Data Annotation Services
that ship reliable training data—fast

Abaka delivers high-accuracy labeling across text, image, video, audio, and 3D/4D—powered by Abaka Forge workflows, multi-layer QA, and domain-specialized annotators.

Talk to an Expert

When your team is blocked on training data, every iteration slows: evaluation cycles stretch, model regressions slip into production, and roadmap commitments turn into rework. In practice, a single ambiguous guideline can cascade into weeks of inconsistent labels—then you pay twice: once to label, and again to clean, re-label, and re-train.

Teams that try to “do it all in-house” often discover the hidden tax: recruiting and training annotators, building QA, managing tooling, and maintaining compliance. Even a modest 10,000-item batch can balloon into 2–3 weeks of coordination if you lack a proven pipeline. Abaka helps you convert that time into measurable progress—by turning labeling into a predictable, auditable production system.

The Data Annotation Services Bottleneck in AI Development

Quality Decay

Annotation quality isn’t a one-time decision—it drifts. As labelers fatigue, edge cases accumulate, and instructions evolve, inter-annotator consistency drops and you start training on noise. That noise shows up as brittle behaviors: a vision model that misses small objects, an LLM that follows instructions inconsistently, or an agent that fails under slightly different conditions. Abaka counters quality decay with multi-layer QA, calibrated review sets, and domain-specialized annotators—targeting 99% accuracy while keeping throughput stable.

Volume Walls

Most teams hit a volume wall: they can label a pilot, but scaling to production means thousands to millions of items across modalities. If each annotator can only process a bounded daily load—Abaka caps throughput at 500 files/day per annotator to protect quality—your operational plan must include routing, queue management, sampling-based QA, and rework loops. Without that, your backlog grows faster than your model improves. Abaka provides elastic capacity via a 1M+ annotator network across 50+ countries, plus platform automation to keep delivery predictable.

Compliance Friction

Training data pipelines touch sensitive assets: proprietary product images, customer conversations, internal code, or location traces. Every handoff adds friction—security reviews, vendor risk checks, NDAs, and data segregation requirements. Abaka is built to reduce that drag with SOC 2 and ISO 27001 controls, GDPR and CCPA readiness, strict NDAs, segregated secure pipelines, and full IP provenance—so you can move fast without introducing avoidable exposure.

Computer Vision labeling for detection, segmentation, and dense understanding

Ship training-ready labels for detection and segmentation workflows across retail, automotive, security, and industrial inspection. We handle bounding boxes, polygons, instance/semantic segmentation, keypoints, and dense captioning—then standardize outputs to your preferred schema. Use Abaka Forge for task routing, instruction versioning, gold sets, and review queues, with exports in JSON/NDJSON and common CV-friendly formats like COCO-style JSON (when requested), plus image masks (PNG) for segmentation. If you already operate in Label Studio, CVAT, or Supervisely, we can mirror your conventions while keeping Abaka’s QA gates and reporting.

LLM RLHF data labeling—preference, ranking, and instruction following

Build RLHF and instruction-tuning datasets that are consistent, auditable, and aligned to your product voice. We support pairwise preference, rubric-based scoring, ranking multiple candidates, and instruction-following checks (format, safety, refusal, tool-use correctness). Abaka’s scholar-network domains include coding, mathematics, medicine, law, and languages—useful when you need expert judgments rather than generic crowd labels. Outputs can be delivered as JSONL with prompt, candidates, chosen/rejected, rubrics, and rater metadata for calibration analysis.

Reasoning, math, and competition-grade QA labeling (including Lean4)

When you’re training models for hard reasoning, the data must be both correct and traceable. Abaka supports mathematics labeling and verification, structured reasoning tasks, and proof-oriented workflows—including Lean4 specialty work when required. Teams use these datasets for evaluation, reinforcement learning, and targeted capability lifts (e.g., algebraic manipulation, geometry, program synthesis). Delivery formats include JSONL with strict schemas (problem, constraints, solution, verifier notes, and difficulty tags), plus separate split manifests for train/val/test governance.

