Training Intelligence Infrastructure
InfoBay engineers the expert-verified training data, annotation infrastructure, and evaluation systems that make AI accurate, factual, and production-ready — grounded in 3.5M+ hours of multilingual audio across 45+ languages, 2.29M+ patient medical records, academic textbooks in 15 languages, and 12K+ DSA codebases.
Arabic · Bahasa · Swahili · French · Nepali · Urdu · Bengali · Portuguese · Bahasa · Java · Python · C++
English · Arabic · French · Swahili · Hindi · Nepali · 45+ languages
12 languages · natural long-form speech
ISBN-attributed · STEM + Non-STEM · 5K+ subjects
99M+ medical Images & Files · MRI · CT · X-ray
Client portfolio
InfoBay's meticulously structured datasets helped us reduce hallucination rates and improve reasoning accuracy across STEM benchmarks.
The Problem
Every hallucination, reasoning failure, and misaligned output in production traces to a training data problem — not model architecture. The solution is earlier and more structural than most teams realize.
Confident, fluent, wrong. Models trained without factuality controls fail in ways that are expensive and invisible until deployment in high-stakes regulated environments.
SFT data without explicit reasoning traces patterns responses rather than teaching reasoning. The model sounds correct without being correct.
RLHF built on commodity crowdwork optimizes for apparent preference, not correct behavior. The model learns to sound good, not to be right.
The Solution
InfoBay is the training intelligence layer — combining a proprietary multilingual corpus, verified domain expertise, structured annotation methodology, and rigorous evaluation design to give AI teams systematic control over what their models know, how they reason, and where they fail.
Every engagement begins with a quality baseline. Every deliverable is measured against it — in benchmark deltas, factuality improvement percentages, and evaluation pass rates.
ISBN-attributed textbooks · 15 languages · factuality-scored · not web-scraped.
Instruction datasets with explicit reasoning traces by domain SMEs.
2.1M+ hours real industry audio · dual-channel · industry-tagged · WER-tracked.
53M+ DICOM images · 1.6M+ patient records · 20 specialties · age-stratified.
12K+ DSA problems · 9 languages · 25M tokens · curated, not scraped.
Systematic identification of hallucination-prone examples. Documented before and after.
Corpus
Twelve proprietary corpus collections - audio, video, textbooks, healthcare, Q&A, coding, image, egocentric, longitudinal, thesis, legal, and operational data. Expert-curated, categorized, and available for enterprise AI training.
Audio corpus
Call center, podcast, and speech intelligence datasets with premium metadata for gender, age, industry, channel, dialect, and language.
View Audio dataset pageEnglish · Arabic · French · Swahili · Hindi · Kinyarwanda · Luganda · Nepali · Bengali · and 45+ more
Key USP — Dual Channel
Agent and Customer on separate tracks. Gold standard for speaker diarization training. No public dataset matches this at scale.
Natural long-form conversational speech across global and regional languages. Covers Arabic, South Asian, and Southeast Asian languages. Ideal for audio-LLM cross-modal alignment where organic multilingual discourse matters.
Dataset USPs
Benchmarked against leading providers. Every USP grounded in the actual dataset files.
USP 01
Live call center conversations capture disfluencies, interruptions, code-switching, and domain vocabulary used organically. Models trained on real conversations generalize far better to production than prompted audio.
USP 02
Perfect diarization ground truth without post-hoc separation. Unlocks contact center AI, agent assist, compliance monitoring. Available for English (US/UK/IN), French, Arabic, Nepali, Bengali, Swahili, and more.
USP 03
Banking, healthcare, insurance, agriculture, telecom, QSR, humanitarian aid, SaaS, real estate, solar energy, travel. Industry-specific vocabulary inherent in the data — not annotated after the fact.
USP 04
African languages: Swahili (146.3K hrs), Kinyarwanda (3.3K hrs), Luganda (21.6K hrs), Ganda, Chichewa. South Asian: Nepali (235K hrs), Bengali (377K hrs), Odia (12.7K hrs). Southeast Asian: Bahasa Indonesia, Javanese. All with enterprise-grade quality annotation and dual-channel where available.
USP 05
Real-world multilingual environments naturally blend languages within conversations — Arabic-French in North Africa, Bahasa-English across Southeast Asia, English-local language in enterprise call centers globally. InfoBay's corpus captures this cross-lingual mixing organically at scale, enabling voice AI models that handle real-world multilingual conversation without brittle hard language boundaries.
USP 06
Collected via formal agreements with enterprise operators. EU AI Act Article 10, India DPDP Act, and CCPA require documented provenance. InfoBay's collection architecture is built for this from the ground up.
Capabilities
Pre-training through evaluation — one vendor, one quality standard, zero handoffs.
ISBN-attributed textbooks · 15 languages · deduplicated · not web-scraped. FineWeb-Edu achieved +24% ARC from educational text — ours comes from actual published books.
Instruction datasets with explicit reasoning traces. Built by domain SMEs — attorneys, physicians, researchers, engineers — not crowdworkers. Chain-of-thought citations included.
3.5M+ hours of real industry audio · dual-channel · 45+ languages · WER-tracked. Speaker-disjoint splits. The signal that makes reward models calibrate correctly.
99M+ DICOM images and clinical files · 2.29M+ patient records · age-stratified disease taxonomy · 20 specialties · real Indian hospital data.
