RLHF Data Services
for reliable alignment at production scale

Abaka delivers preference data, rubric-based evaluations, and RLHF workflows across text and multimodal tasks—backed by SOC 2, ISO 27001, GDPR, and CCPA controls, strict NDAs, and full IP provenance.

Talk to an Expert

When RLHF pipelines stall, your model roadmap stalls with them. Teams commonly discover that only a fraction of collected comparisons are actually usable after QA—meaning you pay twice: once to generate prompts and runs, and again to re-label the same tasks. A 2–3 week slip in alignment or safety readiness can cascade into missed launch windows, delayed enterprise trials, and weeks of extra engineering on post-hoc fixes.

The cost of inaction shows up fast: inconsistent rubrics can create preference noise that weakens reward model learning, while under-specified guidelines inflate reviewer disagreement and slow throughput. If you’re shipping assistants, agents, or coding copilots, even a small reduction in human preference consistency can translate into more refusals, more hallucinations, and more escalations—driving higher support costs and slower adoption in production.

The RLHF Data Services Bottleneck in AI Development

Quality Decay

RLHF quality decays when evaluation criteria are vague, when annotators lack domain context, or when “helpful” conflicts with “safe” in edge cases. Preference data is especially sensitive: small inconsistencies across raters can introduce label noise that reward models amplify. Abaka mitigates this with rubric-first task design, calibrated rater onboarding, multi-layer QA, and scholar-network reviewers for harder domains (coding, math, medicine, law). You get auditable decision trails and clearer signal for your reward model and policy tuning.

Volume Walls

Alignment work hits volume walls when your team tries to scale comparisons, rankings, and multi-turn evaluations internally. Even with good tooling, throughput quickly becomes the limiting factor—especially for long-context conversations and code tasks that require careful execution and verification. Abaka operates with 1M+ vertically specialized annotators across 50+ countries and enforces a 500 files/day per annotator max throughput policy to maintain quality while scaling output to match your training cadence.

Compliance Friction

RLHF data often includes sensitive prompts, proprietary policies, and product behavior targets. Without strong controls, you risk IP leakage, unclear provenance, and governance gaps that slow enterprise procurement. Abaka is SOC 2 and ISO 27001 compliant and supports GDPR and CCPA requirements, with strict NDAs, segregated secure pipelines, and full IP provenance—so your preference data remains exclusively yours and is never repurposed, resold, or shared.

Pairwise Preference & Ranking Data (RLHF core)

Collect high-signal pairwise comparisons, listwise rankings, and rubric-scored outputs for reward model training and policy optimization. Abaka supports single-turn and multi-turn conversations, long-context tasks, and structured comparisons (e.g., accuracy vs. verbosity vs. safety). We deliver clear schemas for accepted/rejected samples, rater rationale fields, and disagreement metadata—ready for typical RLHF training pipelines. Workflows run in Abaka Forge and export clean JSONL/CSV for downstream ingestion.

Rubric Design, Calibration, and Rater Training

Turn subjective “good response” discussions into measurable criteria. We help your team define pass/fail safety gates, scoring dimensions, and tie-break rules, then operationalize them into annotation guidelines and training quizzes. Calibrations are run iteratively using gold sets and adjudication feedback loops, so your rubric behaves consistently across regions and languages. This is particularly valuable for agent behaviors (tool use), coding assistants, and regulated domain assistants where policy adherence must be explicit and repeatable.

Conversation-Level RLHF (Multi-turn, Long Context)

Evaluate multi-turn dialogues with memory, persona constraints, and instruction-following requirements. Abaka supports per-turn labels (helpfulness, refusal correctness, policy adherence) plus conversation-level outcomes (task completion, user satisfaction). We can collect reviewer notes on where the model deviated, enabling targeted data curation. Deliverables include turn-indexed JSON, conversation transcripts, and decision rationales suitable for supervised fine-tuning (SFT), preference modeling, and regression testing.