Code annotation for training, testing, and defensive coding evaluation

For code-centric models, you need labels that reflect real engineering expectations: correctness, style, edge cases, security posture, and test coverage. Abaka provides code annotation and evaluation workflows such as defensive coding checks, unit-test creation, bug localization labels, and rubric-driven quality scoring. Scholar-grade reviewers in coding can annotate across languages your team uses (e.g., Python, JavaScript/TypeScript, Java, Go) and deliver outputs in JSONL plus patch formats (diffs) when appropriate. This is especially valuable when your internal engineers can’t spare time for labeling without stalling product delivery.

Video spatial reasoning and temporal labeling at production scale

Video labeling adds time—literally. We support temporal segmentation, object tracking, keyframes, action labeling, and spatial reasoning tasks for embodied AI, robotics, and autonomy-adjacent perception. Abaka can design label taxonomies that remain stable as you add new scenarios, then apply consistent QA sampling across long-tail cases. Outputs can include per-frame annotations, track IDs, timestamps, and scene-level metadata in JSON/JSONL, plus frame-index manifests for efficient training ingestion.

3D/4D point cloud annotation for robotics, autonomy, and mapping

For LiDAR and point-cloud workloads, we provide 3D cuboids, segmentation, tracking, and attribute tagging for objects and scene elements. Abaka Forge supports 3D/4D pipelines with review layers, consensus checks, and production reporting. We can export in your required schemas (e.g., JSON with 3D boxes, velocities, track IDs, and class attributes), and align on coordinate frames, sensor metadata, and class definitions during onboarding so labels remain consistent across time and across teams.

Multilingual annotation for translation quality, sentiment, and intent

If your product serves global users, English-only labeling becomes a bottleneck. Abaka supports multilingual classification and generation evaluation across 50+ countries, including translation QA, sentiment/intent tagging, and locale-specific safety and tone checks. We can maintain language-specific guidelines and escalation paths for culturally nuanced edge cases. Deliveries can be language-partitioned JSONL, with metadata for dialect/locale and annotator proficiency—helpful for downstream analysis and error clustering.

Managed operations in Abaka Forge—automation, QA, and governance

Labeling at scale is an operations problem. Abaka Forge unifies collection, cleaning, annotation, and production delivery with automation that can be up to 50× faster via large-model assistance. Your team gets clear throughput dashboards, QA analytics, and auditable guideline versions. We can run fully managed projects (you review samples and approve releases) or integrate into your existing stack with agreed exports, acceptance tests, and weekly operating cadences.

Why Outsource Data Annotation Services

Faster Delivery

Outsourcing is not just “more hands”—it’s fewer bottlenecks. With Abaka, you avoid weeks of recruiting, training, and building QA from scratch. You get a production-ready pipeline that can start with a pilot, stabilize guidelines, and then scale without resetting the process. Because we cap throughput at 500 files/day per annotator, speed comes from parallelism and workflow design—not rushed work—so you get faster iteration cycles without sacrificing label integrity.

Direct Savings

Internal labeling costs hide in fully loaded engineering time, tool maintenance, rework, and opportunity cost. Abaka converts that overhead into a transparent, scoped delivery with predictable unit economics. You can use specialized roles (e.g., LLM math/coding annotators at $18/hr, STEM generalists at $12/hr, dense captioning at $6/hr, image editing at $8/hr) instead of over-allocating expensive internal staff. The result: less re-labeling, fewer failed training runs, and better budget control.

Risk Reduction

Labeling touches proprietary and sometimes sensitive data. Abaka reduces vendor and compliance risk with SOC 2 and ISO 27001 controls, GDPR and CCPA readiness, strict NDAs, and segregated secure pipelines. Just as important, we provide full IP provenance and 0% copyright risk on collected data—so you aren’t training models on questionable sources. You also avoid the strategic risk of vendors who reuse your datasets: Abaka never builds models that compete with you, and your data is exclusively yours.

