AI Data Collection Services
for real-world model training at scale

Collect, curate, timestamp, and tag custom text, image, video, LiDAR, and IoT sensor data—delivered through secure pipelines with strict NDAs and full IP provenance.

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When your team can’t get the right data on time, your roadmap turns into a queue. Internal collection efforts often spend weeks just aligning capture specs, permissions, and storage—while model experiments stall and stakeholders lose confidence. In fast-moving programs, a single missed data window can push a milestone by 2–4 weeks and force expensive re-runs of training and evaluation.

The cost of inaction compounds: teams routinely spend the majority of their time on acquisition and preprocessing rather than learning cycles. Even a 10–20% drop in data relevance can translate into months of iteration debt across labeling, training, and evaluation. Abaka helps you move from “data scavenging” to a repeatable collection pipeline—so your team ships features instead of chasing files.

The AI Data Collection Services Bottleneck in AI Development

Quality Decay

Data quality decays the moment capture assumptions drift—different devices, changing environments, inconsistent metadata, or subtle bias in what gets recorded. A model trained on stale distributions can underperform even if you have “enough” samples. High-quality collection means enforcing capture protocols (sensor calibration, lighting/scene constraints, prompt templates, consent and usage rights) and producing consistent timestamps, tags, and traceable lineage. Without that rigor, you can lose weeks to debugging mislabeled domains rather than improving the model.

Volume Walls

Collection is a throughput problem: even a well-designed plan can hit volume walls when teams rely on ad hoc contractors, fragmented vendors, or manual uploads. Data arrives late, in mixed formats, and with missing fields that slow downstream annotation and training. Abaka supports on-demand custom capture pods and curated delivery so you can scale collection without breaking ingestion. The goal is steady flow—not spikes—so your training runs stay scheduled and compute is utilized efficiently.

Compliance Friction

Modern AI programs face compliance friction at every step: NDAs, data minimization, jurisdictional constraints, and proof of provenance. If rights and documentation aren’t handled upfront, your legal review can pause launches for weeks. Abaka operates with SOC 2 and ISO 27001-aligned practices plus GDPR and CCPA support, using segregated secure pipelines and strict NDAs. You get full IP provenance and 0% copyright risk on collected data—so you can ship with confidence.

On-demand custom capture pods for real-world data

Collect purpose-built datasets with on-demand capture pods across environments your model actually faces—streets, warehouses, stores, clinics, factories, or farms. We align to your spec: device lists (RGB cameras, depth sensors, LiDAR, IMUs), sampling rates, scene diversity targets, and safety constraints. Deliverables arrive curated, timestamped, and tagged so your team can immediately route data into annotation and training. This is ideal for robotics navigation, driver assistance, retail shelf intelligence, and industrial inspection where “internet data” doesn’t match deployment reality.

Multimodal sourcing: text, image, video, LiDAR, IoT sensor data

Abaka supports collection across text, image, video, LiDAR, and IoT sensor streams, enabling multimodal training and evaluation. You can define capture recipes that bind modalities together (e.g., video + IMU + GPS + LiDAR) and enforce consistent metadata schemas. We deliver in structured formats your engineering team can ingest—JSON sidecars, CSV manifests, and media files organized by sequence and timestamp—so you can train perception, forecasting, and agent policies with consistent alignment.

Pre-filtered curation, de-duplication, and dataset hygiene

Collection isn’t just gathering; it’s curation. Abaka pre-filters and curates data to remove corrupted files, duplicates, and out-of-spec samples before they reach your labeling or training pipeline. By reducing noise early, teams avoid paying twice—once in manual cleanup and again in retraining. Abaka’s data collection workflows are designed to reduce preprocessing time by up to 70%, helping you reach a “trainable” dataset faster and keeping your MLOps pipeline stable.

Timestamping, tagging, and schema design for reliable training

High-performing models depend on high-integrity metadata: timestamps, sensor IDs, capture locations (when appropriate), environment tags, and scenario labels. Abaka helps you define a metadata contract and validates it at ingestion so downstream tools can rely on consistent fields. This is critical for time-series learning, autonomy stacks, predictive maintenance, and evaluation sets where you must slice performance by scenario. We can also deliver structured prompt/response schemas for text collection, including instruction templates and safety/coverage tags.

