How much do training data generation companies cost?
Cost depends on modality, difficulty, and QA depth, but Abaka can price transparently using known unit rates. Examples include LLM Math/Coding at $18/hr, STEM Generalist at $12/hr, Dense Captioning at $6/hr, and Road Lane annotation at $3/km. For evaluation programs, Red Teaming can be $8/eval and Math Capabilities $12/eval. We’ll scope your ontology, volume, and acceptance criteria, then propose a pilot budget with clear unit assumptions and QA coverage.
How long does it take to get a first dataset delivered?
Most teams start with a pilot that ships in 2–3 weeks, depending on complexity, modality mix, and how quickly guidelines are finalized. The timeline typically includes calibration, gold set creation, reviewer training, and a first QA pass. If you already have a stable ontology and examples, we can move faster; if the task is novel (e.g., complex reasoning rubrics or multi-sensor fusion), we’ll invest more time up front so production doesn’t degrade later.
What data modalities and output formats can you deliver?
Abaka supports text, RLHF, image, video, 3D/4D point clouds, LiDAR + camera fusion, and audio—managed in Abaka Forge. Outputs are delivered in common training-ready formats such as JSONL, CSV, Parquet, COCO-style JSON, PNG masks, frame-indexed manifests, and sensor sequence sidecars. We’ll match the export schema to your ingestion pipeline and provide version tags and change logs so batches remain comparable across weekly releases.
How do you ensure label accuracy and consistency at scale?
We combine guideline design, calibration rounds, and multi-layer QA to keep labeling stable as volume grows. Abaka monitors disagreement, runs targeted adjudication, and uses specialist reviewers for domains like coding, math, medicine, and law. We also enforce operational controls—such as capping per-annotator throughput (often around 500 files/day maximum) on tasks where speed otherwise causes quality decay. You receive QA summaries with each delivery to make quality measurable, not assumed.
Can you support secure or sensitive data workflows?
Yes. Abaka operates with strict NDAs, segregated secure pipelines, and governance aligned to SOC 2 and ISO 27001 practices, plus GDPR and CCPA considerations where applicable. Access can be role-based, audited, and limited to approved personnel. We also provide full IP provenance for collected data and do not repurpose or resell your datasets. If your security team has additional requirements, we can incorporate them into the pilot plan before production begins.
Do you support multilingual training data generation?
Yes—Abaka operates across 50+ countries and can build multilingual datasets for instruction tuning, translation review, speech transcription, and localized evaluations. We align dialect and locale requirements up front (e.g., region-specific terminology) and use reviewer calibration to keep consistency across languages. Deliveries include language tags, sampling plans for long-tail locales, and QA signals so you can monitor drift or bias as you scale to new markets and new content types.
How is Abaka different from other training data generation companies?
Abaka is built to be a long-term data partner, not a one-off labeling shop. You get end-to-end coverage (collection, annotation, RLHF, evaluation) inside Abaka Forge, plus a clear ownership commitment: we never build models that compete with you and your data is never repurposed. Operationally, we focus on repeatable weekly delivery, schema governance, and multi-layer QA so your model progress is driven by real improvements—not vendor-induced noise.
What if we need changes after the project starts?
Change requests are expected—especially as model failures reveal new edge cases. We handle updates through controlled schema evolution: versioned guidelines, targeted backfills, and clearly separated dataset releases. For larger changes (new ontology classes, new modalities, or new rubrics), we’ll propose a short recalibration phase to prevent label drift. You’ll receive change logs and impact notes so your team can decide whether to retrain, fine-tune, or run A/B comparisons.
Can we run a pilot before committing to a long-term contract?
Yes. Most teams begin with a pilot designed to prove three things: quality against acceptance criteria, operational throughput, and clean integration with your pipeline. The pilot typically runs 2–3 weeks and includes calibration, a first delivery, QA reporting, and a feedback loop to tighten guidelines. If the pilot meets targets, we scale into a weekly delivery cadence using the same schemas and governance so production is a smooth continuation—not a reset.
Who owns the data and the outputs we pay for?
You do. Abaka’s position is explicit: your data is exclusively yours—never repurposed, resold, or shared. We also maintain full IP provenance for collected data, aiming for 0% copyright risk on collection workflows, and we can document handling procedures for your internal review. If you provide source data, we treat it as your confidential information under strict NDAs and segregated secure processing.
What tools and platforms do you use for production delivery?
We use Abaka Forge, our all-in-one platform for collection, cleaning, annotation, and production workflows across text, RLHF, image, video, and 3D/4D point clouds. Forge supports role-based access, audit trails, dataset versioning, and automation to accelerate repetitive steps. If you have internal tooling requirements, we can map Forge exports to your schemas and integrate delivery into your storage and pipeline conventions.
What is the minimum dataset size you can support?
We support small gold sets and large production programs. A practical minimum is often a pilot sized to validate guidelines and QA—enough items to measure disagreement and edge-case coverage across classes and languages. For some tasks, that might be a few thousand items; for others (like RLHF or complex evaluations), it may be smaller but more specialized. We’ll recommend a minimum that produces statistically meaningful QA signals and a clear go/no-go decision.
