Abaka delivers scholar-reviewed text labels, NER, and LLM-ready datasets with multi-layer QA, secure pipelines, and elastic throughput for your team’s training and evaluation cycles.
When text labels slip, everything downstream compounds: intent models drift, retrieval quality drops, and RLHF rewards teach the wrong behavior. Teams often discover too late that 5–10% noisy annotations can erase weeks of training gains, while internal reviewers spend 20–40% of their sprint time rechecking work instead of shipping. The result is delayed launches, higher inference and support costs, and governance gaps—especially when datasets span multiple languages, domains, or policy-sensitive content.
Abaka fixes the bottleneck with Human Intelligence — Data for Frontier AI: vertically specialized annotators across 50+ countries, scholar-network reviewers for high-stakes domains, and Abaka Forge workflows that standardize guidelines, sampling, and audits. You get consistent schemas, measurable acceptance criteria, and a production pipeline that scales from pilot to millions of examples—without repurposing your data or building models that compete with you.
Text labeling quality drops as projects expand from a few thousand examples to multiple domains and languages. Without calibrated rubrics, edge cases and ambiguous intents create inconsistent tags, and a small error rate can dominate training signal. Abaka runs multi-layer QA with gold sets, consensus checks, and expert escalation so you can target 99% accuracy where it matters most—especially for medical, legal, coding, and reasoning-heavy corpora.
Most teams hit a throughput ceiling: internal SMEs can’t review fast enough, and vendor pipelines stall on unclear guidelines. Abaka scales through 1M+ specialized annotators and controlled throughput (up to 500 files/day per annotator) to prevent fatigue-driven mistakes. With Abaka Forge, you can ramp volume without changing the schema midstream, keeping training batches stable and reducing relabel cycles that can add 2–4 weeks to delivery.
Text data often contains PII, regulated content, or proprietary knowledge. If access controls, audit trails, and provenance aren’t built in, reviews slow down and security teams block production. Abaka operates under SOC 2 and ISO 27001 aligned practices with GDPR/CCPA readiness, strict NDAs, and segregated secure pipelines. You get full IP provenance and 0% copyright risk on collected data—so approvals move faster and releases don’t get stuck in legal review.
Design and execute NER pipelines for products, places, organizations, medical entities, and policy-sensitive terms. Abaka Forge supports span-level labeling, nested entities, attributes, and adjudication. Your team gets consistent guidelines, reviewer escalation, and exports for modern NLP stacks. Typical outputs include BIO/BILOU tags, JSONL span schemas, and CoNLL-style files for search, compliance, and assistant workflows.
Build robust intent sets for customer support, enterprise copilots, and workflow automation. We help define intent hierarchies, handle ambiguous utterances, and attach metadata such as channel, sentiment, and risk flags. Abaka’s multi-layer QA reduces label drift over time while maintaining stable definitions. Deliverables ship in CSV/JSONL with train/validation splits and change logs aligned to your release cadence.
Turn messy, evolving categories into a versioned taxonomy your models can learn. Abaka supports ontology building, label maps, synonym dictionaries, and deprecation rules. Use Abaka Forge to track guideline revisions and retroactive relabel requirements. This is especially useful for retail catalogs, finance topics, automotive service logs, and security triage where long-tail concepts drive user outcomes.
Annotate content for safety classes like self-harm, hate, harassment, sexual content, and regulated advice, with escalation paths and reviewer calibration. Abaka’s secure pipelines and strict NDAs support sensitive corpora. Outputs can include multi-label tags, severity ratings, and rationale fields to support audits and evaluation. This work pairs naturally with red-teaming and human evaluation loops for LLM governance.
For advanced LLM training, Abaka provides scholar-network reviewers across mathematics, coding, and science domains. We can label reasoning difficulty, verify solutions, and structure prompts/responses for instruction following. When needed, teams can staff Math/Coding specialists at $18/hr and STEM generalists at $12/hr, with QA gates and sampling plans implemented in Abaka Forge for consistent acceptance criteria.
Scale annotation for multilingual chat, translation evaluation, and regional compliance. Abaka recruits native speakers and applies locale-specific style guides so labels reflect real user intent, not literal translations. You can run language-specific rubrics, back-translation checks, and reviewer adjudication. Deliverables include language-coded JSONL/CSV, aligned segments, and metadata for dialect and register—useful for global assistants and cross-market analytics.
Create pairwise preferences, multi-response rankings, and rubric-based scoring that directly feeds RLHF training. Abaka Forge enables task templating, rater calibration, and audit sampling so reward models learn stable signals. We support instruction following, refusal correctness, helpfulness/harmlessness, and style constraints. Outputs include preference JSONL, rubric score tables, and rater metadata for bias and drift analysis.
Move from ad hoc checks to a measurable production system: gold sets, inter-annotator agreement targets, adjudication queues, and weekly acceptance dashboards. Abaka Forge tracks guideline versions, label distributions, and change requests so you can reproduce datasets across releases. This is critical for regulated industries and foundation model labs that require provenance and repeatable evaluation.