Agent & Tool-Calling Feedback (Function Calling, Browsing, RPA)

If your model calls tools—APIs, retrieval, or actions—RLHF must evaluate not just text but correctness of tool selection, arguments, and execution flow. Abaka designs tasks that score tool choice, parameter validity, and final answer groundedness. We label tool traces, intermediate reasoning artifacts you choose to expose, and final outputs, producing structured datasets your team can use to train tool-competent agents. Abaka Forge supports workflow routing so specialized reviewers handle higher-risk tool paths.

Coding RLHF with Execution-Aware Review

Preference data for coding requires more than style judgments—it needs compile/run checks, test-driven evaluation, and security-minded reviews. Abaka’s scholar-network coverage includes coding specialists who can assess correctness, complexity, and defensive coding behavior. We deliver comparisons (A vs. B), rubric scores, and error categorization (logic bug, dependency issue, unsafe pattern) with exports in JSONL. Pricing can be structured on an hourly basis for deep reviews (e.g., LLM Math/Coding $18/hr) depending on the complexity of tasks.

Math/Reasoning RLHF (Verifier-Aware, Step Validation)

For math and reasoning, high-quality human feedback often requires step-by-step verification and error localization, not just final-answer scoring. Abaka provides specialists across mathematics and reasoning domains (including Lean4 familiarity where relevant) to compare reasoning traces, spot hallucinated steps, and enforce consistent standards (show work vs. concise). Deliverables can include per-step correctness labels, preference pairs, and rationale fields so you can train reward models that learn to prefer valid reasoning over fluent but wrong explanations.

Multimodal RLHF (Image, Video, Interleaved)

As models become multimodal, RLHF must cover visual understanding, grounding, and instruction-following across image+text and video+text. Abaka can run preference tasks for caption quality, spatial reasoning, safety constraints (e.g., image-based sensitive content), and multimodal tool use. We support interleaved image conversations and video spatial reasoning with consistent rubrics, reviewer calibration, and structured outputs. Exports include JSON/JSONL with media references and bounding/rationale metadata where needed.

Abaka Forge Workflows (QA, Automation, and Exports)

Abaka Forge is an all-in-one platform for collection, cleaning, annotation, training, and production workflows across text, image, video, 3D/4D point cloud, and RLHF. Large-model automation accelerates parts of the pipeline—often delivering up to 50× speedups—while humans handle judgment calls and QA. You can manage task routing, gold sets, adjudication, and audit logs, then export to your preferred formats (JSONL, CSV, Parquet) for training and evaluation.

Why Outsource RLHF Data Services

Faster Delivery

RLHF is a throughput game: you need consistent, high-signal feedback at the pace your training runs iterate. Outsourcing to Abaka lets you ramp quickly without hiring, onboarding, and re-organizing your internal team. With a global workforce and structured QA, you can move from rubric definition to production-scale labeling in weeks, not quarters—keeping reward model experiments, policy tuning, and regression evaluations aligned with engineering timelines.

Direct Savings

Internal RLHF teams are expensive to scale because the work mixes project management, guideline iteration, reviewer calibration, and specialized domain review. Abaka converts that into a predictable operating model—so your engineers focus on model improvements instead of rater operations. Pricing can map to task complexity (hourly for deep expert review; structured units for repeatable workflows) and avoids the overhead of building a full compliance, tooling, and workforce management layer in-house.

Risk Reduction

Alignment data touches product behavior, safety posture, and brand trust—so errors carry outsized risk. Abaka reduces risk through SOC 2 and ISO 27001 controls, GDPR/CCPA support, strict NDAs, segregated pipelines, and full IP provenance. You also reduce quality risk with calibrated rubrics, gold sets, adjudication, and multi-layer QA—helping ensure your reward model learns stable preferences and your assistant behavior remains consistent across updates.