Full IP provenance and 0% copyright risk on collected data

Your team can’t afford unclear rights. Abaka provides full IP provenance on collected datasets with strict NDAs and secure handling. We never build models that compete with you, and your data is exclusively yours—never repurposed, resold, or shared. This is especially important for foundation model teams, enterprise copilots, and regulated workflows where provenance, auditability, and contractual certainty determine whether data can be used for training and production.

Segregated secure pipelines for sensitive programs

Abaka runs segregated secure pipelines designed for enterprise and research labs that need controlled access, clean separation between projects, and predictable governance. With SOC 2 and ISO 27001-aligned security practices plus GDPR and CCPA support, we help you build a collection process that your security and legal teams can approve. We can match your storage requirements, define access roles, and provide documentation for internal review—reducing the friction that stalls programs late in the cycle.

Abaka Forge integration from collection to cleaning and labeling

For teams that want one pipeline from capture to training readiness, Abaka Forge provides an all-in-one workflow that supports collection, cleaning, annotation, and production delivery across data types. Large-model automation can accelerate routine steps, and your team can standardize output formats for MLOps ingestion. This reduces handoffs between vendors, lowers integration overhead, and makes it easier to iterate on specs as your model learns—without restarting the entire collection process.

Program management: specs, QA gates, and delivery cadence

Data collection programs fail when ownership is unclear. Abaka provides operational structure: capture specs, acceptance criteria, QA gates, and delivery cadence (daily, weekly, or milestone-based). We define what “done” means—field completeness, file integrity, metadata validation, and sampling diversity—so you can predict downstream labeling and training schedules. This is especially valuable for multi-site capture across countries or teams where inconsistent practices otherwise introduce hidden variance into your dataset.

Why Outsource AI Data Collection Services

Faster Delivery

Outsourcing compresses the time between a research question and a trainable dataset. Instead of staffing, training, and coordinating ad hoc collectors, you get a defined pipeline with capture pods, standardized metadata, and repeatable QA gates. That means your team can plan experiments with real dates and deliverables, not “when we finally have enough data.” Faster delivery also reduces wasted compute—training runs don’t sit idle while teams hunt for missing files or rights documentation.

Direct Savings

In-house collection looks cheap until you count coordination, travel, device procurement, inconsistent uploads, and downstream cleanup. Outsourcing shifts collection into a predictable scope and removes hidden costs like re-collection due to missing metadata or invalid permissions. Because Abaka curates and pre-filters, you reduce the time your engineers spend on data wrangling and can reallocate budget to model iteration. Teams also avoid paying twice for the same work—first for capture, then again for reprocessing.

Risk Reduction

Collection risk includes rights ambiguity, privacy exposure, and unusable data due to unclear specs. Abaka is built for provenance, documentation, and secure handling, with strict NDAs and segregated pipelines. You reduce the chance that a dataset becomes unusable after legal review or that sensitive assets leak via informal sharing. Risk reduction also means operational resilience: if a capture plan needs to change, you can update specs and continue delivery without collapsing the entire pipeline.

Elastic Scalability

Model development rarely needs the same volume every week. Outsourcing lets you scale collection up for a launch window and down after you hit coverage targets—without carrying a permanent internal team. Abaka’s on-demand capture pods and global operations help you respond to new failure modes (new geographies, new lighting conditions, new product SKUs) quickly. Elastic scalability keeps you aligned with real-world needs while maintaining stable delivery and ingestion.

Domain Expertise

Domain-specific collection is not a generic task. Robotics needs synchronized sensors and careful scene coverage; retail needs consistent planograms and SKU diversity; healthcare workflows need controlled capture and documentation. Abaka supports specialized programs with well-defined specs and data hygiene so you get usable training data, not a pile of media. We also connect collection to downstream labeling and evaluation plans so your dataset is designed to improve measurable model outcomes.

Innovation Velocity

When you’re not stuck building one-off collection processes, you can move faster on what matters—model architecture, evaluation, deployment, and product integration. Abaka provides a repeatable collection engine that your team can reuse as new products and modalities emerge. That accelerates iteration: new scenarios can be captured, curated, and delivered on a predictable cadence, allowing rapid hypothesis testing and faster closure on failure cases discovered in production.

