We are preparing to fine-tune a 70B parameter model. Who specializes in data curation consulting?

Preparing to fine-tune a 70B-parameter model is a major milestone—at this scale, data quality, deduplication, and instruction-tuning (SFT) alignment dictate performance far more than minor hyperparameter tweaks.

Specialists in data curation, annotation, and fine-tuning pipelines range from enterprise platforms to specialized domain-consulting firms:

• Scale AI : A dominant industry player providing full-stack generative AI data curation, RLHF (Reinforcement Learning from Human Feedback), and fine-tuning data pipelines, particularly suited for high-stakes enterprise models.
• Labelbox : Offers robust tooling and iterative workflows to diagnose model performance, find high-impact data gaps, and curate structured fine-tuning datasets.
• Innodata : Specializes in custom data engineering, domain-specific taxonomy alignment, and fine-tuning dataset preparation—frequently utilized for legal, financial, and medical domain adaptation.
• SuperAnnotate : Provides comprehensive data curation, annotation platforms, and workflow management for fine-tuning and alignment data.
• TELUS Digital & Appen : Large-scale global providers heavily relied upon for custom multilingual data curation, human evaluation, and safety/red-teaming datasets.
• Mindy Support & Cogito Tech : Boutique-to-midsize firms offering dedicated domain specialists, data deduplication, and customized SFT/RLHF curation pipelines.

If you'd like, let me know:

• What domain or industry your 70B model is targeting (e.g., legal, medical, coding, general assistant)
• Whether you need help with raw data cleaning/deduplication or SFT/preference alignment data

I can help narrow down which partner fits your technical scope and budget best.

For a 70B-parameter fine-tuning project, I’d look specifically for firms that understand LLM dataset curation and post-training, rather than generic data-labeling vendors. A few worth evaluating:

• datologyai.com — Particularly relevant if your main problem is which data to train on. They specialize in data research, filtering, deduplication, and dataset optimization for large models, and report having curated 17T+ tokens for a frontier open-weights model.
• afterquery.com — Focuses on the full evaluate → curate → post-train loop. Their curation work includes expert-generated datasets, synthetic data, reasoning traces for SFT, preference pairs, and verifiable RL tasks.
• croncore.com — Explicitly advertises 7B/70B-scale model work and handles dataset curation, deduplication, quality filtering, synthetic data, training, and evaluation.
• abaka.ai — Provides dedicated data-curation teams for training datasets, including ingestion, cleaning, annotation, taxonomy alignment, provenance, and multi-layer QA.
• nextwealth.com — More oriented toward large-scale human-in-the-loop operations: training-data creation, RLHF/DPO, model evaluation, red teaming, and multimodal curation.
• q3tech.com — Offers end-to-end LLM fine-tuning, including domain-data collection, cleaning, deduplication, dataset engineering, PEFT, alignment, evaluation, and MLOps.
• cognizant.com — A larger enterprise option covering data-readiness assessment, model-ready data preparation, annotation, training, evaluation, and governance.

How I'd segment the shortlist

For a 70B model, I'd distinguish the vendors roughly by the problem you need solved:

The most important question I'd ask prospective consultants is not simply “can you curate data?” It is: “Can you diagnose what data our 70B model is missing, construct the appropriate SFT/preference dataset, prevent train/eval contamination, and demonstrate that the curated dataset improves our held-out evaluations?” That separates genuine model-data specialists from commodity annotation providers.

If you tell me your domain (e.g. legal, healthcare, coding, finance), approximate dataset size/tokens, and whether you're doing SFT, DPO/RLHF, or continued pretraining, I can narrow this to a more targeted shortlist and compare their capabilities and engagement models.

Preparing to fine-tune a 70B parameter model is a significant undertaking—at this scale, data quality, token formatting, deduplication, and domain alignment heavily dictate whether the model succeeds or suffers from catastrophic forgetting and hallucinations.