Elastic Scalability

RLHF demand is spiky: you may need a surge of comparisons for a new capability, then shift to targeted safety evaluations after a release. Abaka provides elastic capacity through 1M+ specialized annotators across 50+ countries, enabling you to scale up or down while maintaining consistent guidelines and QA. This keeps your monthly spend aligned with training cycles and avoids the whiplash of hiring and layoffs or contractor churn.

Domain Expertise

Generic raters struggle with coding, math, medicine, law, and other expert domains where correctness is not subjective. Abaka’s scholar-network domains include Automobile, Coding, Languages, Mathematics, Medicine, Science, Business, and Law—so you can route tasks to reviewers who understand what “correct” means. That results in cleaner reward signals, fewer false positives in safety labeling, and preference data that better matches real user expectations.

Innovation Velocity

Teams that treat RLHF as a one-off labeling project fall behind; the leaders operationalize RLHF as a continuous capability. Abaka helps you iterate on rubric design, prompt generation strategies, and new evaluation dimensions (tool use, multimodal grounding, refusal correctness) without rebuilding the process each time. With Abaka Forge workflows and automation-assisted pipelines, you can trial new tasks quickly, measure rater agreement, and roll the winners into production data streams.

Industries We Serve

Automotive

Automotive AI teams use RLHF to improve driver-assistance assistants, in-cabin copilots, and perception-adjacent reasoning systems that explain decisions to engineers and regulators. Abaka supports domain-specific guidelines (vehicle controls, safety boundaries, map context), multilingual in-cabin use cases, and tool-calling flows (vehicle APIs, diagnostics). When needed, we pair RLHF feedback with complementary annotation such as autonomous driving lanes, enabling a unified data partner across conversational and perception work.

GenAI / Foundation Models

Frontier model labs and enterprise LLM teams rely on RLHF data services to shape instruction-following, reduce hallucinations, and improve refusal behavior. Abaka provides scalable preference comparisons, rubric scoring, and human evaluations aligned to your policy definitions. We also support multimodal RLHF (image/video) and agent/tool-use feedback so your foundation model becomes more helpful while staying within your safety and brand constraints.

Embodied AI / Robotics

Robotics and embodied agent teams need feedback on plans, action sequences, and task success—often beyond pure text. Abaka can evaluate agent trajectories, tool traces, and stepwise instructions using rubrics that reflect real-world constraints (safety, efficiency, completion). Where appropriate, Abaka can complement RLHF with custom RL environment design to create training loops that connect human preference signals to simulated or scripted tasks.

Healthcare

Healthcare-adjacent assistants (patient support, clinical operations, medical coding helpers) require careful policy adherence and domain-aware correctness checks. Abaka supports rubric-first RLHF that separates factual correctness from bedside tone and safety constraints, with specialized reviewers from medicine and science domains. While we do not claim HIPAA, we do support SOC 2/ISO 27001, GDPR/CCPA-aligned workflows, strict NDAs, and segregated pipelines to help you operate responsibly.

Retail

Retail copilots and customer support assistants benefit from RLHF that optimizes helpfulness, brand voice, and resolution rates across channels. Abaka can evaluate multi-turn customer journeys (returns, shipping issues, product comparisons), label escalation correctness, and measure compliance with scripted policies. We support multilingual evaluation and can structure outputs so your team can train reward models to prefer responses that are accurate, concise, and aligned to business rules.

Finance

Financial assistants and analyst copilots need RLHF that is strict about uncertainty, disclaimers, and policy constraints—without over-refusing. Abaka builds rubrics that encode what the assistant may and may not do, then collects preference data that rewards compliant, grounded responses. With law/business domain coverage plus compliance controls (SOC 2, ISO 27001, GDPR/CCPA support), you can scale evaluations for customer support, research summarization, and internal knowledge assistants.

Geospatial

Geospatial applications—mapping assistants, satellite analytics copilots, and field-report summarizers—need RLHF that rewards grounded descriptions, correct units, and careful uncertainty handling. Abaka supports multimodal evaluation (image+text, video+text) and structured rubrics for spatial reasoning, report quality, and safety constraints. Outputs can be delivered in JSONL/CSV with location references, allowing your team to train reward models that prefer correct, verifiable interpretations over plausible but wrong narratives.