12K+ DSA codebases · 200+ legacy codebases · ~1.36B+ tokens · cross-language parallel structure. Same problem in Java, Python, C++, JavaScript, and C# — curated, not scraped.
Audio · Video · Healthcare · Textbooks · Q&A · Coding · Image · Egocentric · Longitudinal · Thesis · Legal · Operational. Twelve proprietary corpus collections with reviewable metadata. Expert-curated. Enterprise-licensed. Training-ready.
Why InfoBay
InfoBay's 3.5M+ audio hours, 2.29M+ patient records, 42K+ textbook library, and 64K+ coding solutions accumulated over years of production engagements. Every project adds to a retained, quality-scored asset for future model work. When you engage InfoBay, you start from millions of verified examples in your domain — not from zero.
Corpus proof: audio, healthcare, textbooks, coding, Q&A, image, video, egocentric, longitudinal, thesis, legal, and operational data with reviewable metadata.
Every textbook carries its ISBN. Every audio hour carries its language code, industry vertical, channel type. Every medical record carries its modality, specialty, and clinical fields. Every code problem carries its language and algorithmic category. Traceability is not a compliance feature — it is the mechanism that makes training data trustworthy at the root.
EU AI Act Article 10: High-risk AI must document training data sources. InfoBay's corpus is built for this.
We measure success in benchmark deltas, factuality improvement percentages, and evaluation pass rates. FineWeb-Edu demonstrated educational text improves ARC by 24% and MMLU by 12% over web text. Our textbooks achieve that quality without filtering. Our audio provides real-world grounding no open dataset matches. Our medical data is the only Indian hospital corpus with paired DICOM+PDF at this volume.
FineWeb-Edu NeurIPS 2024: educational text -> +24% ARC, +12% MMLU. InfoBay textbooks: educational by source.
Use cases
Global Presence
InfoBay maintains offices and annotation operations across North America, Europe, the Middle East, South Asia, and Southeast Asia — with language coverage spanning every major commercial AI market. Our distributed infrastructure means enterprise SLAs, regional data residency compliance, and 24-hour delivery windows.
Infrastructure & R&D
InfoBay operates purpose-built annotation platforms, GPU-accelerated processing pipelines, and proprietary quality evaluation tooling. Our technical team includes NLP researchers, computational linguists, ML engineers, and domain experts — building infrastructure that no crowdsourcing marketplace can replicate.
Dedicated GPU infrastructure for ASR model runs, OCR pipelines, speaker diarization, MinHash deduplication at scale, and language classification across 176+ languages. Processing that would take weeks on CPU runs in hours.
Purpose-built annotation interfaces for each data type: audio waveform editors with word-level alignment review, DICOM image annotation tools with radiology report linking, and structured Q&A curation workflows with expert verification steps.
Dedicated R&D function benchmarking against FineWeb-Edu, Dolma, Nemotron-CC, and production ASR baselines. Methodology papers, quality evaluation frameworks, and benchmark datasets published openly to establish technical credibility.
Automated quality gates (SNR, WER, perplexity, duplicate detection) run before any human review. Human QA operates in three tiers: general annotators, senior domain reviewers, and expert gold-set validators. IAA tracked per language per dataset.
EU AI Act Article 10 documentation architecture built-in. ISO-aligned data management processes. PII detection and redaction pipelines. Data lineage tracking from raw source to delivered training file. Audit trail available for regulated clients.
12,000+ verified domain experts across legal, medical, financial, and engineering verticals. Computational linguists for 40+ languages. Radiologists, pathologists, and clinicians for healthcare annotation. Senior software engineers for code curation and review.
Frequently Asked
Yes. The corpus is structured by language, domain, and category — licenses can be scoped to Arabic + Legal, Bahasa + Engineering, or any combination. Minimum license volumes apply. Contact us to discuss your specific requirements.
Automated PII detection runs across all datasets before delivery. Audio uses speaker anonymisation. Healthcare records have patient identifiers removed at the source. Data lineage documentation available for HIPAA, GDPR, and EU AI Act compliance review.
Yes. 10% sample packs are available for textbooks (per language group) and coding. Audio samples available for specific language-industry combinations. Healthcare samples available under NDA with IRB-equivalent institutional review. Request via the audit form.
Yes. Custom corpus collection, annotation, and curation for domains or languages not in the standard catalogue. Minimum engagement sizes apply. Lead time depends on language, domain, and volume. The Model Quality Audit is the starting point for custom engagements.
Audio: WAV/FLAC + JSON transcripts + RTTM diarisation + Metadata CSV. Textbooks: JSONL (gzip) + Parquet metadata manifest + HuggingFace-compatible. Healthcare: DICOM + PDF + anonymised metadata. Coding: JSONL per language + problem taxonomy JSON. All formats compatible with Megatron-LM, NeMo, LLaMA-Factory, and HuggingFace datasets.
Every textbook carries its ISBN. Every audio hour carries its enterprise data agreement reference, language code, industry vertical, and channel configuration. Every medical record carries its institutional source and modality. Data Cards delivered with every dataset include provenance documentation in the EU AI Act Article 10 format.
For governance and procurement teams, InfoBay also documents how corpus metadata supports reviewable training data decisions.
Contact
Tell us what you are building, what data you need, and where your model is underperforming. InfoBay will respond with the right corpus, sample, or custom data path.