Industries We Serve

Automotive

Automotive teams need high-coverage, real-world data across weather, lighting, and road types. Abaka supports collection for perception and planning workflows, including multi-sensor capture (video, LiDAR, GPS/IMU) and scenario tagging for downstream annotation. Whether you’re improving driver assistance or building robust simulation inputs, we help you design capture routes, define acceptance criteria, and deliver timestamped sequences that can be immediately used in training and evaluation.

GenAI / Foundation Models

Foundation model teams often need custom data that matches their target user interactions—domain-specific text, instruction-style dialogues, and multimodal examples. Abaka’s collection pipelines emphasize provenance and safety so training data can survive internal review. You can specify coverage by topic, difficulty, and style, and receive structured outputs with consistent metadata. This is useful for enterprise copilots, research benchmarks, and domain-tuned models where generic web data is insufficient.

Embodied AI / Robotics

Embodied systems need data that reflects physical constraints: camera motion, occlusion, depth, and time-series sensor fusion. Abaka can collect synchronized data streams (video + depth + IMU + LiDAR where needed) and tag tasks, environments, and success conditions to support policy learning and evaluation. We also help teams build repeatable capture protocols so new tasks can be collected consistently as the robot’s capabilities expand.

Healthcare

Healthcare-adjacent AI workflows require careful governance, documentation, and controlled capture. Abaka supports secure, segregated pipelines and strict NDAs to help your team collect the data you are authorized to use, with clear provenance and metadata. Typical use-cases include imaging workflows, patient-facing assistant content that must follow policy, and operational analytics. The focus is on auditability and consistent formatting for downstream QA and model evaluation.

Retail

Retail AI depends on reality: shelves change, packaging evolves, and store conditions vary. Abaka supports image and video collection for shelf availability, planogram compliance, and product recognition across stores and regions. We can define capture instructions (angles, distances, lighting), enforce metadata completeness, and deliver curated datasets ready for labeling. This reduces false positives and improves robustness when models face crowded aisles, reflections, and frequent SKU updates.

Finance

Finance teams often need high-precision text and document data for assistants, compliance workflows, and analytics. Abaka can collect domain-specific corpora with consistent formatting, structured fields, and provenance suitable for internal review. Programs commonly require strict access controls and careful separation between projects; Abaka’s segregated pipelines and NDA-first operations support that. The result is data that can be used for training and evaluation without uncertainty about usage rights.

Geospatial

Geospatial AI benefits from consistent capture, metadata, and alignment across time and location. Abaka supports collection and curation for satellite/overhead imagery workflows, ground truth capture, and sensor fusion datasets used in mapping and monitoring. We help define schemas for timestamps, coordinates (when appropriate), and scene tags so your team can slice model performance by terrain, seasonality, and scenario—improving reliability in real deployments.

Security / Defense

Security-sensitive programs require disciplined data handling and clear provenance. Abaka operates with SOC 2 and ISO 27001-aligned security practices, strict NDAs, and segregated pipelines. We support collection of multimodal data for detection, monitoring, and decision support, with controlled access and documentation suitable for audit. Importantly, Abaka never builds models that compete with you—your data remains exclusively yours and is not reused.

Agriculture / Industrial

Industrial and agriculture environments are variable: dust, motion blur, changing weather, and complex machinery. Abaka supports collection of image/video and sensor data to train models for inspection, yield estimation, anomaly detection, and predictive maintenance. We design capture protocols to ensure diversity across conditions and deliver curated, tagged datasets that reduce preprocessing and accelerate your path to usable training data in production-like settings.

How It Works

1) Day 0–3 — Scope, specs, and provenance plan

We start by translating your model goal into a collection spec: modalities, environments, target edge cases, and required metadata. You share acceptance criteria (e.g., minimum resolution, sampling rates, or scenario coverage), plus constraints around jurisdictions, privacy, and storage. We define a provenance and documentation approach so rights and usage are clear from the start. By Day 3, you have a concrete plan: capture protocol, delivery formats, QA gates, and a proposed cadence.

2) Week 1–2 — Capture setup and pilot collection

In Week 1–2, Abaka configures capture pods, validates devices, and runs a pilot to prove the spec works in real conditions. The pilot is intentionally small but representative—enough to test data integrity, metadata completeness, and ingestion into your pipeline. We also confirm labeling readiness if you plan to annotate next. This stage reduces re-collection risk and surfaces issues early (missing tags, inconsistent timestamps, or hard-to-capture scenarios) before scaling volume.