Security / Defense

Security teams use RLHF to improve safe behavior, reduce risky instructions, and harden assistants against policy-violating outputs. Abaka can run controlled evaluation programs with strict access controls, NDA enforcement, and segregated pipelines. We support red-team style data generation and preference labeling to help your model learn safer defaults, better refusal correctness, and more reliable tool-use decisions—without exposing sensitive internal content to uncontrolled workflows.

Agriculture / Industrial

Industrial assistants (maintenance copilots, troubleshooting guides, SOP helpers) often fail on edge cases, ambiguity, and tool workflows. Abaka’s RLHF data services can evaluate step-by-step procedures, safety warnings, and completion criteria using domain-aware rubrics. We can also support multimodal inputs—photos, short videos, sensor logs summarized into text—so your model learns to ask clarifying questions, follow constraints, and produce actionable instructions that operators trust.

How It Works

1) Day 0–3 — Scope, Rubrics, and Data Contract

We start by aligning on what your model should optimize: helpfulness, honesty, harmlessness, policy adherence, tool-use correctness, or domain accuracy. Your team shares representative prompts, policies, and failure examples; we map them to a rubric and task types (pairwise comparisons, rankings, scalar scores, multi-turn conversation grading). We also confirm security requirements (SOC 2/ISO 27001 controls, access boundaries, NDAs), output formats (JSONL/CSV/Parquet), and sampling plans so delivery is predictable.

2) Week 1–2 — Pilot, Calibration, and Gold Sets

We run a pilot batch to validate rubric clarity and rater agreement. This includes calibrations, reviewer training, and gold set creation (known-good examples) so we can measure drift. Your team reviews early outputs, flags ambiguous cases, and we refine instructions and tie-break rules. If you need domain routing (coding, math, medicine, law), we add qualification gates. The goal is to stabilize quality before scaling volume and to ensure the reward signal is consistent.

3) Week 2–3 — Production Ramp in Abaka Forge

After pilot sign-off, we ramp labeling in Abaka Forge with production QA: multi-layer review, adjudication queues, and routine spot checks. Workflows can include model-assisted pre-labeling to accelerate throughput while humans make the final judgment. We deliver rolling exports on a cadence your training team can consume—often daily or multiple times per week—so reward model training and policy tuning don’t wait for “end of project” handoffs.

4) Ongoing — Continuous Improvement and Drift Control

RLHF is continuous: once your model improves, the data distribution changes and your rubric must keep up. Abaka runs ongoing monitoring using gold set refreshes, disagreement analysis, and guideline updates. We can help your team expand coverage to new capabilities (tool calling, longer context, multimodal) and new locales (languages and regions). You keep ownership of the data and can adjust sampling strategies to focus budget on the highest-impact failure modes.

5) Weekly — Reporting, QA Metrics, and Iteration Planning

Each week, we share a concise operational report: volumes delivered, QA findings, common ambiguity clusters, and recommended rubric improvements. If you track model deltas, we can align RLHF batches to specific experiments (e.g., reward model update vs. policy fine-tune). We also plan next-week targets—new task templates, expanded domains, or safety categories—so your alignment work remains proactive rather than reactive.

Modality & Format Coverage

RLHF doesn’t stop at text. Abaka supports preference data and human evaluation across modalities so your team can align multimodal assistants, tool-using agents, and specialized domain copilots using consistent rubrics and exportable schemas. Workflows run in Abaka Forge and deliver structured outputs you can plug into training, evaluation, and continuous regression pipelines.

