Ship high-accuracy text labels, taxonomies, and RLHF signals with scholar-grade QA, secure pipelines, and Abaka Forge workflows tuned for your model and domain.
When text annotation slips, model quality follows. A 2–3 week delay to fix guidelines, redo batches, or reconcile label drift can push your release window and inflate costs by 20–40% through rework. In production, inconsistent entities, intents, or policy labels show up as hallucinations, unsafe outputs, and brittle tool calling—issues that are hard to debug after training. If your dataset grows while your QA stays flat, you’ll see accuracy decay over time as edge cases accumulate and reviewers interpret rules differently.
Abaka AI helps your team operationalize text annotation at scale without sacrificing rigor. We combine vertically specialized annotators across 50+ countries with multi-layer QA, measured acceptance gates, and Abaka Forge to manage instructions, sampling, and adjudication. Whether you’re building NER corpora, intent/slot datasets, preference signals for instruction tuning, or evaluation sets, you get consistent guidelines, secure handling (SOC 2, ISO 27001, GDPR, CCPA), and throughput that matches your roadmap—without vendor lock-in or data resale.
Text labels drift quietly: new edge cases arrive, reviewers interpret guidelines differently, and yesterday’s “good enough” becomes today’s model bug. Even a 1–2% consistency drop across NER spans or intent classes can ripple into retrieval, routing, and safety layers. Abaka mitigates decay with tight rubric design, calibration rounds, and adjudication workflows in Abaka Forge, plus controlled reviewer throughput (up to 500 files/day per annotator) to preserve attention and reduce fatigue-driven mistakes.
Most teams hit a wall when annotation demand spikes—new locales, new product features, or a sudden push for instruction tuning. Hiring and training internally can take 6–10 weeks, and ramp quality often lags another sprint. Abaka provides elastic capacity through a global network of 1M+ specialized annotators across 50+ countries, so you can expand coverage quickly while keeping the same guideline, QA, and sampling system across every batch.
Text datasets frequently include sensitive business context—support logs, contracts, medical notes, or security incidents—so teams lose weeks negotiating controls, access, and provenance. Abaka runs segregated secure pipelines with strict NDAs and supports SOC 2, ISO 27001, GDPR, and CCPA-aligned operations. You maintain full IP provenance and exclusive ownership—your data is never repurposed or resold—reducing downstream legal and privacy risk while keeping delivery moving.
Design entity taxonomies and annotate spans with strict boundary rules, nested entities, and normalization (aliases, canonical IDs). Abaka Forge supports reviewer calibration, adjudication queues, and gold-set sampling to keep 99% accuracy targets realistic at scale. Common outputs include JSONL, BIO/IOB2 tags, and CoNLL-style TSV for search, compliance, and LLM grounding workflows.
Label intent classes, slots, and dialogue states for customer support, banking, retail, and enterprise copilots. We help you define a stable label set, manage long-tail intents, and implement confusion-matrix driven QA. Deliverables include intent/slot JSON, CSV, and conversation-level annotations that plug into NLU stacks or modern LLM routers.
Create multi-level taxonomies for knowledge bases, internal wikis, policy libraries, and product catalogs. Abaka builds guideline examples, edge-case rules, and reviewer playbooks to reduce drift as the taxonomy evolves. Outputs can be multi-label CSV/JSON, YAML label maps, and dataset cards to support training, evaluation, and governance.
Annotate toxicity, self-harm, regulated content, privacy leakage, and policy violations with clear severities and rationales. We structure review to separate sensitive adjudication from high-volume passes and maintain audit-friendly records in Abaka Forge. This is commonly paired with RLHF preference data and red-team evaluation sets for safer deployments.
Collect pairwise preferences, rubric-based scoring, and instruction-following judgments aligned to your product. Abaka’s scholar-network domains (math, coding, medicine, law, business) support complex tasks where generic labeling fails. We deliver JSONL with prompts, responses, ranks, rationale fields (when desired), and reviewer metadata for reproducible training runs.
For advanced reasoning datasets, we route tasks to domain-specialized reviewers—mathematics (including Lean4), coding, and science—so labels reflect correct solutions, not superficial patterns. Abaka Forge enables multi-pass verification and disagreement analysis. Outputs include structured solutions, unit-test style validations, and JSONL schemas suitable for SFT and evaluation.
Scale annotation across 50+ countries with language-aware guidelines and localized examples. We handle translation QA, dialectal variation, and locale-specific policy interpretation while keeping a unified taxonomy. Deliverables include language-tagged JSONL, parallel corpora metadata, and consistent label maps to support multilingual LLM training and evaluation.