Elastic Scalability

AI roadmaps rarely grow linearly. One week you need a 2,000-sample pilot; the next you need a 200,000-sample refresh, or a multimodal push across image + text + video. Abaka’s 1M+ vertically specialized annotator network across 50+ countries lets you ramp capacity up or down while maintaining consistent QA gates and governance. This elasticity prevents backlogs from becoming product delays and keeps model teams focused on experiments rather than staffing.

Domain Expertise

General labelers can’t reliably judge medical reasoning, legal nuance, competitive math, or secure coding standards. Abaka’s scholar-network spans automobile, coding, languages, mathematics, medicine, science, business, and law—so you can match tasks to qualified reviewers. This is especially critical for RLHF, evaluations, and high-stakes classification, where a small rate of expert mistakes can skew learning signals. Expert-led adjudication also shortens the time it takes to stabilize guidelines and resolve edge cases.

Innovation Velocity

When labeling becomes a stable service, your team can iterate on what matters: better prompts, better architectures, better evaluations, and tighter product feedback loops. Abaka supports experimentation-friendly operations—A/B guideline tests, targeted edge-case sampling, and rapid dataset refreshes—so you can improve model behavior without waiting for a new internal workflow to be built each time. With Abaka Forge automation and QA instrumentation, labeling becomes an engine for iteration rather than a constraint.

Industries We Serve

Automotive

Automotive AI teams need consistent labeling across long-tail driving scenarios—often spanning video, 3D point clouds, and sensor fusion. Abaka supports lane and scene understanding tasks, object annotation, and road context labeling with governance that can withstand dataset refresh cycles. For map-adjacent workflows and road features, we also support road lane annotation priced per distance ($3/km), plus metadata tagging for conditions like weather, lighting, and occlusion so you can stratify training and evaluation.

GenAI / Foundation Models

Frontier GenAI teams rely on high-quality instruction following, preference signals, and domain-specific judgments. Abaka provides RLHF labeling, rubric scoring, and specialist review across math, coding, science, and multilingual content. We help you maintain consistent schemas (JSONL), rater calibration, and audit trails—so you can run repeatable training cycles and avoid “mystery improvements” that can’t be reproduced. When you need speed, Abaka Forge automation accelerates workflow steps while preserving QA gates.

Embodied AI / Robotics

Robotics and embodied agents depend on spatial, temporal, and goal-conditioned labels—often requiring video spatial reasoning, 3D annotation, and environment feedback signals. Abaka supports video labeling (actions, steps, temporal segments) and 3D/4D point cloud annotation aligned to your coordinate frames and class definitions. For teams building agents, we can also support custom RL environment design via Abaka’s RL environment capability, enabling training data and evaluation loops that map to real agent behaviors.

Healthcare

Healthcare-adjacent AI demands careful data handling, strong governance, and expert review—especially for medical reasoning tasks or clinical text classification. Abaka supports medical-domain annotation and evaluation with strict NDAs, secure pipelines, and compliance controls (SOC 2, ISO 27001, GDPR, CCPA). We help you define clear label taxonomies, adjudication processes, and error reporting so you can identify systematic failure modes and reduce rework. Your team gets transparent QA sampling and release sign-offs.

Retail

Retail AI uses annotation for product categorization, search relevance, visual similarity, shelf analytics, and customer support automation. Abaka supports CV labeling (boxes, segmentation), text labeling (intent, sentiment), and multimodal pairs for product understanding. We can structure schemas that connect images, attributes, and copy, enabling better retrieval and recommendation performance. With elastic staffing, you can scale labeling during seasonal catalog refreshes without diverting internal teams from growth priorities.

Finance

Finance use cases often involve high sensitivity and strict governance: document classification, entity extraction, risk text analysis, and LLM evaluation for accuracy and tone. Abaka delivers annotation pipelines designed for auditable quality, with segregated workflows, strict NDAs, and proven compliance controls (SOC 2, ISO 27001). For LLMs, we provide rubric-based evaluation and preference labeling so outputs can be aligned to policy and customer experience requirements while remaining measurable and repeatable.

