Abaka curates, cleans, and structures multi-modal data with multi-layer QA, secure pipelines, and fast throughput—so your team trains and evaluates on trustworthy inputs, not noise.
When you delay data curation, model performance plateaus for the wrong reasons: duplicates inflate training steps, label drift contaminates evaluation, and edge cases get silently dropped. Teams often lose 30–50% of iteration time to re-cleaning and re-splitting after experiments fail to reproduce. The downstream cost shows up as weeks of retraining, rising GPU burn, and product regressions that are hard to diagnose because the dataset—not the model—kept changing under the hood.
Abaka helps you hire a dedicated data curation workflow without the overhead of building and managing it internally. Using Abaka Forge plus human intelligence, we standardize schemas, de-duplicate at scale, filter and stratify by policy, and produce versioned datasets with audit trails. You get curated outputs ready for training, RLHF, and evaluation, delivered through SOC 2 and ISO 27001-aligned processes with GDPR/CCPA controls—so your team can iterate confidently and ship faster.
Curation quality decays when rules live in tribal knowledge instead of explicit specs. A single untracked guideline change can shift class balance by 10–20% and make offline metrics look “better” while real-world performance drops. Abaka turns curation into a governed process: written rubrics, calibration sets, multi-layer QA, and versioned deliveries so every filter, transform, and exclusion is reproducible—and your evaluation set stays stable across weeks of iteration.
Raw data arrives faster than internal teams can normalize it. Even at strong individual throughput, humans must cap work to maintain accuracy; a common ceiling is ~500 files/day per annotator, which becomes a hard limit during surge periods. Abaka scales curation with elastic staffing, automation in Abaka Forge, and standardized schemas—so you can process large backlogs without sacrificing review depth, stratified sampling, or edge-case coverage.
Curation often becomes the slowest step once security and provenance enter the picture. NDAs, access control, and audit requirements can add 2–6 weeks if you rely on ad-hoc tools or consumer storage. Abaka operates with SOC 2 and ISO 27001 controls, GDPR/CCPA alignment, segregated secure pipelines, and full IP provenance—so you can curate sensitive data without blocking engineering or risking rework after a compliance review.
We ingest from your storage, exports, or capture pipelines and build an auditable inventory of files, metadata, and constraints. Abaka Forge supports text, images, video, audio, and 3D/point clouds, with role-based access and segregated secure pipelines. Your team gets a clear map of what exists, what’s usable, and what needs remediation—critical for regulated verticals like finance, healthcare, and security programs.
Abaka standardizes formats and removes common failure modes: duplicates, corrupt files, low-signal samples, and inconsistent encodings. We design deterministic cleaning steps so the same input always yields the same output—supporting reproducible training and evaluation. Outputs are delivered as versioned drops with manifests so your ML pipeline can track what changed, when, and why across experiments.
We help you define the dataset schema that matches your objective: instruction following, safety, tool use, spatial reasoning, or perception. That includes required fields, constraints, sampling rules, and split strategy (train/val/test + challenge sets). For GenAI and robotics teams, we align schemas to the way your trainers, reward models, and evaluators actually consume data—reducing conversion work later.
When curation requires labeling, we add vertically specialized annotators and scholar-network reviewers across domains like mathematics, coding, medicine, law, and languages. Work is calibrated to target quality (up to 99% accuracy when specified) with multi-layer QA and adjudication. Formats include classification, entity spans, dense captioning, visual QA, and structured tabular fields—managed end-to-end in Abaka Forge.
We curate prompts and responses for alignment workflows: preference pairs, rubric-scored outputs, refusal policy checks, and bias/safety audits. Abaka teams create balanced prompt sets, remove leakage and duplicates, and enforce consistent grading standards. This produces cleaner signals for reward modeling and evaluation, especially when you need stable datasets across iterative policy changes.
Abaka curates multilingual corpora with locale-specific conventions, quality gates, and script normalization. We support language coverage across 50+ countries and can apply per-locale policy rules (tone, safety constraints, culturally appropriate examples). Deliverables include language-tagged splits and metadata for domain and difficulty, making it easier to train and evaluate multilingual assistants without hidden distribution shifts.
Every curated dataset includes provenance and a changelog: sources, filters applied, exclusion reasons, and reviewer sign-offs. Abaka provides strict NDAs, segregated pipelines, and controls aligned with SOC 2, ISO 27001, GDPR, and CCPA. We also maintain full IP provenance with 0% copyright risk on collected data—so your team can ship with confidence and pass procurement reviews faster.
We deliver curated datasets in the formats your stack expects—JSONL, CSV/TSV, Parquet, COCO-style JSON, or task-specific schemas—along with manifests and sampling notes. For perception teams, we package sequences, timestamps, and sensor metadata; for GenAI teams, we package conversations, tool traces, and rubric scores. Your engineers get deterministic drops that plug into training and evaluation pipelines without manual reformatting.
Instead of recruiting, onboarding, and building curation SOPs from scratch, you start with a ready operating model. Abaka can stand up a scoped pilot quickly and deliver curated, versioned datasets in weeks—not quarters—so your experiments stop waiting on “data readiness” tickets.