Run annotation like a production system: onboarding, rubrics, sampling, adjudication, and weekly reporting. Abaka Forge supports task routing, reviewer calibration, and audit trails while maintaining segregated secure pipelines. You get stable throughput planning, change-control for guidelines, and clear acceptance criteria so your training schedule stays predictable.
Avoid the 6–10 week cycle of hiring, training, and re-training internal labelers. Abaka ramps with specialized annotators and a ready QA system, so you can start producing usable batches in 2–3 weeks while keeping guideline discipline.
Reduce rework costs by using calibrated reviewers, gold sets, and adjudication from day one. With Abaka Forge, your team spends less time chasing label drift and more time improving prompts, models, and evaluations that move product metrics.
Protect sensitive text with SOC 2 and ISO 27001-aligned controls, strict NDAs, and segregated secure pipelines. You keep full IP provenance and exclusive ownership—your data is not repurposed, resold, or shared.
Scale up for launches, new locales, or sudden RLHF pushes without breaking your internal bandwidth. With a global workforce across 50+ countries and controlled throughput, you can grow volume while maintaining review quality gates.
Generic labeling struggles with technical text. Abaka supports scholar-network domains like math, coding, medicine, law, and business, so you can label complex reasoning and compliance-heavy documents with confidence.
Move beyond basic labels into richer supervision—rubric scoring, preference signals, and evaluation sets. Abaka Forge accelerates iteration with automation and structured workflows, helping you test new data strategies without rebuilding tooling.
Label driver-assistance text signals like incident reports, service notes, and edge-case descriptions that feed retrieval and QA copilots. Pair taxonomy labeling with safety policy tags to reduce hallucinations in technician-facing assistants and internal triage workflows.
Build instruction tuning and RLHF datasets with preference labels, rubric scoring, and domain-specialist review. Abaka supports complex tasks (math, coding, law, medicine) and produces structured JSONL suitable for scalable training and evaluation.
Annotate natural-language task plans, operator logs, and human feedback for agents that must follow instructions reliably. Use consistent intent/slot schemas and preference signals to improve tool calling, task sequencing, and failure recovery behaviors.
Create medical text labels for summarization, triage routing, and knowledge-base assistants with privacy-first handling. Abaka supports expert review and audit-ready processes while aligning to GDPR/CCPA requirements and strict NDA-driven access controls.
Label product questions, support chats, and returns narratives into intents, entities, and resolution outcomes. A stable taxonomy improves search, routing, and personalization while reducing time spent manually tagging new SKUs and seasonal catalog changes.
Annotate KYC/AML narratives, disclosures, and customer interactions with policy and risk labels. Abaka’s secure pipelines and adjudication workflows help maintain consistency across regulated terms and reduce downstream compliance escalations.
Label text linked to locations—field notes, incident descriptions, place attributes, and metadata—for retrieval and analytics. Combine structured entity normalization with multilingual coverage to improve global operations and map-related assistants.
Tag reports, advisories, and intelligence-style documents with entities, events, and severity categories. Abaka supports strict NDAs, segregated environments, and audit trails so your team can build reliable text datasets without loosening controls.
Annotate maintenance logs, sensor-related notes, and work-order narratives into standardized taxonomies and intents. This improves dispatch, root-cause analysis, and field assistant reliability—especially when terminology varies by site and region.
We align on your use case (NER, intent/slot, taxonomy, RLHF), define label schemas, and agree on measurable acceptance gates. You share sample data and edge cases; we propose guidelines, QA design, and output formats (JSONL/CSV/CoNLL). Security and access controls are finalized up front.
Abaka runs a pilot to validate guidelines and measure reviewer agreement. We use calibration rounds, gold sets, and adjudication queues in Abaka Forge to surface ambiguity early. You review pilot outputs, adjust rules, and sign off on the “definition of done” before scaling.
We expand to a production team sized to your weekly target while keeping consistent reviewer training and sampling. Throughput is planned to avoid fatigue (up to 500 files/day per annotator) and to maintain quality gates. Deliveries follow your preferred cadence and file formats.
As your taxonomy evolves, we manage change requests with versioned guidelines, backfill strategies, and targeted re-annotation only where needed. We track disagreements, edge-case patterns, and label distributions so you can prevent silent drift across releases.
You receive weekly reporting on volume, QA results, and issue categories, plus recommendations for rubric refinements and automation opportunities. We prioritize the highest-impact fixes—reducing rework and improving dataset consistency as your model, product, and policies evolve.