Geospatial

Geospatial teams work across satellite imagery, aerial video, map features, and sensor-derived layers. Abaka supports image and video annotation for land use, object detection, change detection tagging, and metadata enrichment (timestamping, geotags, condition labels). When your pipelines combine multiple sources, we help standardize schemas and deliver consistent outputs for training and evaluation. Governance and IP provenance matter here—Abaka’s approach helps keep datasets defensible and traceable.

Security / Defense

Security-focused AI requires accuracy, controlled access, and careful handling of sensitive content. Abaka supports secure labeling pipelines with SOC 2 and ISO 27001 controls, strict NDAs, and segregated secure workflows. Common tasks include object and event labeling in video, image classification, multilingual text triage, and evaluation of model behavior under adversarial prompts. We also support red-teaming style evaluation workstreams when you need to pressure-test failure modes before deployment.

Agriculture / Industrial

Industrial and agriculture AI teams use annotation for defect detection, equipment monitoring, yield estimation, and safety compliance. Abaka supports vision labeling (segmentation for defects, keypoints for parts), time-series-adjacent metadata tagging, and multilingual instructions for field operations. With consistent QA and stable taxonomies, you can compare model improvements across seasons or production lines. Abaka’s elastic capacity helps you handle bursts—like harvest periods or maintenance cycles—without rebuilding the labeling organization each time.

How It Works

1) Day 0–3 — Scope, schema, and acceptance tests

We start by translating your model goals into a labeling spec: label taxonomy, edge-case policy, and output schema (e.g., JSONL for RLHF; masks for segmentation; track IDs for video). We define acceptance tests—sample-based QA thresholds, error categories, and what “done” means—so delivery is measurable. Security and compliance onboarding happens in parallel: NDAs, access controls, and data transfer procedures aligned to SOC 2 / ISO 27001 practices.

2) Week 1–2 — Pilot batch + guideline stabilization

We run a pilot to surface ambiguity early. Your team reviews curated samples, we measure disagreement, and we iterate the guideline until edge cases are resolved. This is where most projects win or lose: investing a few days here prevents weeks of rework later. We also calibrate rater pools (generalist vs scholar-domain) and finalize workflow routing in Abaka Forge—primary labeling, secondary review, adjudication, and gold-set checks.

3) Week 2–3 — Scale production with QA instrumentation

After the pilot is approved, we scale throughput while protecting consistency. Abaka enforces quality gates, caps throughput at 500 files/day per annotator, and applies multi-layer QA sampling to catch drift. We deliver in agreed increments (daily or multi-week drops) with clear reporting: volume completed, rework rate, error types, and guideline change log. For multimodal projects, we align deliveries so training pipelines can ingest without manual stitching.

4) Ongoing — Refreshes, edge-case mining, and iteration support

As your model evolves, your data needs evolve too. Abaka supports dataset refreshes, targeted edge-case labeling, and ongoing RLHF/evaluation cycles. We can run focused workstreams—like instruction-following checks, hard-negative mining, or domain expansion into new languages—without disrupting your baseline pipeline. You get stable ops plus the ability to add new label types or new modalities as your product expands.

5) Weekly — Operating cadence, reporting, and change control

We keep your program predictable with a weekly cadence: status reporting, sample review, and change-control on guidelines. If requirements shift (new classes, new rubrics, new edge cases), we version the spec and run a small validation batch before scaling changes across the full workforce. This reduces regressions and keeps your training data comparable across releases—so when your model improves (or degrades), you can trace why.

Modality & Format Coverage

You rarely train on one modality forever. Abaka’s data annotation services are built to span text, RLHF, images, video, audio, and 3D/4D—without forcing your team to re-platform or re-train a new vendor for each new dataset. We use Abaka Forge to standardize operations: task routing, QA sampling, adjudication, and auditable exports. Below are common modalities, annotation types, tooling, and delivery formats we support.