3) Week 2–3 — Scale collection, curation, and delivery

Once the pilot passes, we scale collection according to the agreed cadence and coverage plan. Data is pre-filtered, curated, timestamped, and tagged before delivery so your team receives trainable assets rather than raw dumps. We package outputs into consistent folder structures and manifests (e.g., JSON/CSV) to simplify ingestion. If you use Abaka Forge, we can connect collection to cleaning and annotation workflows to reduce handoffs and speed iteration.

4) Ongoing — Edge-case expansion and continuous improvement

As your model trains and evaluation reveals failure modes, we adapt the capture plan to target new edge cases. This might include new environments, device changes, scenario rebalancing, or additional metadata fields. Because the process is standardized, changes don’t require starting over—they become controlled revisions to the spec with updated QA checks. Ongoing collection keeps your dataset aligned to production reality and reduces performance regressions caused by distribution shift.

5) Weekly — Governance, reporting, and QA checkpoints

Every week, we run structured checkpoints with your team: delivery counts, QA findings, schema validation, and upcoming coverage needs. You get visibility into what was collected, what was filtered out, and what remains to hit targets. Weekly governance prevents silent drift and ensures the dataset remains internally defensible—especially when legal, security, or product stakeholders require documentation. The outcome is a predictable pipeline that your MLOps schedule can rely on.

Modality & Format Coverage

AI data collection rarely stays in a single modality. Abaka supports multimodal capture and delivery with consistent metadata so your team can train, evaluate, and iterate without rebuilding ingestion. Below is a practical view of common annotation types (when you choose to label), the tools used to manage workflows, and the formats we deliver for training and MLOps integration.

Modality	Annotation Types	Tools	Output Formats
Text	Instruction/response collection, domain corpora sourcing, classification tags, PII redaction cues (as required), metadata schemas for topic/difficulty/style	Abaka Forge	JSONL, JSON, CSV, UTF-8 TXT; manifest files; dataset cards (project documentation)
LLM RLHF	Preference data collection, rubric-guided ratings, tool/function-call traces, safety and policy coverage tags, conversation-level metadata	Abaka Forge	JSONL/Parquet-ready structures, conversation manifests, function-call logs, evaluator rubric exports
Image	Capture protocols + tagging (scene, lighting, device), optional bounding boxes, polygons, keypoints, dense captions for downstream training	Abaka Forge	JPG/PNG/WebP, COCO-style JSON, CSV manifests, sidecar JSON metadata
Video	Sequence capture + timestamps, scenario tags, optional tracking/segmentation plans for downstream labeling, clip-level metadata	Abaka Forge	MP4/MOV, frame manifests, JSON/CSV timestamps, clip indexes and metadata sidecars
3D/4D Point Cloud	Point cloud capture specs, sensor calibration records, sequence metadata, optional 3D boxes/segmentation planning for labeling	Abaka Forge	PCD/LAS/LAZ (as needed), sequence manifests, calibration files, timestamped metadata JSON
LiDAR + Camera fusion	Synchronized capture pipelines, time alignment, calibration + extrinsics tracking, scenario tagging for perception/planning datasets	Abaka Forge	Time-synced sequences, calibration bundles, JSON/CSV alignment manifests, media + point cloud package structure
Audio	Speech collection protocols, speaker/environment tags, optional transcription plan, multilingual capture requirements	Abaka Forge	WAV/FLAC/MP3, JSON/CSV manifests, timestamp markers, optional transcript-ready exports

Success Story

A leading embodied AI team building a real-world navigation and manipulation system

Challenge

The team’s models performed well in lab settings but struggled in deployment-like environments where lighting, motion blur, and clutter were common. Their internal collection process produced inconsistent metadata, and sensor streams arrived out of sync, making it difficult to build reliable training sequences. Legal and security reviewers also required clearer provenance documentation before new data could be used in training.

Approach

Abaka worked with the team to define a capture spec that prioritized the failure modes seen in evaluation: fast camera motion, occlusions, reflective surfaces, and tight indoor spaces. We deployed on-demand capture pods to gather synchronized video and sensor data with consistent timestamps and a standardized metadata schema. Data was pre-filtered to remove corrupted or out-of-spec files and delivered in a repeatable package structure with manifests to simplify ingestion. Throughout the program, weekly checkpoints tracked coverage, flagged gaps, and updated capture targets as new model errors were discovered. The team also adopted a provenance-first documentation approach so every delivered asset could be reviewed and approved quickly for training use.

