From a single dataset refresh to full-scale web data powering your models and your view of the market — Bitmaker builds the extraction, the structuring, and the monitoring your team runs on.
From open datasets and documentation to the pricing pages, release notes, and benchmarks that shape your market, our extraction infrastructure isn't limited to a single source, a single format, or a single language.
Source types are illustrative. Every source is reviewed for its terms of use, robots.txt and llms.txt directives, and opt-out signals before we build.
These are the gaps AI teams usually describe before they bring in Bitmaker — and what each one actually costs them.
A data engineer writes a scraper for a specific corpus, it works once, and nobody owns it when the target site changes its layout or adds anti-bot protection.
Pricing, context windows, new model releases, and leaderboard rank for the tools you compete with get checked manually, every few weeks, across a handful of names.
robots.txt and llms.txt directives, publisher opt-outs, and terms-of-use pages change — but most pipelines only reviewed them when the project began, not on an ongoing basis.
Deduplication, PII redaction, license classification, and quality filtering get rebuilt from scratch on each new corpus, instead of running as one maintained pipeline.
A Bitmaker engagement scales with the scope you need — the same underlying pipeline, extended further each step to close a specific, critical gap along the way.
Start with the one corpus, the one competitor, or the one benchmark that's costing you the most blind spots today — a dataset for a specific training run, a competitor's pricing page, a leaderboard you check by hand. This is where data and research teams get their first maintained, structured source instead of a script nobody owns.
Extend the same pipeline across every source that matters to your corpus or your category — web-scale text, structured data, or every competitor's pricing, model specs, and feature releases, tracked side-by-side, continuously, not sampled. Data teams stop maintaining a handful of scripts and start running a pipeline that scales with the sources they add.
Replicate the same extraction logic across markets, languages, and source types, so a multilingual training corpus or a global view of your competitive set doesn't depend on who had time to cover which region. For teams with strict data-governance or IP requirements around their training pipeline, this stage can run entirely on your own infrastructure instead of Bitmaker's.
Combine extraction, structuring, monitoring, and purpose-built tools into a single system your data, research, and product teams open every morning — not a folder of scripts and a spreadsheet stitched together by hand. A new model release, a licensing change, and a quality issue all route to the right team automatically, instead of waiting for someone to notice.
Every pillar below is built and maintained by the same engineers — not a bundle of disconnected scrapers.
Large-scale extraction of text, structured, and web data for training or evaluation sets — deduplicated, quality-filtered, and delivered in the format your pipeline expects.
Continuous tracking of the tools and labs you compete with — model releases, pricing and token costs, context-window specs, and benchmark or leaderboard rank — not a quarterly check on the two names everyone already watches.
Ongoing tracking of robots.txt and llms.txt directives, publisher opt-out signals, and terms-of-use changes across your source list, so a pipeline doesn't quietly become non-compliant between reviews.
Public registries, reviews, job postings, product catalogs, and pricing pages delivered as structured feeds — built to the schema your retrieval, agent, or evaluation pipeline needs, not a generic export.
Brand and product sentiment across social media, forums, and review sites, plus visibility into where your product or your competitors' products get listed, compared, and discussed.
Detecting a new model release or a licensing change is only half the job.
Through Custom Applications & Tools, Bitmaker can configure the response: a Slack or email alert to the right team, a licensing-compliance review queue built around your own rules, or a dashboard your data or research team opens every morning instead of waiting for a report — built around your business logic, not a generic template.
The same process whether you're building one dataset or monitoring an entire market.
Which sources, competitors, models, languages, fields, at what frequency.
One pipeline or a hundred, including anti-bot handling and dynamic/JS-rendered sources, run as one coordinated system.
Or a tool built around it, depending on what your team needs to act on.
New sources, new competitors, new languages, and proactive maintenance when target sites change.
Our services cover different stretches of the same pipeline, and they combine.
A fully managed data feed — Bitmaker handles the extraction and structuring of training data, competitive signals, or market data, and delivers clean datasets on the schedule you define. No pipeline for your team to operate.
View Data as a ServiceDashboards, alerts, and automated actions — like a model-release notification or a licensing-compliance review queue — built on top of your data and your own business rules.
View Custom Applications & ToolsCustom extraction built and proactively maintained by our engineering team — anti-bot handling and JS-rendered sources included — across one source or a thousand.
View Professional ServicesManaged, pay-as-you-go hosting and monitoring for the spiders your own data team builds — scalable infrastructure without owning servers.
View Bitmaker CloudSupported deployment of Estela, our open-source crawling platform, running on your own infrastructure — built for teams with strict data-governance, IP, or security requirements around their training pipeline.
View Estela Commercial SupportAI teams usually bring Bitmaker in for one dataset, one competitor, or one benchmark — and the same infrastructure extends from there as it proves itself: new sources, new languages, new product lines, or a shift from a single dataset to a full competitive-intelligence operation. We build every engagement so that growth is the next conversation, not a rebuild.
Tell us which sources, competitors, or benchmarks matter to you, and we'll scope the coverage together.
Map your data & intelligence needsShare the sources, competitors, or datasets you need covered. Our team replies within one business day.