Modality	Annotation Types	Tools	Output Formats
Text	Classification (intent/sentiment), NER, summarization quality scoring, safety/tone checks, retrieval relevance labels	Abaka Forge	JSON, JSONL/NDJSON, CSV (on request)
LLM RLHF	Pairwise preference, ranking, rubric-based scoring, instruction-following evaluation, tool/function-call correctness checks	Abaka Forge	JSONL with prompt/candidates/labels, rubric tables (CSV), evaluator metadata exports
Image	Bounding boxes, polygons, semantic/instance segmentation, keypoints, dense captioning	Abaka Forge	JSON/COCO-style JSON (when requested), PNG masks, CSV manifests
Video	Object tracking, temporal segments, actions/events, keyframes, scene attributes	Abaka Forge	JSON with timestamps/track IDs, frame-index manifests (CSV/JSON)
3D/4D Point Cloud	3D cuboids, 3D segmentation, tracking over time, attributes (velocity, occlusion, state)	Abaka Forge	JSON with 3D boxes/track IDs, sequence-level manifests
LiDAR + Camera fusion	Cross-sensor consistency checks, 2D–3D association labels, fused object attributes	Abaka Forge	JSON with sensor references, synchronized frame manifests
Audio	Transcription, speaker diarization tags, intent/sentiment, QA scoring for TTS/ASR outputs	Abaka Forge	JSON/JSONL, TXT (transcripts), CSV (timecode tables)

Success Story

A frontier model lab improving instruction-following and reasoning quality

Challenge

A leading frontier model lab needed a scalable pipeline for RLHF and instruction-following evaluation across multiple domains (general, math, and coding). Their internal team could run small experiments, but consistency broke down when they tried to scale: rubric drift across reviewers, uneven edge-case handling, and slow turnaround that delayed training cycles. They also needed strong governance—clear guideline versioning, auditable exports, and a vendor that would not reuse their data for competing models.

Approach

Abaka designed a rubric-driven RLHF workflow in Abaka Forge with calibrated reviewer pools: STEM generalists for broad coverage and scholar-network specialists for math and coding adjudication. We ran a pilot batch to stress-test ambiguous prompts, then stabilized guidelines with an explicit edge-case policy and decision logs. Production scaled via multi-layer QA: gold sets for ongoing calibration, targeted sampling for high-risk categories, and adjudication for disagreements. Deliveries shipped as JSONL with structured fields (prompt, candidates, rubric scores, preference labels, and reviewer metadata) so the customer could analyze variance and retrain selectively.

Results

Within weeks, the lab moved from ad-hoc labeling to a repeatable production pipeline. They reduced guideline churn by versioning changes and validating them on a small holdout before scaling. Reviewer variance dropped as calibration sets became routine, enabling cleaner learning signals for preference optimization and instruction-following improvements. Most importantly, the model team regained iteration speed: training runs were fed by predictable weekly drops rather than sporadic batches, and error analysis could be traced back to specific rubric dimensions and edge-case categories. - **Outcome 1:** **2–3 week** turnaround from scoped spec to scaled production delivery - **Outcome 2:** **99%** target accuracy supported by multi-layer QA and adjudication - **Outcome 3:** Access to **1M+** annotators across **50+** countries for elastic scaling

2–3 weeks

From onboarding to scaled delivery

99%

Target annotation accuracy

1M+

Specialized annotator network

By the Numbers

1,000+

Enterprise and research customers

1M+

Vertically specialized annotators

50+

Countries represented in the workforce

99%

Target accuracy with multi-layer QA

What Customers Say

We were stuck in a loop of labeling, discovering inconsistencies, and then re-labeling. Abaka helped us stabilize guidelines early and introduced a review cadence that made quality measurable. The biggest change was operational: we finally had predictable weekly deliveries that our training pipeline could rely on, instead of sporadic batches that disrupted our experiments.

**Head of ML** GenAI Platform, Series B

Our use case needed domain judgment, not just generic labeling. Abaka staffed reviewers who could handle math and code tasks with consistent rubrics, and the adjudication process was clear when disagreements happened. The audit trail and versioned guidelines made it easy to explain dataset changes to internal stakeholders and keep experiments reproducible.