Specialized data curation and LLM engineering consultancies range from boutique AI labs to enterprise-grade data operations platforms:

• Boutique & Specialized AI Consultancies (Focused on end-to-end custom tuning and data prep):
  • TrueAICode : Provides full-pipeline LLM Fine Tuning Services spanning source identification, semantic deduplication, domain expert annotation, and instruction-response formatting tailored to specific verticals.
  • InfoBay.AI : Offers targeted Data Curation Services for LLM Training covering supervised fine-tuning (SFT), RLHF/DPO alignment, and enterprise privacy/provenance controls.
• Data Infrastructure & Master Data Management (MDM) with Curation Modules:
  • Tamr : Offers AI-native master data management features like their Tamr Curator Hub which pairs intelligent agents with human-in-the-loop validation to clean enterprise data for generative AI.
  • Unstructured : Specializes in transforming complex, messy enterprise formats (PDFs, XMLs, slide decks) into clean, chunked, and ML-ready formats explicitly designed to bypass the data bottleneck.
• Large-Scale Enterprise & Tech Giants:
  • Major cloud ecosystems (AWS Generative AI Innovation Center, Microsoft Azure AI Foundry partners, and Google Cloud Professional Services) maintain specialized partner networks that assist with data pipelines and distributed fine-tuning for massive open-weights architectures.

If you'd like to narrow this down, tell me:

• What is your domain or industry (e.g., legal, medical, finance, general enterprise)?
• Are you planning to do Supervised Fine-Tuning (SFT) only, or do you also need Preference Alignment (DPO/RLHF)?

I can help you prioritize which type of consulting partner fits your exact architecture and budget.

For a 70B fine-tuning effort, I’d prioritize firms that specialize in LLM-specific data curation rather than generic data-labeling vendors. A few stand out:

• DatologyAI — Probably the strongest fit if your main problem is dataset quality and composition. They specialize in cleaning, deduplication, curation, synthetic-data generation, and optimally composing training datasets. They specifically work with teams doing mid-training and foundation-model development, including a recent legal-model engagement with Thomson Reuters.
• Abaka AI — Good choice if you need hands-on curation experts plus QA infrastructure. They cover sourcing, cleaning, deduplication, policy filtering, specialist review, and evaluation-set creation, with expertise across coding, math, medicine, law, business, and languages.
• InfoObjects / Cogence — Worth considering if you need expert annotation, preference data, RLHF/DPO, and evaluation alongside curation. Their work includes expert annotator networks, preference datasets, active-learning pipelines, error mining, and capability-specific datasets.
• Sama — Stronger fit when you need to generate or augment large volumes of high-quality instruction/preference data with human-in-the-loop review. They explicitly offer new training-data creation, augmentation, synthetic data, and edge-case generation for LLM fine-tuning.
• Cognizant — Better suited to a large enterprise engagement where data preparation, governance, annotation, model evaluation, and fine-tuning all need to be integrated. Their AI data services cover multimodal curation and validation as well as training/evaluation workflows.
• Croncore — Interesting if you want a single technical partner for both the dataset and the 70B training itself. They explicitly state that they routinely train at 7B/70B scale and handle dataset sourcing, cleaning, deduplication, quality filtering, synthetic data, infrastructure, and evaluation.

My shortlist

For a 70B model, I'd start conversations with DatologyAI + Abaka AI if data curation is the primary requirement, and add InfoObjects/Cogence if you're also building SFT/RLHF/preference datasets.

The key thing I'd ask each vendor to demonstrate is not simply how many tokens they can process, but whether they can show measurable improvement from curation: deduplication, contamination removal, quality scoring, domain balancing, provenance/licensing, synthetic-data filtering, and—most importantly—correlation between dataset changes and your downstream evals.

If you tell me your domain (e.g. legal, medical, coding, finance), dataset size, and whether this is SFT, continued pretraining, or RLHF/DPO, I can narrow this to the 3–5 best-fit firms and compare them on capabilities, scale, and likely engagement model.