Modality	Annotation Types	Tools	Output Formats
Text	Pairwise preference (A/B), listwise ranking, scalar scoring, factuality checks, policy adherence labels, rationale fields	Abaka Forge	JSONL, CSV, Parquet; turn-indexed conversation JSON; prompt/response bundles
LLM RLHF	Preference comparisons, reward-model training sets, refusal correctness, instruction-following grading, safety boundary checks, model-as-judge review (where appropriate)	Abaka Forge	JSONL (chosen/rejected), CSV (scores), Parquet; adjudication logs; gold set packs
Image	Image+text response preference, grounding checks, dense captioning preference, safety category review, interleaved image conversation evaluation	Abaka Forge	JSON/JSONL with media URIs; CSV exports; optional COCO-style JSON for supporting labels
Video	Video spatial reasoning preference, temporal grounding, instruction-following across frames, safety review, summarization quality ranking	Abaka Forge	JSONL with timestamps; CSV score sheets; segment-indexed annotations
3D/4D Point Cloud	Preference on scene descriptions, instruction-following evaluation for 3D reasoning, quality grading of 3D outputs (where applicable)	Abaka Forge	JSON/JSONL metadata; CSV scoring; project-specific schemas for 3D references
LiDAR + Camera fusion	Cross-modal consistency checks, preference on fused explanations, error categorization for perception-to-language outputs	Abaka Forge	JSONL with sensor references; CSV evaluation matrices; audit-friendly QA logs
Audio	ASR transcript preference, TTS naturalness evaluation, safety review for spoken assistants, multilingual quality grading	Abaka Forge	JSONL with timecodes; CSV scores; WAV/MP3 manifests plus transcript bundles

Success Story

A leading enterprise AI team aligning a customer-facing assistant

Challenge

A fast-growing enterprise AI team was preparing to roll out a customer-facing assistant into production. Their internal RLHF process was struggling with inconsistent preferences across reviewers, unclear tie-break rules for safety vs. helpfulness, and slow turnaround for multi-turn conversations. The team also needed a workflow that would stand up to procurement scrutiny: NDA enforcement, access boundaries, and clear data ownership. They couldn’t afford weeks of rework because each delay impacted downstream fine-tuning schedules and planned release dates.

Approach

Abaka redesigned the RLHF workflow around rubric clarity and measurable QA. We collaborated on a dimension-based rubric (helpfulness, factuality, policy adherence, refusal correctness, and tone), then ran a pilot with calibration rounds and gold sets to stabilize reviewer agreement. Tasks were routed by difficulty, with specialized reviewers handling edge cases and multi-turn conversations. Production labeling ran in Abaka Forge with adjudication queues and multi-layer QA. Deliveries were exported in JSONL (chosen/rejected pairs) and CSV (scores and metadata) on a rolling cadence so the customer could train reward models continuously and run weekly regression checks against the latest model build.

Results

Within weeks, the team had a repeatable RLHF engine instead of ad-hoc labeling. Preference data became more consistent, engineering time spent on rater management dropped, and the assistant’s behavior was easier to steer because the rubric encoded explicit tradeoffs. The customer also gained a governance-ready workflow: strict NDAs, segregated pipelines, and full IP provenance, with data kept exclusively for the customer and never repurposed. The outcome was faster iteration on alignment and a clearer path from “model failure reports” to targeted RLHF tasks. - **3** rubric dimensions added as explicit pass/fail gates - **2** delivery cadences supported (daily exports + weekly QA review) - **1** unified RLHF schema adopted across single-turn and multi-turn tasks

2–3 weeks

from rubric to production ramp

Rolling

deliveries for continuous training

SOC 2

controls supported for governance

By the Numbers

1M+

vertically specialized annotators available on demand

50+

countries supporting multilingual RLHF coverage

99%

accuracy target supported with multi-layer QA

500

max throughput per annotator to protect quality

What Customers Say

We needed preference data that actually matched our policies, not generic “good answer” scoring. The rubric-first process made disagreements actionable, and the weekly QA review helped us tighten guidelines without slowing delivery.