Ramp from pilot to production without waiting to hire and train an internal labeling team. Abaka standardizes guidelines and QA so you can start shipping usable batches in days, not quarters, and keep a steady weekly cadence for training runs.
Reduce relabeling, reviewer bottlenecks, and tooling overhead. With Abaka Forge workflow templates and calibrated reviewers, you spend less engineering time on cleanup and more on model iteration—especially when tasks span multiple teams and languages.
Operate with SOC 2 and ISO 27001 aligned controls, strict NDAs, segregated pipelines, and GDPR/CCPA readiness. Your data remains exclusively yours—never repurposed, resold, or shared—supporting safer procurement and governance approvals.
Scale up for launch deadlines and scale down after milestones without disrupting definitions or quality gates. Abaka’s global workforce and controlled throughput (up to 500 files/day per annotator) help avoid burnout-driven label drift at high volume.
Access scholar-network specialists in automobile, medicine, law, mathematics, and coding for high-stakes text corpora. This prevents “surface-level” labels that look fine statistically but fail on edge cases your users care about.
Experiment faster with new schemas like rationale fields, multi-intent routing, or preference rankings for RLHF. Abaka can stand up pilots, validate rubrics, and roll successful formats into production—without derailing your main roadmap.
Annotate service logs, driver feedback, and ADAS incident narratives for intent, taxonomy, and safety signals. Abaka supports domain-specific terminology and escalation for edge cases so downstream models can power diagnostics, support copilots, and fleet analytics.
Create instruction-following datasets, preference rankings, and high-quality text corpora for pretraining and post-training. Abaka’s scholar-network reviewers and RLHF workflows help your team produce stable signals across reasoning, coding, and policy tasks.
Label robot logs, natural-language commands, and task narratives that connect perception to action. Abaka helps define hierarchical intents, failure taxonomies, and safety categories so agents learn robust behaviors and your evaluations reflect real operator language.
Annotate clinical-style text, call transcripts, and patient-facing assistant content with careful escalation, privacy controls, and domain reviewers. Use NER for conditions/medications, structured triage intents, and safety constraints to reduce harmful outputs.
Normalize product and search queries into clean taxonomies: intent, category mapping, sentiment, and returns reasons. Abaka’s structured guidelines improve retrieval and recommendation quality while keeping long-tail categories consistent across seasonal catalog changes.
Label communications and documents for compliance, risk, and customer intent—supporting triage, summarization, and monitoring workflows. Abaka’s secure pipelines and audit trails help you maintain provenance while improving model reliability on edge cases.
Annotate place names, POI mentions, and location-linked narratives for entity resolution and structured extraction. Abaka supports multilingual variants and normalization to internal IDs, enabling better search, routing assistants, and geospatial analytics.
Classify and extract signals from reports, tickets, and analyst notes with strict access control and segregated workflows. Abaka supports taxonomy-based triage, red-flag labeling, and reviewer adjudication to improve detection and reduce false positives.
Label maintenance notes, incident descriptions, and operator logs for fault categories, parts, and root-cause signals. Abaka helps build stable ontologies across sites and languages, enabling faster diagnosis, better support, and more reliable automation.
We align on your target model outcomes, define label schemas (entities, intents, taxonomies, rationales), and set measurable QA gates. Abaka configures Abaka Forge projects, access controls, and sampling plans, then imports a small calibration set to validate guidelines.
Abaka runs a pilot with trained annotators and reviewer adjudication to surface edge cases early. You receive batch outputs plus error analysis: confusion matrices for intents, boundary disagreements for NER, and guideline refinements so production doesn’t drift as volume grows.
After pilot sign-off, we scale workforce and automation while keeping the same rubric and audit plan. Abaka Forge enforces task templates, gold checks, and reviewer escalation. Deliverables ship in your preferred formats (JSONL/CSV/CoNLL) with version tags.
We maintain secure, segregated pipelines with strict NDAs and provenance tracking. As your product evolves, we manage label-map changes, deprecations, and retroactive relabel decisions. Your team gets predictable delivery without rebuilding internal operations.
Every week, you receive QA metrics, sampled audits, and issue logs: edge-case counts, guideline clarifications, and drift warnings. We review upcoming releases and adjust the queue, ensuring training and evaluation datasets stay aligned to your roadmap and policy needs.
