Why is specialized training data crucial for generative AI models?

Generative models don't just need data, they need the right data. Specialized, high-quality datasets ensure the model learns context, structure and tone accurately. Without domain-specific data, outputs can become generic, biased or misleading.

What types of training data work best for generative models?

Text, audio, images and multimodal datasets can all be used; the key is clarity, diversity and relevance. LLMs need rich, structured text across topics and tones. Image generators need high-resolution visuals with consistent labeling.

Can I use publicly available datasets instead?

You can, but public datasets often contain noise, bias and inconsistent formatting. For commercial use, custom or curated datasets are safer and usually perform better.

How do you guarantee accuracy?

Every batch passes our multi-layer QA (peer review, expert QA and automated checks) before you sign off, and we back our work with a 98%+ accuracy guarantee.

Can we start with a pilot?

Yes. Send us a sample and we'll annotate a free pilot batch to your guidelines, so you can judge the quality before you commit.

How do you keep our data secure?

We sign NDAs, isolate projects, enforce strict access control and keep data in secure storage. Your proprietary data stays confidential.

Fine-tune your LLMs with high-quality training data

Boost generative AI and large language models with expertly labeled, diverse and scalable training data for text, image, audio and video generation.

Book a demo Try a free pilot batch

Prompt–response · QA · summarization
Domain-expert reviewers
98%+ accuracy guarantee

sft_batch_12.jsonlreview

task: explanation

domain: science · K-12

writer: subject expert

Instruction

Explain photosynthesis to a 10-year-old in two sentences.

Reference response

Photosynthesis is how plants make food. Using sunlight, they turn water from the soil and carbon dioxide from the air into sugar, and release oxygen as a by-product.

Follows the instruction
Factually correct
Reading level: age 10
No unsafe content

illustrative examplesample data

Generative AI data we create

The right data, not just more data

Generative models need specialized, high-quality datasets to learn context, structure and tone. Without purpose-built data, outputs become generic, biased or misleading.

Fact checking
Model claims verified against reliable sources and labeled true, false or unverifiable.
labelfeature_request
span“dark mode”
prioritymedium
Text classification & sequence labeling
Structure, categorize and understand language at scale.
Image captioning & sentiment
Descriptive captions and emotional context that bridge visual understanding and language.
Summarization
Long-form content distilled into clear, faithful summaries that keep the original meaning.
Question answering
Question–answer pairs that teach models to understand and respond accurately.

Grounded data

Every claim checked, not just the grammar

Reviewers break model outputs into individual claims, verify each against reliable sources and write corrected references your model can learn from.

Claim-level verdicts with evidence
Writers matched to domain and reading level
Rubric-based review before delivery

Model output · claim-level review

The Eiffel Tower was completed in 1889for the World's Fair in Londonand is about 330 metres tall.

supportedThe Eiffel Tower was completed in 1889Evidence: Encyclopedia entry · completion date
refutedthe World's Fair in LondonEvidence: The 1889 Exposition Universelle was held in Paris
supportedis about 330 metres tall.Evidence: Official site · height incl. antennas

Corrected: …for the World's Fair in Paris…

Use cases

Where teams put this data to work

Supervised fine-tuning
Prompt–response pairs that teach a base model your tone, format and domain.
Domain assistants
Expert-written data for legal, medical, finance and technical copilots.
Summarization & QA
Faithful summaries and grounded question–answer pairs from your documents.
Vision-language data
Image captions and visual QA for multimodal models.

How we work

From pilot to production

We agree on task definitions and quality bars with you, write and review data with domain specialists, calibrate on your feedback and scale once the pilot meets your standard.

Step 01
Consult
We review your data, edge cases and success criteria, then write annotation guidelines with you.
Step 02
Annotate
A trained team labels a pilot batch, following the agreed guidelines to the letter.
Step 03
Calibrate
You review the pilot. We fold your feedback into the guidelines before scaling up.
Step 04
Multi-layer QA
Every batch passes peer review, expert QA and automated checks before it leaves us.
Step 05
Deliver
Labels ship in your format on your schedule, ready to drop into training.

Quality pipeline

Five checkpoints between raw data and your model

98%+

Accuracy guarantee

01
Annotator
Labels against your guidelines
02
Peer review
A second annotator reviews the work
03
Expert QA
Senior reviewer samples and corrects
04
Automated checks
Rules catch missing or invalid labels
05
Client sign-off
You approve before final delivery

Results

Quality you can measure

Accuracy guarantee: 98%+
Accuracy guarantee
Returning clients: 96%
Returning clients
Cost reduction: 2.5x
Cost reduction
Projects completed: 200+
Projects completed

“Our fashion AI model required pixel-level segmentation across 72 garment categories, and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.”

Valerio Colamatteo

Sr. AI Scientist & Team Lead, MindEarth

FAQ

Questions, answered

Something else on your mind? Email sales@intellisane.ai.

Get started

Let's scope your project

Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.

Book a demo Try a free pilot batch

Fine-tune your LLMs with high-quality training data

The right data, not just more data

Fact checking

Text classification & sequence labeling

Image captioning & sentiment

Summarization

Question answering

Every claim checked, not just the grammar

Where teams put this data to work

Supervised fine-tuning

Domain assistants

Summarization & QA

Vision-language data

From pilot to production

Consult

Annotate

Calibrate

Multi-layer QA

Deliver