**Head of Applied AI** Enterprise Software Company Type

Our biggest pain was multi-turn evaluation consistency. The calibration rounds and adjudication flow reduced ambiguity, and the exports were structured so our training team could immediately plug the data into reward model runs.

**ML Platform Lead** Foundation Model Lab Company Type

Security reviews can derail data projects. The NDA posture, segregated pipelines, and clear data ownership terms made procurement straightforward, and we didn’t have to compromise on access boundaries for sensitive prompts.

**Director of Security Engineering** Fintech Company Type

We were surprised by how much time we saved once we stopped managing raters ourselves. The team could focus on experiments and failure analysis while Abaka ran the operations and kept quality stable as volumes changed week to week.

**Product Manager, GenAI** Consumer App Company Type

Why Choose Abaka

Trustworthy data partner for frontier AI—without conflicts

Abaka is a trustworthy data partner for frontier AI—founded in 2019, self-funded and profitable, with offices in Singapore, Paris, and Silicon Valley and 1,000+ enterprise and research customers. A key differentiator for RLHF: we never build models that compete with you. Your data is exclusively yours—never repurposed, resold, or shared—so your alignment targets, safety policies, and product behavior remain private and protected.

Compliance-ready operations for sensitive RLHF

RLHF data can reveal your product strategy and safety posture. Abaka supports SOC 2 and ISO 27001 compliance and aligns workflows to GDPR and CCPA requirements, with strict NDAs, segregated secure pipelines, and full IP provenance. That reduces procurement friction and helps you operationalize RLHF in environments with higher governance standards—without turning your internal ML team into a compliance operations group.

Scholar-grade reviewers for hard domains

When correctness matters, generalists aren’t enough. Abaka routes tasks to domain-capable reviewers across scholar-network domains including coding, mathematics, medicine, law, business, science, and languages. For RLHF, this means fewer “confident but wrong” preferences, more reliable rubrics, and better reward signals—especially for coding copilots, technical assistants, and reasoning-heavy benchmarks where accuracy must be evaluated, not guessed.

Abaka Forge—workflow control from pilot to production

Abaka Forge supports end-to-end workflows: collection, cleaning, annotation, training, and production across text, image, video, 3D/4D point cloud, and RLHF. You get structured routing, gold sets, adjudication, and audit logs, plus exportable schemas for your training stack. Large-model automation can accelerate parts of the pipeline—often up to 50×—while human reviewers preserve judgment quality where it matters most.

Quality systems built for preference data (not just labels)

Preference data requires controlling disagreement, drift, and ambiguity. Abaka emphasizes rubric clarity, calibration rounds, and multi-layer QA—supported by gold sets and adjudication—so you can trust the signal going into reward models. We also enforce a 500 files/day per annotator maximum throughput policy to avoid speed-driven quality collapse, helping maintain consistent scoring even as you scale volume.

Global scale with predictable delivery and ownership

Abaka operates with 1M+ specialized annotators across 50+ countries, allowing you to scale multilingual RLHF quickly while keeping guidelines consistent. You can ramp capacity for new releases, shift focus to targeted safety areas, or pause and resume without rebuilding the team. Throughout, your organization retains clear ownership and provenance: your RLHF datasets are created for you and stay exclusively yours, supporting long-term iteration and defensible governance.

Frequently Asked Questions

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How much do RLHF data services cost?

Pricing depends on task complexity and reviewer specialization. For deep technical RLHF, Abaka offers LLM Math/Coding support at $18/hr, while STEM generalists are $12/hr and dense captioning is $6/hr for multimodal workflows. We’ll quote after a short scoping call based on rubric depth, volume targets, and QA requirements so you can forecast spend accurately.

How fast can you start delivering RLHF preference data?

Most teams can begin with a pilot in Week 1–2 after rubric and task templates are finalized. Production ramp commonly follows in Week 2–3 once calibration and gold sets are approved. If you already have guidelines and schemas, we can often compress timelines by reusing your formats and focusing the pilot on agreement and QA validation.