Text Annotation Services often connect to multimodal training and evaluation. Abaka supports consistent schemas across text, RLHF, and multimodal data, with exports designed for modern LLM pipelines and reproducible dataset versioning.
| Modality | Annotation Types | Tools | Output Formats |
|---|---|---|---|
| Text | NER (span), intent & topic classification, taxonomy mapping, sentiment & tone tags, rationale fields | Abaka Forge | JSONL, CSV, TSV, CoNLL, Parquet |
| LLM RLHF | Pairwise preference, multi-response ranking, rubric scoring, refusal correctness checks, safety & policy reviews | Abaka Forge | Preference JSONL, rubric score tables (CSV), prompt-response logs, Parquet, eval reports (PDF/HTML) |
| Image | Captioning, dense captioning, VQA pairs, text-in-image transcription, multimodal safety tags | Abaka Forge | JSON, JSONL, COCO-style JSON, CSV, Parquet |
| Video | Temporal captions, instruction following from video, event tagging, spatial reasoning QAs, safety classification | Abaka Forge | JSONL, CSV, timecoded segments (SRT/VTT), Parquet, clip manifests |
| 3D/4D Point Cloud | Object labeling, semantic segmentation, scene descriptions, instruction grounding text, QA audits | Abaka Forge | JSON, CSV, PLY metadata, Parquet, sequence manifests |
| LiDAR + Camera fusion | Cross-sensor object alignment, lane/scene descriptions, sensor-synced narratives, QA sampling, edge-case tagging | Abaka Forge | JSON, CSV, sensor sync manifests, Parquet, dataset version logs |
| Audio | Transcription, speaker diarization tags, intent from calls, sentiment, safety & policy labels | Abaka Forge | JSONL, CSV, RTTM, SRT/VTT, Parquet |
The team needed a large, high-precision text annotation pipeline to improve instruction following and reduce refusal mistakes across multiple languages. Prior vendor outputs looked acceptable on aggregate but failed on edge cases, and internal SMEs were spending too much time adjudicating inconsistent labels. They also required strict data handling: segregated access, auditable exports, and clear provenance so the resulting datasets could be reused safely across training runs and evaluations.
Abaka designed a rubric-first workflow in Abaka Forge: taxonomy definitions, gold sets for calibration, and an escalation ladder to scholar-network reviewers in mathematics, coding, and safety. We ran a pilot to quantify disagreement drivers, tightened guidelines, and then scaled workforce while preserving QA gates. The pipeline produced parallel datasets for supervised fine-tuning and RLHF preferences, with versioned exports (JSONL/Parquet) and weekly reports covering drift, error clusters, and change requests.
Within three weeks, the customer moved from pilot to steady production with consistent labels across languages and domains. Reviewer workload dropped because edge cases were resolved upstream through adjudication rules rather than ad hoc SME intervention. The lab shipped new training batches on a weekly cadence and saw measurable improvements in instruction-following stability and refusal correctness—while maintaining secure handling and repeatable dataset provenance with 99% accuracy targets and controlled throughput up to 500 files/day per annotator.
We came in with a messy intent set and inconsistent labels from multiple sources. Abaka helped us lock the taxonomy, calibrate reviewers, and deliver weekly batches we could train on immediately. The audit trail and versioning made it easy to reproduce experiments and explain changes to stakeholders.
The difference was guideline discipline. Edge cases were surfaced early, adjudicated, and documented instead of being rediscovered every sprint. Our SMEs stopped acting as a manual QA team and started focusing on evaluation design and model behavior analysis.
Security reviews usually slow vendors down, but Abaka’s segregated workflows and NDAs were straightforward. We were able to share sensitive text safely, get consistent outputs, and maintain provenance for future reuse without worrying about data repurposing.
We needed multilingual coverage and consistent intent tags across regions. Abaka staffed native speakers, ran calibration checks, and gave us clean exports aligned to our training pipeline. The weekly reporting made drift visible before it impacted production metrics.
Abaka combines scholar-grade reviewers, a global specialized workforce, and Abaka Forge tooling to deliver text labels you can train and evaluate on with confidence. You get secure, segregated pipelines; clear provenance; and versioned exports—plus a partner that never builds models that compete with you. Your datasets stay exclusively yours: never repurposed, resold, or shared. The outcome is faster iteration with fewer relabel cycles and fewer surprises at deployment.
Multi-layer QA with gold sets, consensus checks, and adjudication helps you hit 99% accuracy targets where it matters—especially for NER boundaries, ambiguous intents, and policy-sensitive classes.
Access domain specialists across medicine, law, mathematics, coding, and science to validate high-stakes labels and prevent “looks-right” annotations that fail on real edge cases.
Standardize task templates, sampling, and audits in Abaka Forge so guidelines don’t drift. You receive versioned exports, change logs, and repeatable datasets aligned to your release cadence.
SOC 2 and ISO 27001 aligned operations, strict NDAs, segregated pipelines, and GDPR/CCPA readiness support sensitive text. Full IP provenance keeps data governance defensible over time.
Abaka is self-funded and profitable, founded in 2019, with offices in Singapore, Paris, and Silicon Valley. With no model competition and no acquisition pressure, we optimize for consistent delivery and long-term trust—so your annotation program remains stable as your datasets and governance needs grow.
Label the Present. Train the Future.