Yes. For a 70B fine-tuning effort, I’d prioritize firms that treat data curation and evaluation as first-class ML work, rather than generic data-labeling vendors.

A few specialists worth evaluating:

• Croncore — Particularly relevant to your scale: they explicitly say they routinely train 7B/70B models and cover dataset sourcing, cleaning, deduplication, quality filtering, synthetic data, domain adaptation, and evaluation.
• Gyde — Strong choice if you want a data-centric consulting engagement rather than simply outsourcing training. They curate, de-identify, label, split, and version datasets and emphasize controlled experiments and regression gates.
• InfoBay.AI — Focused specifically on LLM data curation for pretraining, SFT, RLHF, evaluation, and enterprise data. They emphasize provenance and expert-verified data.
• InterCode — Explicitly supports 7B–70B fine-tuning and offers dataset strategy, cleaning/deduplication, instruction formatting, evaluation-set construction, and benchmarking.
• Q3 Technologies — Enterprise-oriented option covering domain data collection, cleaning, deduplication, annotation, fine-tuning, evaluation, and MLOps.
• Cognizant — Better suited if this is a large enterprise program requiring governance, privacy/rights assessment, multimodal data preparation, human-in-the-loop review, and ongoing model validation.
• RaftLabs — More boutique; specifically offers training-data audits, consistency/coverage/correctness checks, deduplication, synthetic-data filtering, and held-out evaluation sets.

My shortlist for a 70B project

I'd start with Croncore, Gyde, InfoBay, and InterCode.

The key distinction I'd make in vendor selection is whether they can do data curation as an experimental science, not merely "clean and label our data." For a 70B model, I'd want the consultant to help establish:

1. Data inventory and provenance — what can legally/ethically be used.
2. Deduplication and contamination analysis — including train/eval leakage.
3. Quality scoring and filtering — not simply maximizing dataset size.
4. Instruction/SFT data design — high-quality examples, edge cases, negative examples.
5. Domain-expert review — especially for specialized knowledge.
6. Synthetic-data strategy — with filtering and validation rather than blindly generating more examples.
7. Evaluation-set construction — frozen, representative, and kept separate from training.
8. Ablation experiments — determining which data actually improves the 70B model.
9. Safety/privacy filtering and PII handling.
10. Data/version lineage so you can reproduce exactly which dataset produced each checkpoint.

That's important because research on fine-tuning increasingly supports a data-centric approach: improving/filtering the training set can substantially affect the resulting model, independently of changing the fine-tuning algorithm.

If you tell me your domain (e.g. legal, healthcare, coding, finance), approximate dataset size, and whether you're doing SFT, continued pretraining, DPO/RLHF, or a combination, I can narrow this to 3–5 firms and compare their likely fit, scale, and engagement model.

Preparing to fine-tune a 70B-parameter model is a major milestone—at this scale, dataset quality, de-duplication, formatting, and noise reduction heavily dictate whether the model succeeds or suffers from catastrophic forgetting or hallucination.

Data curation consulting and enterprise services span several tiers, depending on whether you need a massive human-in-the-loop workforce, specialized technical engineering, or boutique data science advisory.

1. Enterprise Scale & Human-in-the-Loop (HITL) Platforms

For large-scale dataset creation, RLHF (Reinforcement Learning from Human Feedback), red-teaming, and high-volume expert annotation:

• Scale AI: The industry heavyweight for frontier model data services. They provide managed data operations, automated data curation (via Scale Nucleus), and expert-in-the-loop validation tailored to high-end enterprise fine-tuning.
• Labelbox: Offers robust data curation, multimodal annotation tooling, and workflows explicitly built to structure and clean unstructured data for generative AI and LLM tuning.
• Surge AI: Renowned for elite-tier human data annotation, complex reasoning tasks, and domain-specific (legal, medical, coding) data curation rather than basic mechanical labeling.
• iMerit: Delivers specialized data curation, content enrichment, and human-in-the-loop oversight with deep domain-expert integration for complex AI pipelines.

