
Accuracy target with multi-layer QA
2–3 weeks
From scope to scaled supervised delivery
50+
Countries supported for multilingual coverage

By the Numbers

2019
Founded — trustworthy data partner for frontier AI
1,000+
Enterprise and research customers
1M+
Vertically specialized annotators
50+
Countries for multilingual data coverage

What Customers Say

We needed supervised labels that stayed consistent across batches and across teams. Abaka helped us tighten the rubric, run a calibration pilot, and then scale production without the quality swings we saw before. The audit trail and weekly QA reviews made it easy for our ML engineers to trust each dataset version and move faster on training iterations.

Director of Applied MLEnterprise Software Company

Our internal labeling pipeline became the bottleneck the moment we expanded into new languages and edge cases. Abaka brought the right domain reviewers, handled adjudication cleanly, and delivered exports that dropped into our training stack. We stopped spending cycles on relabeling and started spending cycles on model improvements.

Head of Data ScienceGlobal Retail Platform

Security and data ownership were non-negotiable for us. Abaka’s segregated workflows, NDA-first posture, and compliance alignment reduced procurement friction and helped us start quickly. The team was responsive to change requests while keeping guidelines stable — which is exactly what supervised learning needs to remain reproducible.

ML Engineering ManagerFinancial Services Organization

The value wasn’t just more labels — it was better supervision. Abaka’s approach to defining edge cases, running gold tasks, and escalating ambiguity improved our training data quality noticeably. The process felt engineered for production, not a one-time dataset. We now run active learning refreshes with a cadence we can rely on.

Principal Research ScientistApplied AI Lab

Why Choose Abaka

01

Supervised data you can defend — quality, provenance, and control

Abaka combines rubric-driven annotation, multi-layer QA, and enterprise-grade security so your supervised datasets stay consistent and auditable as you scale. You get access to 1M+ specialized annotators across 50+ countries, supported by Abaka Forge workflows for review, adjudication, and versioned exports. We maintain strict NDAs, segregated secure pipelines, and full IP provenance with 0% copyright risk on collected data. And we never build models that compete with you — your data remains exclusively yours.

02

99% accuracy target with QA gates

We design QA gates to match your risk profile, then enforce them with reviewer passes, adjudication, and calibration. This keeps supervision stable across batches and prevents hidden label drift from corrupting your training signal over time.

03

Elastic capacity without hiring risk

Scale labeling up for launches and down after peaks without carrying permanent headcount. Abaka’s production operations remove throughput ceilings and let your ML team keep momentum when the long tail expands.

04

Enterprise compliance built in

Abaka supports SOC 2 and ISO 27001 practices and aligns with GDPR and CCPA. With strict NDAs and segregated pipelines, you can label sensitive supervised data while maintaining access controls, auditability, and clear ownership boundaries.

05

One platform across modalities

Abaka Forge unifies text, image, video, 3D/4D point cloud, and RLHF workflows so your datasets share consistent standards and delivery conventions. Large-model automation can accelerate appropriate steps up to 50x, while keeping humans in control where it matters.

06

A trustworthy partner for frontier AI outcomes

Founded in 2019 and self-funded & profitable, Abaka supports 1,000+ enterprise and research customers from offices in Singapore, Paris, and Silicon Valley. We’re built to be the long-term data layer your team can rely on — without VC pressure, without competing products, and without your data being repurposed.