**Director of Applied AI** Frontier Model Lab

Security reviews usually slow vendors down, but Abaka’s process was straightforward. The secure pipeline approach and clear NDA posture reduced back-and-forth with our compliance team. We also valued the commitment that our data wouldn’t be repurposed or used to build competing models—this mattered for both legal and strategic reasons.

**Security & Compliance Lead** Enterprise Software Company

We needed to scale quickly across multiple modalities while keeping the labeling schema consistent. Abaka handled the operational complexity—routing, QA sampling, and reporting—so our engineers stayed focused on model improvements. The net effect was fewer surprises in training, faster iteration, and a clearer view of where errors came from.

**ML Engineering Manager** Autonomy-Adjacent Robotics Team

Why Choose Abaka

A trustworthy data partner built for frontier AI

Abaka is a trustworthy data partner for frontier AI—founded in 2019, self-funded and profitable, with offices in Singapore, Paris, and Silicon Valley, serving 1,000+ enterprise and research customers. That operating model matters: no VC pressure, no acquisition incentives, and no reason to repurpose your data. We never build models that compete with you. Your data is exclusively yours—never resold, shared, or reused.

Quality systems designed to prevent drift

High-quality annotation is a system, not a promise. Abaka’s pipelines use calibrated reviewers, gold sets, adjudication, and sampling-based QA to keep guidelines consistent as volume scales. We also cap throughput at 500 files/day per annotator to avoid “speed over accuracy” failure modes. Your team gets reporting that highlights error types and drift so you can intervene early—before label noise becomes model brittleness.

Secure, compliant delivery with clear governance

Abaka supports SOC 2 and ISO 27001 controls with GDPR and CCPA readiness, strict NDAs, and segregated secure pipelines. This reduces procurement friction and helps you move sensitive datasets through labeling without improvising security. We also provide full IP provenance and 0% copyright risk on collected data, so your training corpus remains defensible over time—especially important as models move closer to regulated or high-stakes deployment.

Abaka Forge—one platform for multimodal annotation operations

Abaka Forge is our all-in-one platform for collection, cleaning, annotation, and production delivery across image, video, text, RLHF, and 3D/4D point cloud. Teams use it to manage task routing, guideline versions, review queues, and exports—while benefiting from large-model automation that can be up to 50× faster in specific workflow steps. If you prefer a managed service, we run the platform for you; if you prefer integration, we align exports to your ingestion pipeline.

Domain-specialized workforce at real scale

You don’t need “more labelers,” you need the right labelers. Abaka provides access to 1M+ vertically specialized annotators across 50+ countries, including scholar-network expertise in automobile, coding, languages, mathematics, medicine, science, business, and law. That breadth lets you run mixed programs—general labeling at scale plus expert adjudication where it matters—without juggling multiple vendors or reinventing your QA stack each time.

Transparent pricing paths—from pilots to production

Abaka offers practical pricing models that map to real work: hourly specialist rates (e.g., LLM math/coding at $18/hr; STEM generalist at $12/hr) and unit-based programs for specific tasks (e.g., dense captioning at $6/hr; road lane annotation at $3/km). For teams using Abaka Forge as a platform, credits are available at $0.20 USD each. This makes it straightforward to run a small pilot, measure outcomes, and then scale with predictable economics.

Frequently Asked Questions

Expand all

How much do data annotation services cost?

Pricing depends on modality and expertise. Examples: LLM math/coding annotation is $18/hr, STEM generalist is $12/hr, dense captioning is $6/hr, image editing is $8/hr, and road lane annotation is $3/km. We typically start with a small pilot to confirm guidelines and QA, then provide a scoped quote for scaled production based on volume and review depth.

How fast can you deliver a pilot and then scale production?

Most teams can complete onboarding and a pilot within 1–2 weeks, then scale production in week 2–3 after guidelines stabilize. Timelines vary by modality, schema complexity, and review requirements. We keep delivery predictable by defining acceptance tests early and running a validation batch when guidelines change, so you avoid surprises during scaling.