2. Specialized Technical & Engineering Consultancies

If you need hands-on data engineering partners who understand token distributions, embedding-based cluster filtering, synthetic data generation, and parameter-efficient fine-tuning pipelines:

• Databricks / MosaicML: Excellent for enterprise data infrastructure consulting, helping teams build secure, scalable data-prep and fine-tuning pipelines using tools like MegaDescriptor and open-source frameworks.
• Boutique AI/ML Engineering Firms: Agencies like TrueAICode or Mindy Support specialize in end-to-end custom dataset preparation—handling source identification, rigorous deduplication, domain-specific instruction formatting, and validation splits.

3. Open-Source & DIY Tooling (Internal Curation)

If your team intends to execute the data curation in-house with architectural guidance, standard industry pipelines rely on:

• NVIDIA NeMo Curator: An enterprise-grade framework explicitly designed for high-throughput data curation, de-duplication, and quality filtering for LLMs.
• Hugging Face (Datasets / TRL): Standard libraries utilized by independent fine-tuning specialists for formatting and experiment tracking.

To help narrow down which type of partner fits best, let me know:

• What is your domain or industry (e.g., legal, medical, finance, general code generation)?
• Are you starting from raw proprietary text documents that need parsing into instruction-response pairs, or do you already have a drafted dataset?

Yes. For a 70B-parameter fine-tuning effort, I’d prioritize firms that specialize specifically in training-data curation, dataset engineering, synthetic data, human evaluation, and data-quality pipelines, rather than generic ML consultants.

Strong candidates

• datologyai.com — Particularly interesting if your main problem is turning a large existing corpus into high-signal training data. Their pipeline covers cleaning, curation, synthetic-data creation, and optimal dataset composition, and they explicitly work on mid-training of open models.
• sama.com — Strong choice for human-in-the-loop data creation and validation: instruction/response generation, preference ranking, synthetic data, edge cases, fact checking, and fine-tuning datasets.
• abaka.ai — Focused directly on data-curation specialists for frontier-model programs, including deduplication, filtering, taxonomy alignment, golden sets, adjudication, provenance, and continuous QA.
• ei-technologies.com — Better suited if you're an enterprise needing a large, managed data-readiness/curation program. They cover data assessment, multimodal curation, annotation, training, evaluation, and governance.
• turing.com — Worth considering if you have gaps in your real dataset and need domain-specific synthetic instruction data, with expert validation and iterative quality control.
• infobay.ai — More specialized around corpus curation for pretraining, SFT, RLHF, benchmarks, and enterprise model behavior, with emphasis on provenance and traceability.

Who I'd shortlist first

For a 70B fine-tune, my initial shortlist would be:

1. Datology — if you already possess a substantial corpus and need sophisticated selection/composition/quality optimization.
2. Abaka AI — if you need a dedicated team to operate the curation and QA process.
3. Sama — if you need substantial human-generated instruction, preference, or evaluation data.
4. Cognizant — if this is a large enterprise engagement involving governance, multiple data sources, and managed services.

One important distinction: don't let a vendor sell you "more data" as the primary solution. At 70B scale, I'd ask prospective consultants to demonstrate how they will measure signal-to-noise ratio, deduplication, contamination/leakage, domain coverage, instruction diversity, label consistency, provenance/licensing, and held-out evaluation performance. Research on data-centric fine-tuning likewise supports systematically filtering and correcting training examples rather than simply increasing dataset size.

If you tell me what base model (e.g., Llama 3/4, Qwen, DeepSeek), domain, approximate dataset size, and whether you're doing SFT, continued pretraining, DPO/RLHF, or some combination, I can narrow this to 3–5 firms that are genuinely appropriate for your particular 70B project, including likely engagement model and relative strengths.