Results

With a steady cadence of curated, deployment-like data, the team reduced time spent on wrangling and increased iteration speed between evaluation and retraining. The synchronized sequences made it easier to diagnose model failures by scenario and to add targeted captures when new edge cases appeared. Most importantly, the dataset became operationally dependable: training jobs could be scheduled against predictable deliveries, and governance stakeholders had the documentation they needed without slowing the roadmap. - **70% less preprocessing time** by receiving curated, trainable deliveries aligned to the spec. - **2–3 week collection ramp** from scope to scaled capture and repeatable weekly drops. - **0% copyright risk** on collected data with full IP provenance and strict NDAs.

70%

preprocessing time reduction target

2–3 weeks

from scope to scaled collection

By the Numbers

2019

Founded—trustworthy data partner for frontier AI

1,000+

Enterprise and research customers served

70%

Preprocessing time reduction with curated collection workflows

What Customers Say

We needed a collection partner who could translate model failures into a concrete capture plan. Abaka delivered consistent metadata, predictable weekly drops, and documentation our reviewers could approve without weeks of back-and-forth. That reliability let our team focus on training and evaluation instead of chasing missing fields and mismatched formats.

Director of Applied ML Enterprise Robotics Company

Our internal teams could collect some data, but the overhead of coordination and cleanup was slowing us down. Abaka’s curated deliveries reduced the amount of preprocessing we had to do and made ingestion repeatable. The project felt managed end-to-end, with clear QA gates and fast adjustments when we changed requirements.

Head of Data Operations Foundation Model Lab

Provenance and governance were non-negotiable for us. Abaka’s secure workflows, strict NDAs, and clear documentation helped us get data approved for training without last-minute legal surprises. We also appreciated that Abaka does not build competing models—our data stayed exclusive to our program.

Security & Compliance Lead Financial Services AI Team

What stood out was operational consistency. We weren’t just handed raw files; we got organized packages with manifests and tags that matched our schema, making downstream labeling and evaluation much smoother. Weekly check-ins kept the collection aligned to what our model actually needed as edge cases emerged.

ML Engineering Manager Retail Computer Vision Company

Why Choose Abaka

Provenance-first AI data collection you can defend

Abaka is designed for teams who need more than “some data.” You get full IP provenance and 0% copyright risk on collected datasets, backed by strict NDAs and secure handling. That means fewer late-stage surprises during legal and product reviews, and more confidence that the data you train on can ship. Your data is exclusively yours—never repurposed, resold, or shared.

Secure, segregated pipelines for sensitive programs

Security and governance are built into how we operate. Abaka supports SOC 2 and ISO 27001-aligned practices and works with GDPR and CCPA requirements. We keep projects separated through segregated secure pipelines, making it easier for your organization to approve collection workflows and maintain strict access controls across teams, vendors, and geographies.

Real-world capture across modalities—built for deployment

We collect data that matches production conditions: text, image, video, LiDAR, and IoT sensor streams, including synchronized multi-sensor capture when needed. Abaka’s on-demand capture pods allow you to target the scenarios your model fails on—lighting shifts, occlusions, motion blur, clutter, and rare events—so performance improves where it matters: in the field.

Curated delivery that reduces preprocessing overhead

Raw collection is expensive if your team must clean it before it becomes usable. Abaka pre-filters and curates data so corrupted files, duplicates, and out-of-spec samples are removed early. With workflows that can reduce preprocessing time by up to 70%, you move faster from capture to training—without burning engineering time on wrangling and dataset hygiene.

Abaka Forge to unify collection, cleaning, and annotation

If you want to reduce vendor fragmentation, Abaka Forge provides a single workflow spanning collection, cleaning, annotation, and production delivery. It supports all major data types (image, video, text, RLHF, 3D/4D point cloud) and can accelerate routine steps with large-model automation. Your team gets consistent outputs, fewer handoffs, and faster iteration when specs change.

A stable, non-competing partner built for long-term programs

Abaka is self-funded and profitable, founded in 2019, with offices in Singapore, Paris, and Silicon Valley, supporting 1,000+ enterprise and research customers. We never build models that compete with you—so there’s no conflict of interest and no pressure to reuse your data elsewhere. The result is a trustworthy data partner for frontier AI programs that need continuity, discretion, and dependable delivery.