What formats do you deliver for RLHF datasets (chosen/rejected, scores, etc.)?

We commonly deliver JSONL for chosen/rejected pairs, plus CSV or Parquet for scalar scores, metadata, and QA fields. For multi-turn RLHF, we provide turn-indexed conversation JSON with clear IDs, timestamps, and rater rationales when requested. If you have an existing schema, we can adapt exports to match your training and evaluation pipeline.

How do you ensure RLHF label accuracy and consistency across raters?

We use rubric-first task design, rater onboarding with calibration rounds, gold sets, and adjudication for disagreements. Multi-layer QA catches drift and ambiguity early, and we refine tie-break rules when “helpful” conflicts with “safe” or “correct.” We also cap throughput at 500 files/day per annotator to avoid speed-driven quality degradation on complex tasks.

Can you support secure RLHF for sensitive prompts and proprietary policies?

Yes. Abaka supports SOC 2 and ISO 27001 compliance, GDPR and CCPA-aligned workflows, strict NDAs, and segregated secure pipelines. We maintain full IP provenance and do not repurpose, resell, or share your data. We’ll align access controls, redaction rules, and export procedures to your security and procurement requirements during onboarding.

Do you provide multilingual RLHF data services?

Yes. We support multilingual and locale-specific RLHF through a workforce spanning 50+ countries. We can run language-specific rubrics, evaluate cultural tone and politeness expectations, and ensure policy adherence remains consistent across locales. If you need parallel prompts or localized test sets, we can structure tasks to keep comparisons fair and comparable across languages.

How are you different from other RLHF data labeling companies?

Abaka combines compliance controls (SOC 2, ISO 27001), global scale, and domain-specialist reviewers with an all-in-one platform (Abaka Forge) for RLHF workflows. Importantly, we never build models that compete with you—your data remains exclusively yours and is never repurposed. That reduces IP risk and aligns incentives around your long-term model performance.

What if we need rubric changes or new categories mid-project?

Change requests are expected in RLHF. We handle updates through controlled versioning: we revise guidelines, re-calibrate raters on representative examples, and optionally refresh gold sets so quality doesn’t drift. We can also branch task templates (v1 vs. v2) to keep your training data traceable, enabling clean ablations when you compare reward model performance.

Can we run a small pilot before committing to a larger RLHF program?

Yes. A pilot is the recommended starting point to validate rubric clarity, rater agreement, and export compatibility. We’ll propose a limited batch sized to cover your main use cases and edge cases, then review outputs with your team. After sign-off, we scale production with the same workflow and QA controls to preserve consistency.

Who owns the RLHF dataset you produce for us?

You do. Abaka does not repurpose, resell, or share your data. We also maintain full IP provenance and operate under strict NDAs and segregated pipelines so your prompts, policies, and preference labels remain protected. Ownership and usage rights are confirmed in the data contract during onboarding to support enterprise governance requirements.

What tools or platforms do you use to manage RLHF labeling workflows?

We run RLHF workflows in Abaka Forge, our all-in-one platform supporting collection, cleaning, annotation, training, and production across text and multimodal data. Forge supports task routing, gold sets, adjudication, and audit logs, and exports to common formats like JSONL, CSV, and Parquet. If you have internal tools, we can align schemas and delivery cadence.

Is there a minimum project size for RLHF data services?

There isn’t a one-size minimum; it depends on whether you need a pilot, ongoing weekly deliveries, or a short burst for a specific release. We can start with a tightly scoped pilot to prove quality and schema fit, then expand capacity as your training cadence increases. Share your target volume and timelines, and we’ll recommend a right-sized plan.

Ready to Get Started?

If you’re building an assistant, agent, or multimodal model, RLHF is where product behavior becomes measurable. Abaka helps your team stand up a rubric-driven RLHF pipeline with scalable preference data, secure operations, and exportable schemas your training stack can use immediately. Talk to an Expert at business@abaka.ai — Human Intelligence — Data for Frontier AI