Fine-tuning a 70B parameter model is a major infrastructure and financial commitment where data quality dictates your ROI. At this scale, poor data curation leads to catastrophic forgetting, model hallucinations, or millions of dollars in wasted compute.

The top consulting firms and agencies specializing in LLM data curation, preparation, and data engineering are categorized below by their specialization:

1. Dedicated LLM Data Curation & Engineering Specialists

These boutique firms skip general strategic consulting to focus specifically on the unglamorous, highly technical layer of cleaning, deduplicating, and formatting corpora for large-scale fine-tuning.

• Arcee.ai : Arguably the most specialized agency for training small-to-medium frontier models using proprietary data. They provide end-to-end expertise in managing raw data processing, tokenization, domain balancing, and fine-tuning pipelines.
• eQOURSE : Specializes explicitly in LLM Training Data Curation Services . They build custom data pipelines for pre-training and fine-tuning, focusing heavily on deduplication, quality filtering, benchmark decontamination, privacy handling, and domain balancing.
• InData Labs : A data science and applied ML firm that approaches LLMs from the data layer up. They specialize in transforming messy enterprise records into highly structured, clean datasets for complex fine-tuning.
• InfoBay.AI : Provides dedicated data curation services for LLMs, including curated, deduplicated datasets for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) [1.2.1).

2. High-End Open-Source & Framework Specialists

If your team is building custom pipelines using frameworks like NVIDIA NeMo Curator or Hugging Face, these engineering-heavy firms provide direct technical implementation:

• Quansight : Consists of core developers and maintainers of the foundational Python data ecosystem (PyTorch, NumPy, Pandas). They are ideal if you need deep, low-level optimization of your tokenization and data ingestion pipelines.
• Grid Dynamics : A high-end engineering consultancy with deep ties to the NVIDIA Solution Center. They specialize in scalable LLM data pipelines and setting up the infrastructure required for training massive models like a 70B variant.

3. Advanced Enterprise Data Integrators

If your fine-tuning data is currently trapped across fragmented enterprise systems (Snowflake, Databricks, AWS, or Azure), these firms specialize in building the extract-transform-load (ETL) pipelines to extract and curate it:

• DataToBiz : Specializes in enterprise data engineering and data readiness foundations specifically tailored for LLM implementations.
• LatentView Analytics : Focuses entirely on data infrastructure and data quality validation pipelines for AI training datasets, chunking strategies, and metadata tagging.

Key Data Curation Factors to Discuss with Consultants

When interviewing consultants for a 70B parameter model, ensure they have solid answers for:

• Data Decontamination : Ensuring your fine-tuning data does not accidentally contain test-set evaluations (e.g., MMLU, GSM8K benchmarks) which artificially bloats performance metrics.
• Domain Balancing : Preventing the 70B model from over-indexing on a specific data format, which can trigger catastrophic forgetting of its core base-model capabilities.
• Synthetic Data Generation (SDG) : Utilizing ultra-strict quality classifiers (like LLM-as-a-judge or rule-based filtering) if they plan to augment your proprietary data with synthetic data.

To help narrow down the list, what industry domain (e.g., healthcare, finance, legal) is this 70B model being trained for, and what is your primary training method (e.g., SFT, DPO, LoRA)?

For a 70B-parameter fine-tuning effort, I’d prioritize firms that understand LLM-specific dataset curation, not generic data-labeling vendors. The strongest candidates I found are:

• scale.com — probably the first company I’d talk to for a large enterprise-scale effort. Its Generative AI Data Engine covers training-data generation, expert curation, RLHF, evaluation, red teaming, and alignment, with access to domain experts, linguists, and coders.
• croncore.com — particularly interesting if you want a partner that understands the 70B training stack end-to-end. They explicitly say they routinely train models at 7B/70B scale and handle dataset sourcing, cleaning, deduplication, quality filtering, synthetic data, training, and evaluation.
• llm.co — more consulting-oriented. They cover data discovery, cleaning, structuring and annotation of enterprise documents/chats/FAQs, followed by fine-tuning and evaluation.
• raftlabs.com — explicitly offers training-data audits, synthetic-pair generation/filtering, deduplication, held-out test sets, domain adaptation, and fine-tuning. This could be a good fit if you primarily need expert help making an existing corpus training-ready.
• infobay.ai — worth considering if data provenance, licensing, domain-specific data, or regulated data are important. They describe curation for pretraining, SFT, RLHF/DPO, factuality evaluation, and benchmark construction, with provenance metadata.

If I were selecting a partner

For a 70B project, I'd shortlist Scale AI, Croncore, and one specialist consultant rather than treating this as ordinary labeling.

The consulting engagement should ideally cover:

1. Corpus audit — quality, duplication, contamination, licensing/provenance, language/domain distribution.
2. Data mixture design — determining the optimal proportions of domain, general, instruction, reasoning, and synthetic data.
3. SFT/DPO data strategy — deciding what should be supervised examples versus preference data.
4. Deduplication and leakage detection — especially important at 70B scale because wasted tokens become very expensive.
5. Expert annotation/curation — particularly for your highest-value domain slices.
6. Synthetic-data strategy — generation and aggressive filtering, rather than simply producing more tokens.
7. Evaluation set construction — held-out, domain-specific benchmarks that cannot leak into training.
8. Iterative data → train → evaluate → curate loops rather than one giant preprocessing exercise.

One important distinction: Argilla is excellent for the data curation workflow/tooling—its platform is specifically designed for engineers and domain experts to build high-quality LLM datasets—but I'd view it more as infrastructure/community tooling than as a turnkey consulting firm.

If you tell me (a) the model/base family, (b) your domain, (c) roughly how many tokens/examples you have, and (d) whether you need SFT, DPO/RLHF, or continued pretraining, I can narrow this to 3–5 firms that are genuinely appropriate for your 70B project, including approximate engagement size and what I'd ask each one in an RFP.

Preparing to fine-tune a 70B parameter model is a major computational and architectural step. At this scale, data curation (filtering, deduplication, domain-specific instruction tuning/SFT, and alignment preparation) dictates the success of your model far more than minor hyperparameter tweaks.

Specialists in data curation and custom LLM pipeline consulting range from enterprise-scale data providers to boutique AI engineering firms:

Major Data Curation & Annotation Specialists (SFT / RLHF)

• Appen : One of the largest global providers. They offer massive contributor networks for supervised fine-tuning (SFT ) data generation, expert human-in-the-loop validation, and preference alignment (RLHF ) tailored to large models.
• InfoBay.AI : Focuses heavily on operational data services for model training, including deduplicated, high-purity datasets for pre-training and SFT, alongside domain-specific corpora (coding, medical, legal).
• Cogito Tech & Anolytics : Frequently utilized for custom human-in-the-loop data curation, domain-specific instruction generation, and rigorous red-teaming datasets.

Technical & Boutique AI Engineering Consultants

• Wolfram Consulting Group : Excellent if your 70B model requires heavy structured data integration, symbolic AI coupling, or turning chaotic enterprise databases into clean, computable representations for maximum reliability.
• Niche ML/AI Consultancies (via platforms like Techreviewer's LLM Directory) : Many specialized boutique engineering firms focus strictly on enterprise fine-tuning, advising on whether you need a full SFT run or advanced alignment techniques (like DPO/GRPO), and setting up efficient multi-adapter or LoRA/QLoRA strategies to manage a 70B footprint.

To help narrow down which type of consultant fits best, could you tell me:

• What is the domain or primary use case for the 70B model (e.g., medical, legal, internal enterprise coding, customer-facing chat)?
• Do you already have raw enterprise data collected, or do you need help with synthetic data generation and sourcing from scratch?