PRODUCTS
Every strong AI product starts with strong data. Algorithms and compute get the headlines, but the quality of your data for AI decides how accurate, fair and useful your model will be once it meets real users. Dhumi helps businesses, startups and research teams collect, clean, structure and deliver the data their models need to perform.
From a custom AI dataset built around your exact use case to a managed web scraping service for AI training datasets, we handle the hard, unglamorous data work so your engineers can focus on building.
THE PROBLEM
Most AI projects do not fail because of a bad model architecture. They fail because the data feeding the model is incomplete, outdated, biased or inconsistent. A model can only learn what its data shows it, so gaps and noise in the data become gaps and noise in the output.
Teams often assume that more records automatically mean better results. In practice, a smaller, well-curated set of AI training data sets usually beats a huge pile of messy, duplicated or irrelevant records. Relevance, accuracy, diversity and consistent labeling are what move model performance.
Bad data shows up late and costs a lot. Engineers spend weeks cleaning files by hand, models hallucinate or drift in production, and retraining cycles stretch out. Investing in the right AI dataset at the start saves time, compute budget and credibility later.
| Common Data Problem | Impact on Your AI Model |
|---|---|
| Duplicate and near-duplicate records | Overfitting and inflated accuracy scores |
| Inconsistent labels | Unreliable predictions and poor generalisation |
| Outdated information | Models that give stale or wrong answers |
| Narrow or biased sources | Unfair results and weak real-world performance |
| Missing fields and broken formats | Failed training runs and wasted engineering time |
WHAT WE DELIVER
Dhumi covers the full data lifecycle, from sourcing to delivery. You can use us for a single step or hand over the entire pipeline.
DATASETS
We build AI training data sets for a wide range of models and applications. Here is a snapshot of what teams ask us for most.
| Dataset Type | Typical Use Case | Example Fields |
|---|---|---|
| Text and language datasets | Chatbots, search, summarisation, LLM fine-tuning | Articles, reviews, FAQs, forum posts, metadata |
| Product and pricing datasets | Price intelligence, recommendation engines | Title, price, category, availability, ratings |
| Review and sentiment datasets | Opinion mining, brand monitoring | Review text, rating, date, source, tags |
| Job and talent datasets | HR tech, matching, salary benchmarking | Role, skills, location, salary range, company |
| Real estate and location datasets | Valuation, demand forecasting | Price, area, amenities, location, listing date |
| Image and media datasets | Computer vision, object detection | Image files, labels, bounding boxes, captions |
| Financial and market datasets | Forecasting, risk scoring | Prices, filings, news, indicators |
Don't see your use case here? We regularly build niche datasets from scratch, so tell us what your model needs to learn.
HOW IT WORKS
We keep the process transparent and predictable, so you always know what you are getting and when.
We start with your model objective, target users and success metrics. From there we agree on the data fields, volume, languages, regions, time range and quality thresholds.
We identify reliable, relevant public sources and build collection workflows around them. Where needed, we combine multiple sources to improve coverage and diversity.
Our scrapers pull the data, and automated checks clean and validate it. We deduplicate, standardise and filter out noise before anything reaches you.
We organise the data into your required schema and add labels or annotations if your model needs them. Samples are reviewed by humans to catch what automation misses.
You receive the dataset in your preferred format and location. If you need fresh data, we set up recurring runs and monitor them so nothing breaks quietly.
WHO IT’S FOR
Our AI dataset services support teams across many sectors.
Product catalogs, pricing history, customer reviews and competitor listings power recommendation, search and demand forecasting models.
News, market signals, public filings and alternative data feed risk models, forecasting systems and research tools.
Public research content, terminology and non-sensitive structured data support search, summarisation and knowledge systems. We never collect private patient information.
Listings, availability, pricing and location data help valuation, recommendation and dynamic pricing models.
Job postings, skill taxonomies and course content support matching engines, learning platforms and career tools.
Early-stage teams get focused, affordable datasets to prototype faster, while larger labs get scale and repeatability.
TRUST & SECURITY
Responsible AI begins with responsibly sourced data. We build quality and compliance into every project, not as an afterthought.
Every dataset goes through validation rules, duplicate detection, schema checks and sample audits. We share quality reports so you can see what was collected, what was removed and why.
We focus on publicly available data and respect website terms, robots directives and applicable data protection laws. We avoid personal and sensitive information unless a lawful basis and your explicit instructions exist, and we can anonymise or mask fields when required.
Your requirements, datasets and delivery channels stay confidential. We use controlled access, secure transfer and clear data-handling agreements so your intellectual property stays protected.
| Area | What We Do |
|---|---|
| Accuracy | Validation rules, sample audits, human review |
| Consistency | Standard schemas, normalised fields, label guidelines |
| Compliance | Public-source focus, terms awareness, privacy-conscious design |
| Security | Secure transfer, restricted access, confidentiality agreements |
WHY DHUMI
We do not sell generic, one-size-fits-all files. Each AI dataset is scoped around your use case, your schema and your quality bar.
Dhumi is a software and AI company. Our team understands how models are trained, which means we think about data the way your ML engineers do: formats, edge cases, bias and drift included.
Whether you need ten thousand records or ten million, our pipelines are built to scale, recover from failures and adapt when sources change.
You get defined milestones, sample batches for approval and honest timelines. No surprises, no black boxes.
ENGAGEMENT
Choose the setup that fits your stage and budget.
| Model | Best For | What You Get |
|---|---|---|
| One-time dataset | Prototypes, research, proof of concept | A defined AI dataset delivered once, with a quality report |
| Recurring data feed | Production models that need fresh data | Scheduled collection, cleaning and delivery |
| Dedicated data pipeline | Large teams with continuous needs | Custom pipeline, monitoring, API or cloud delivery |
| Annotation add-on | Supervised learning projects | Labeling, review and consistency checks on your data |
FAQ
Data for AI is the collected, cleaned and structured information used to train, validate and test machine learning models. The better and more relevant the data, the better the model performs.
An AI dataset is an organised collection of examples, such as text, images, numbers or records, that a model learns from. It is usually split into training, validation and test portions.
Regular scraping focuses on pulling data out of a page. A scraping service built for AI goes further: it cleans, deduplicates, standardises, labels and validates the data so it is actually ready to train a model.
Yes. Niche domains are where custom collection helps most, because off-the-shelf datasets rarely match specialised vocabulary, formats or scenarios.
We deliver in CSV, JSON, Parquet, database exports and image or text file collections, through cloud storage, secure download or API.
Small datasets can be ready within days, while larger or labeled datasets take longer. We share a timeline after the requirements call and send a sample batch early so you can approve direction.
We focus on publicly available, non-sensitive data. If a project involves personal information, we work only within applicable laws and your documented instructions, and we can anonymise fields.
Your model is only as good as the data behind it. Tell us what you are building, and we will map out the dataset, the sources and the timeline to get you there. Share your requirements and get a clear plan from the Dhumi data team.