What data formats do you support for labeled outputs?

We commonly deliver JSON, JSONL/NDJSON, and CSV manifests, plus modality-specific artifacts like PNG masks for segmentation and timestamped track files for video. For RLHF, we can export JSONL with prompt, candidates, rubrics, and preference labels. During scoping, we align on a schema that matches your training ingestion pipeline to minimize custom glue code.

How do you ensure annotation accuracy and consistency?

We use multi-layer QA with calibrated annotators, gold sets, targeted sampling, and adjudication for disagreements. We also cap throughput at 500 files/day per annotator to reduce fatigue-related errors. Guidelines are versioned, and we validate changes on a small batch before scaling. This keeps labels consistent over time, even as you expand classes, domains, or modalities.

Can you work with sensitive or proprietary data securely?

Yes. Abaka operates with SOC 2 and ISO 27001 controls and supports GDPR and CCPA readiness, strict NDAs, and segregated secure pipelines. We agree on access controls and data handling procedures during onboarding. We also provide full IP provenance and do not repurpose your data—your datasets remain exclusively yours throughout the engagement.

Do you support multilingual annotation and global languages?

Yes. Abaka supports multilingual labeling across 50+ countries, including intent/sentiment, translation QA, locale-specific tone checks, and multilingual RLHF evaluation. We can maintain language-specific guidelines and escalation paths for cultural nuance. Outputs can be delivered as language-partitioned JSONL with metadata for locale and reviewer calibration, helping you analyze performance by region.

How are you different from other data labeling companies?

Abaka combines a large specialized workforce (1M+ annotators) with Abaka Forge operations—routing, QA, and auditable exports—plus strong governance (SOC 2, ISO 27001, segregated pipelines). Strategically, we never build models that compete with you, and your data is never repurposed, resold, or shared. That alignment reduces long-term risk for frontier AI teams.

What if we need changes to guidelines or labels mid-project?

Change is normal. We use versioned guidelines and a change-control process: propose the update, run a small validation batch, measure impact on disagreement and error types, then scale once approved. If re-labeling is required, we scope it explicitly (what changed, which items are impacted, and acceptance criteria) so you maintain dataset comparability across releases.

Can we start with a small pilot before committing to a large program?

Yes—starting with a pilot is recommended. A pilot helps validate label taxonomies, uncover edge cases, and calibrate reviewers before you scale. We typically define acceptance tests and deliver a pilot batch quickly, then iterate on guidelines with your feedback. Once the pilot is approved, we scale production with the same QA gates and reporting cadence.

Who owns the labeled data and the guidelines we create together?

You do. Your data is exclusively yours and is not repurposed, resold, or shared. We also do not build models that compete with you. We operate under strict NDAs and secure pipelines, and we can structure deliveries so that labeled outputs, schemas, and guideline artifacts are stored and returned according to your internal governance requirements.

What tools do you use for annotation—can you work with our stack?

We can run projects in Abaka Forge, our platform for collection, cleaning, annotation, and production delivery across modalities. If you have an existing toolchain, we can align exports and schemas to your ingestion requirements. The key is maintaining consistent QA gates, reviewer calibration, and auditable guideline versions—regardless of where labels are produced.

Is there a minimum project size for data annotation services?

There’s no single minimum that fits every modality, but we typically recommend a pilot batch large enough to expose edge cases and measure agreement reliably. Even small programs can be worthwhile if the task is high-value (e.g., expert RLHF or evaluation). After reviewing your scope, we’ll propose a right-sized pilot and a scale plan aligned to your timeline and budget.

Ready to Get Started?

If your roadmap is blocked by inconsistent labels, slow throughput, or compliance overhead, Abaka can help you turn data annotation into a predictable production system. Share your modality, volume, and target schema—we’ll propose a pilot, acceptance tests, and a scale plan that protects quality while improving iteration speed. Talk to an Expert at business@abaka.ai. Human Intelligence — Data for Frontier AI