Frequently Asked Questions

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How much do AI data collection services cost?

Pricing depends on modality, geography, capture complexity, and how much curation you want included. If your program includes labeled components, reference rates include Road Lane at $3/km and Dense Captioning at $6/hr. For platform-managed workflows, Abaka Forge uses credits at $0.20 USD each. We scope a pilot first to set accurate unit economics.

How fast can you start a custom data collection project?

Most teams can kick off quickly once scope, modalities, and governance requirements are defined. A common timeline is Day 0–3 for specs and acceptance criteria, Week 1–2 for a pilot capture, and Week 2–3 to scale delivery. If your security review is complex, we align documentation early to avoid schedule surprises.

What data types and formats can you deliver?

We support text, image, video, audio, LiDAR, and IoT sensor streams, including synchronized multi-sensor sequences. Deliveries typically include media files plus structured manifests (JSON/JSONL/CSV) and metadata sidecars for timestamps, capture context, and tags. If you plan to annotate next, we can also align outputs to formats commonly used in training pipelines.

How do you ensure the collected data is accurate and usable for training?

We start with a clear capture spec and acceptance criteria, then validate through a pilot before scaling. Data is curated and pre-filtered to remove corrupted or out-of-spec samples, and metadata is checked for completeness and schema consistency. Weekly QA checkpoints keep collection aligned to coverage targets so you get trainable data, not just raw files.

Can you support secure or sensitive data collection programs?

Yes. Abaka supports SOC 2 and ISO 27001-aligned security practices, strict NDAs, and segregated secure pipelines. We also support GDPR and CCPA requirements. Access controls, storage requirements, and delivery processes can be tailored to your governance needs so security and legal stakeholders can approve the workflow without blocking iteration.

Do you collect multilingual data?

Yes. Abaka operates globally and can collect multilingual text and audio aligned to your target locales, dialects, and domains. We define language coverage and metadata requirements upfront so your team can slice performance by language and region. If the project includes speech, we can capture audio under controlled environment conditions and provide structured manifests for downstream processing.

How are you different from other data collection vendors?

Abaka is built for frontier AI programs that need provenance, governance, and reliable delivery. You get full IP provenance and 0% copyright risk on collected data, plus secure, segregated pipelines. We also never build models that compete with you—your data is exclusively yours. Finally, curated delivery reduces preprocessing overhead, accelerating your path to training.

What if we need to change requirements mid-project?

Change is expected as models reveal new failure modes. We manage updates through versioned specs, updated acceptance criteria, and controlled QA checks so changes don’t disrupt the pipeline. Weekly checkpoints help prioritize new edge cases and adjust capture plans while preserving consistency. This keeps your dataset coherent across iterations and reduces the risk of re-collection.

Can we run a pilot before committing to a larger collection?

Yes—pilots are the fastest way to validate capture specs, metadata schemas, and ingestion into your pipeline. A pilot typically happens in Week 1–2 and is designed to surface issues early: missing tags, inconsistent timestamps, or hard-to-capture scenarios. Once the pilot passes acceptance, we scale collection with a predictable cadence.

Who owns the data you collect for us?

You do. Abaka does not repurpose, resell, or share your collected data. We never build models that compete with you, and we operate under strict NDAs with secure handling. Deliverables include documentation and provenance so your organization can confidently use the data for training, evaluation, and production without ambiguity about ownership.

Can you work with our existing tools and MLOps pipeline?

Yes. We can deliver data in structured formats (e.g., JSON/CSV manifests plus media) that fit common ingestion patterns, and we can align metadata fields to your internal schemas. If you want a unified workflow, Abaka Forge supports collection through to cleaning and annotation across modalities. We’ll confirm integration requirements during scoping.

Is there a minimum project size for AI data collection services?

There’s no one-size minimum, but collection is most efficient when scoped around a clear model goal and acceptance criteria. Many teams start with a pilot to validate feasibility and unit economics, then scale to meet coverage targets. If your need is small, we can still propose a focused capture plan designed to produce measurable evaluation gains.

Ready to Get Started?

If your team is blocked by sourcing, governance, or inconsistent delivery, Abaka can stand up a repeatable collection pipeline—curated, timestamped, and tagged for training readiness. Talk to an Expert at business@abaka.ai. Human Intelligence — Data for Frontier AI