Outsourcing reduces the hidden costs of rework: repeated cleaning, inconsistent splits, and manual conversions across teams. With Abaka Forge automation and standardized QA, you reduce operational overhead and keep engineering focused on model improvements rather than dataset firefighting.
Curation touches sensitive data, evaluation sets, and policy constraints—mistakes are expensive. Abaka provides SOC 2 and ISO 27001-aligned pipelines, GDPR/CCPA controls, strict NDAs, and full provenance so you reduce security, compliance, and IP risk while maintaining auditability.
Your needs change: a new model release, a safety incident, a new geography, or a perception edge-case sprint. Abaka scales capacity up or down without forcing you to over-hire. You get steady throughput with predictable QA while keeping internal headcount stable.
High-quality curation often requires domain knowledge—math, medicine, law, coding, or multilingual nuance. Abaka brings vertically specialized annotators and scholar-grade reviewers so your datasets reflect expert judgments, not generic heuristics that fail in hard cases.
Abaka teams help you evolve from ad-hoc cleaning to a governed dataset lifecycle: specs, calibration, drift checks, and versioning. That means faster iteration on new tasks (RLHF, tool use, multimodal reasoning) without repeatedly rebuilding your data process each time requirements shift.
Curate perception datasets for lanes, signage, rare events, and long-tail conditions. Abaka structures sequences, timestamps, and metadata so training and evaluation splits stay consistent across releases—especially useful when you need reliable regression testing and stratified sampling by weather, geography, or scenario.
Build model-ready corpora for instruction following, reasoning, tool use, and safety. Abaka curates high-signal data, removes duplicates/leakage, and packages JSONL conversations with rubric fields so your pretraining, SFT, RLHF, and eval pipelines consume consistent inputs.
Curate multimodal logs (video, depth/point cloud, sensor metadata) and standardize task definitions for agent learning. Abaka helps you create stable training sets and challenge sets for manipulation, navigation, and HRI—so agent improvements reflect learning, not shifting data.
Curate sensitive text and imaging datasets with strict access controls, audit trails, and policy-driven filtering. Abaka focuses on governance—versioning, reviewer sign-offs, and provenance—so you can train clinical NLP or imaging models with clear constraints and reproducible evaluation.
Curate product catalogs, search logs, and customer support data into clean, structured datasets. Abaka normalizes attributes, removes duplicates, and builds balanced splits across categories and locales—improving ranking, recommendation, and assistant quality without noisy training signals.
Curate datasets for document understanding, risk workflows, and customer communications with compliance-aware rules. Abaka enforces consistent redaction policies, keeps audit trails, and produces versioned datasets suitable for model evaluation and monitoring—so changes in policy don’t break comparability.
Curate satellite, aerial, and map-related datasets by aligning metadata, timestamps, tiles, and labels into consistent schemas. Abaka supports dataset stratification by region and conditions and delivers manifests that make it easier to run reproducible experiments across changing sources.
Curate mission-relevant data in segregated secure pipelines with strict NDAs and access control. Abaka emphasizes provenance, auditability, and controlled transformations so sensitive datasets remain governed while still being usable for training, evaluation, and red-team style testing.
Curate field imagery, sensor streams, and operational logs into structured datasets for detection, forecasting, and quality control. Abaka normalizes modalities, removes low-signal samples, and builds scenario-balanced splits (seasonality, lighting, equipment types) to improve robustness.
We define the curation target (training, RLHF, evaluation, or all three), success metrics, and acceptance tests. Then we set up secure access, NDAs, and workflow roles in Abaka Forge. You get a written curation spec: inclusion/exclusion rules, schema fields, and delivery format—so everyone aligns before the first file is processed.
Abaka runs a pilot batch to validate rules, edge cases, and reviewer agreement. We tune cleaning steps (normalization, de-duplication, filtering) and establish QA gates with adjudication. You review sample outputs and we lock the rubric, schema, and split strategy so the full run is reproducible and consistent.
We scale throughput with elastic staffing and automation while maintaining quality targets. Deliverables include curated data, manifests, and a changelog that documents transforms and exclusions. Outputs are packaged for your stack (e.g., JSONL/Parquet/COCO-style JSON) so your team can start training or evaluation immediately.
As your product and policy evolve, we run controlled updates: new sources, updated filters, new label policies, or new challenge sets. We preserve dataset lineage and versioning so metrics remain comparable. Your team can request targeted refreshes (new locales, new edge cases, new tools) without rebuilding the pipeline.
Each week you receive throughput and quality reporting plus a dataset health review: distribution shifts, common failure modes, and recommended additions to challenge sets. We turn observations into actionable next steps—new sampling rules, improved rubrics, or targeted data collection—so your curation program continuously strengthens model performance.