Frequently Asked Questions

How much does a supervised learning data service provider cost?
Pricing depends on modality, difficulty, and QA depth, but we can share concrete starting points. For supervised LLM math/coding labeling, pricing can start at $18/hr; STEM generalist work can start at $12/hr; dense captioning can start at $6/hr; and road lane annotation can be priced at $3/km. We’ll recommend the most cost-effective setup by mapping your task to the right annotator profile, rubric complexity, and review layers. Talk to an Expert and we’ll scope a pilot with a clear, itemized estimate.
How fast can you deliver supervised training data?
Most teams can move from scope to scaled production in about 2–3 weeks, depending on task complexity and security onboarding. Day 0–3 is typically used for taxonomy, rubrics, and acceptance criteria, followed by a pilot for calibration and QA tuning. After the pilot, we scale production with multi-layer QA and deliver on an agreed cadence (often weekly). If you already have stable guidelines and just need volume, we can accelerate by reusing your existing specs and focusing on throughput and review.
What data types and export formats do you support for supervised learning?
We support text, image, video, audio, and 3D/4D point cloud — plus multimodal workflows like LiDAR + camera fusion. Common exports include JSONL/CSV/Parquet for text labels, COCO JSON or YOLO TXT for vision, frame-level masks for video, and labeled PCD/PLY or structured JSON bundles for 3D. If your training stack expects a custom schema, we can align outputs to your spec and include manifests, version tags, and QA summaries so each dataset drop is reproducible.
What label accuracy can you achieve for supervised datasets?
We target high-accuracy outcomes (often up to 99% on defined QA gates) by combining clear rubrics, annotator calibration, and multi-layer review with adjudication. Accuracy depends on how ambiguity is handled and how “correct” is defined, so we start by tightening the taxonomy and decision rules. For inherently ambiguous classes, we may recommend an “abstain/unknown” policy or confidence labeling to avoid forcing noisy supervision. You’ll receive QA reporting so you can see agreement rates and error categories over time.
How do you secure sensitive training data and meet enterprise requirements?
Abaka is designed for enterprise security: SOC 2 and ISO 27001 practices, GDPR and CCPA alignment, strict NDAs, and segregated secure pipelines. We can enforce role-based access controls, project-level separation, and controlled reviewer workflows for sensitive content. We also maintain full IP provenance and ensure your data is exclusively yours — never repurposed, resold, or shared. This approach reduces vendor risk while giving your team the auditability needed for internal governance and external scrutiny.
Can you label multilingual data for supervised learning models?
Yes. Abaka supports multilingual supervision using specialized annotators across 50+ countries. We can localize guidelines, define language-specific edge cases (tone, formality, slang), and maintain consistent label policies across markets. For multilingual classification and extraction tasks, we recommend per-language calibration and gold sets to reduce hidden drift. If your goal is cross-lingual generalization, we can also help design sampling so training data reflects the real distribution of languages, domains, and customer segments you expect in production.
How are you different from other data labeling vendors?
Two differences matter most for supervised learning: trust and operational rigor. Abaka is a trustworthy data partner for frontier AI with enterprise compliance (SOC 2, ISO 27001, GDPR, CCPA) and strong provenance controls — including 0% copyright risk on collected data. Operationally, we emphasize rubric-first design, calibration pilots, adjudication, and versioned change management so supervision stays reproducible. Finally, we never build models that compete with you, and your data is never repurposed or resold — it remains exclusively yours.
What if we need to change the label taxonomy mid-project?
Taxonomy changes are common — the key is controlling them so experiments remain comparable. We handle change requests through a documented process: propose updates, define how legacy labels map to new classes, and decide whether to relabel prior data or maintain multiple dataset versions. Abaka provides change logs and versioned exports so your ML team can retrain with confidence and measure impact cleanly. For large changes, we often run a small recalibration batch first to validate the new rubric before scaling.
Can we start with a pilot project before scaling?
Yes — and we recommend it for most supervised learning programs. A pilot lets us validate guidelines, measure inter-annotator agreement, surface ambiguous edge cases, and tune QA gates before high-volume labeling. You’ll receive pilot outputs in your target formats plus a QA summary and recommendations (e.g., taxonomy refinements, abstain rules, sampling adjustments). Once the pilot meets acceptance criteria, we scale production with a predictable delivery cadence so your training pipeline can run continuously.
Who owns the labeled data and can it be reused elsewhere?
You own your data and the resulting labeled outputs. Abaka’s trust differentiator is that we never build models that compete with you, and your data is exclusively yours — never repurposed, resold, or shared. We also support strict NDAs and segregated secure pipelines so your datasets don’t mix with other customers’ work. If you require additional contractual language around IP, retention, and deletion, we can align the engagement to your internal governance policies.
What tools do you use to manage supervised labeling workflows?
We use Abaka Forge — our all-in-one platform for collection, cleaning, annotation, and production delivery across text, image, video, 3D/4D point cloud, and RLHF. Forge supports task templates, reviewer routing, adjudication, audit logs, and large-model automation for appropriate steps. Exports can be standardized or customized to your schema, and we maintain versioning discipline so dataset drops are traceable. Forge can also run on a credit model ($0.20 USD each) where that fits your program.
What is the minimum dataset size or project size you can support?
We support both small pilots and large-scale production programs. A typical minimum starting point is a focused pilot batch large enough to calibrate guidelines and measure agreement — often a few hundred to a few thousand items, depending on task complexity and class count. From there, we scale to ongoing weekly deliveries. If you’re unsure what minimum makes sense, we’ll recommend a pilot size based on your taxonomy, expected edge-case rate, and the amount of data needed to produce a reliable first training run.

Ready to Get Started?

Label the Present. Train the Future. Talk to an Expert to scope your supervised learning data pipeline, lock guidelines, and scale high-accuracy ground truth with secure, auditable delivery.

Talk to an Expert