Text is the core, but your dataset rarely stays text-only. Abaka Forge supports multimodal labeling and RLHF workflows so you can keep one QA system and one delivery standard across modalities.
| Modality | Annotation Types | Tools | Output Formats |
|---|---|---|---|
| Text | NER spans; intent/slot; taxonomy + topics; safety/policy labels; rubric scoring | Abaka Forge | JSONL; CSV; TSV/CoNLL; BIO/IOB2 tags; YAML label map |
| LLM RLHF | Pairwise preference; ranking; scalar ratings; instruction-following checks; rationale (optional) | Abaka Forge | JSONL (prompt/response/rank); CSV exports; Parquet; evaluation-ready schemas |
| Image | Image captioning; dense captioning; VQA pairs; instruction-following checks; safety labels | Abaka Forge | JSON; JSONL; COCO-style JSON; CSV; PNG/JPEG metadata manifests |
| Video | Video captioning; temporal segments; action tags; spatial reasoning QAs; policy labels | Abaka Forge | JSONL; CSV; MP4 metadata manifests; segment timestamps; dataset cards |
| 3D/4D Point Cloud | 3D bounding boxes; semantic classes; track IDs; scene attributes; QA sampling | Abaka Forge | JSON; CSV; PCD/PLY manifests; frame-indexed annotations; Parquet |
| LiDAR + Camera fusion | Cross-sensor alignment checks; fused 3D boxes; occlusion tags; lane/scene attributes; QA audits | Abaka Forge | JSON; CSV; synchronized sensor manifests; frame timestamps; calibration metadata |
| Audio | Transcription; speaker diarization tags; intent from calls; sentiment labels; safety labels | Abaka Forge | JSON; JSONL; SRT/VTT; CSV; time-coded transcripts |
The team needed a text annotation company to build a reliable instruction-tuning and evaluation dataset from internal knowledge and support interactions. Early batches showed label drift across intents and policy categories, and the team’s engineers were spending too much time adjudicating disagreements instead of improving retrieval and routing. They also needed secure handling and strong IP provenance because the dataset contained sensitive, business-specific information that could not be exposed to uncontrolled tooling or reused by a vendor.
Abaka scoped a unified schema for intents, entities, and policy labels, then ran a pilot with calibration rounds to lock down ambiguous edge cases. Using Abaka Forge, we set up gold sets, sampling, and adjudication queues, and routed complex categories to domain-specialist reviewers. We added versioned guidelines and a change-control loop so new intents and policies could be introduced without breaking historical consistency. Deliverables were produced as JSONL and CSV exports for direct integration into the team’s training and evaluation pipelines.
With a stable labeling system and multi-layer QA, the team scaled production while reducing internal adjudication time and preventing silent drift between batches. The dataset became consistent enough to support weekly model iteration and downstream evaluation, with security controls and exclusive ownership maintained throughout. Final deliveries included calibrated intent/slot labels, normalized entities, and policy categories packaged for training and benchmarking, improving turnaround time and enabling measurable quality gates—achieving 99% accuracy targets and launching the program in 2–3 weeks.
We came in with messy guidelines and inconsistent labels across teams. Abaka helped us tighten the schema, run a real pilot, and scale production without losing traceability. Weekly reporting made drift visible early, and our engineers stopped spending their time adjudicating every edge case.
Their strength is operational discipline. The secure pipeline and QA gates were clear, and the outputs were consistently formatted for our training jobs. We could ramp volume quickly while keeping the same rubric and acceptance criteria across multiple languages.
We needed domain-aware review for complex technical text, not generic labeling. Abaka staffed specialized reviewers and set up adjudication so disagreements were resolved systematically. The resulting dataset performed far better in evaluation than our internal baseline.
Abaka was straightforward about ownership and provenance—our data stayed ours, with strong controls and auditability. The team was responsive to change requests and kept the project on schedule even as we refined labels and introduced new categories mid-stream.
Abaka combines scholar-grade reviewers, multi-layer QA, and Abaka Forge workflows so your labels stay consistent as volume grows. You get versioned guidelines, calibration rounds, adjudication, and audit trails—backed by SOC 2 and ISO 27001-aligned controls. We are self-funded and profitable, and we never build models that compete with you. Your data remains exclusively yours—never repurposed, resold, or shared.
Quality is managed with calibration, gold sets, sampling, and adjudication—not hope. We design acceptance gates that match your schema, whether it’s NER spans, intents, or policy labels.
Annotate across 50+ countries with locale-specific QA. You keep one taxonomy and one delivery standard while handling dialects, translations, and region-specific terminology.
SOC 2, ISO 27001, GDPR, and CCPA-aligned operations with strict NDAs and segregated secure pipelines. Designed for sensitive text like contracts, support logs, and regulated workflows.
Use Abaka Forge to standardize task routing, QA, and exports across teams and modalities. Large-model automation accelerates repetitive steps so humans focus on the hard judgment calls.
We never reuse your data. Your datasets are yours—full IP provenance and 0% copyright risk on collected data. No VC, no acquisition pressure, and no incentive to resell your work to someone else.
Label the Present. Train the Future.