Data curation is rarely one modality. Abaka supports end-to-end curation across text, RLHF, vision, audio, and 3D—packaged into the formats your training and evaluation pipelines expect, with versioning and audit trails.
| Modality | Annotation Types | Tools | Output Formats |
|---|---|---|---|
| Text | Deduplication & filtering rules, taxonomy normalization, entity and span checks, structured field validation | Abaka Forge | JSONL conversations, CSV/TSV tables, Parquet datasets, UTF-8 normalized text |
| LLM RLHF | Preference pairs, rubric scoring, safety/refusal policy checks, tool-use trace validation | Abaka Forge | JSONL (prompt/response), pairwise preference JSON, rubric-scored records, eval-ready bundles |
| Image | Dataset filtering & stratification, classification checks, bounding boxes, dense captioning | Abaka Forge | COCO-style JSON, JSONL + image paths, CSV manifests, zipped delivery with checksums |
| Video | Clip selection, temporal segments, keyframe extraction specs, scenario tagging and QA sampling | Abaka Forge | MP4 + JSON manifests, frame-level JSONL, timestamped CSV, dataset index files |
| 3D/4D Point Cloud | Point cloud filtering, frame alignment checks, object tagging, sequence integrity validation | Abaka Forge | PCD/PLY + JSON metadata, sequence manifests, Parquet indices, timestamped exports |
| LiDAR + Camera fusion | Sensor sync validation, calibration metadata checks, fused frame sampling, cross-modal consistency QA | Abaka Forge | Synchronized bundles + manifests, JSON metadata, CSV indices, checksum-verified drops |
| Audio | Audio quality filtering, language/locale tagging, transcript QC, sensitive content policy screening | Abaka Forge | WAV/MP3 + JSONL transcripts, CSV segment tables, metadata manifests, train/val/test splits |
The team had enough raw data volume, but model gains were inconsistent. Training runs were slowed by duplicates, noisy sources, and shifting dataset splits across experiments. Evaluation results were hard to trust because the test set included leaked examples and poorly controlled distributions. Internal engineers spent cycles writing one-off cleaning scripts and manually triaging edge cases, delaying iteration on instruction following and safety behaviors.
Abaka set up a governed data curation workflow in Abaka Forge: a written curation spec, deterministic cleaning and de-duplication steps, and versioned dataset releases with manifests and changelogs. We introduced multi-layer QA with calibrated reviewers and an adjudication loop for ambiguous cases. The team received consistent train/val/test splits plus a challenge set designed around their failure modes (refusals, hallucination triggers, and tool-use formatting).
Within 3 weeks, the customer moved from ad-hoc cleaning to a repeatable dataset lifecycle: curated drops on a schedule, stable evaluation sets, and faster experiment turnaround. Duplicate and low-signal content was removed, guideline drift was controlled via change requests, and new data sources were onboarded without breaking lineage. The team reported improved reproducibility and smoother releases, with curated deliveries supporting 2–3x faster iteration cycles and up to 99% QA-target accuracy on curated subsets.
We didn’t need more data—we needed the right data. Abaka helped us turn a messy corpus into versioned, reproducible dataset drops with clear manifests and change logs. Our offline evaluations finally became comparable week to week, and our engineers stopped spending nights rebuilding cleaning scripts after every policy tweak.
The biggest win was governance. Abaka’s workflow made inclusion rules explicit, added calibrated review, and created a controlled process for change requests. That reduced disagreement across teams and gave us confidence that model regressions were real, not artifacts of a shifting test set.
Security and provenance mattered for us. The segregated pipelines, NDA process, and auditability removed procurement friction, and we could finally curate sensitive datasets without slowing down the roadmap. Deliveries were clean, consistent, and ready for training the same day.
Abaka’s team handled the unglamorous work—normalization, de-duplication, and edge-case triage—while we focused on modeling. The weekly reporting and dataset health checks made it easy to prioritize new data sources and challenge sets, which improved robustness in the scenarios we cared about.
Abaka combines Abaka Forge with human intelligence to deliver curated datasets you can trust: explicit specs, calibrated reviewers, multi-layer QA, and versioned outputs with audit trails. You get secure, compliant execution (SOC 2, ISO 27001, GDPR, CCPA) and full provenance—plus a partner that never builds models that compete with you. Your data remains exclusively yours—never repurposed, resold, or shared.
Move from backlog to production with structured pilots, rapid calibration, and repeatable workflows. Abaka scales throughput while keeping quality gates intact, so you can ship curated dataset versions on a predictable cadence.
Use large-model automation inside Abaka Forge to accelerate routine steps like triage, formatting, and policy screening. Humans stay focused on judgment calls and edge cases—where quality matters most.
SOC 2 and ISO 27001-aligned controls, strict NDAs, segregated pipelines, and GDPR/CCPA alignment make it easier to curate sensitive datasets. You get auditability and lineage that stand up to procurement and security review.
Bring in expertise when you need it—math, coding, languages, medicine, law, and more. Abaka’s specialized networks help you curate higher-signal data and reduce subtle errors that generic teams miss.
Abaka is self-funded and profitable, and we never build models that compete with you. Your curated datasets are exclusively yours—never repurposed, resold, or shared. That governance posture, combined with full IP provenance, reduces risk for frontier model teams and enterprise deployments alike.
Label the Present. Train the Future.
