Web data your business and your agents can rely on.
A managed web scraping service: monitored data feeds from public websites, built by AI agents, checked by engineers and delivered by API, MCP or file, with the source on every row. Then, if you want, the agents that use it.
Sample in 2 business days. No contract to see it, no sales sequence after.
100 rows
from your sources, before you pay anything
With the sample you get the access route we'd use and a fixed quote: setup plus a monthly price that covers running and repairing the feed.
- Refresh
- Any cadence weekly, daily or hourly
- Delivered by
- API · MCP files or warehouse
- Then
- Agents built on your data
35% off · 6 months
Every row carries its receipt.
You get clean rows in the schema you agreed, and each one says where it came from. When someone asks “where did this number come from?”, the answer is in the row:
- The page it came from
- When it was read
- The schema version it follows
- No value written by a model
{ "product": "Trail runner 2 · black", "store": "Store B", "price": 124.95, "was": 129, "currency": "USD", "in_stock": true, "source_url": "https://store-b.example/p/trail-runner-2", "retrieved_at": "2026-11-08T06:00:12Z", "schema": "prices@3"}How the top 1,000 sites treat AI crawlers.
We read the robots.txt of the 1,000 most visited sites. 30% block at least one AI training crawler, and 14% turn away a plain automated client outright. That is why we choose an access route per source.
The AI Crawler Access Index →| Crawler | Operator | Used for | Blocked by |
|---|---|---|---|
| GPTBot | OpenAI | Model training | 21% |
| ClaudeBot | Anthropic | Model training | 21% |
| Google-Extended | Model training | 18% | |
| Applebot-Extended | Apple | Model training | 18% |
| Meta-ExternalAgent | Meta | Model training | 20% |
| CCBot | Common Crawl | Model training | 24% |
Share of sites whose robots.txt disallows the crawler outright. Tranco top 1,000, collected 6 November 2026. Data under CC BY 4.0.
“Can't my AI agent just do this?”
For 50 rows, once: yes, and we'll tell you so. For data your team or product depends on every day, you need what an agent session doesn't give you.
| Aspect | Your own AI agent | A managed feed from us |
|---|---|---|
| Good for | 20–500 rows, once, today | 10k to 10M rows, every day, for years |
| Cost | Roughly $0.02–0.50 per page-level task in model and browser time | Fractions of a cent per page: agents build the pipeline, plain code runs it |
| When a site changes | Returns wrong or empty data, silently | Detected, alerted and fixed within the SLA |
| Bot blocking | Increasingly blocked by default | A route that keeps working: official APIs, licensed or engineered access |
| Schema | Different every run | A versioned contract; changes announced in advance |
| Traceability | “The model found it” | Source URL, timestamp and method on every value |
| Where it lands | A chat transcript | Your API, MCP server, warehouse, Sheets or CSV |
Sources for the cost and reliability figures: browser agents vs. scrapers.
You pay for a feed that keeps working. Not for hours, seats or tokens.
How pricing works →Hand off the scraping.
Keep the data.
We build, run and repair the pipeline. Your team gets rows in a schema that doesn't move, and a person to call when something looks off.
- Fixing broken selectors
- Rotating proxies
- CAPTCHA whack-a-mole
- Babysitting agent sessions
- Silent empty columns
- Diffing CSV exports
- Explaining gaps to the data team
Four ways to get the data.
Not sure which fits? Describe the data and we'll tell you the cheapest way to get it, even if that isn't us.
Ask us →- Managed data feeds Prices, listings, jobs, reviews and company signals from one source or hundreds. Scheduled, de-duplicated, versioned, and repaired when sites change.
- Web monitoring Competitor pricing pages, changelogs and policies. You get the exact change and a plain-language summary.
- Custom datasets One-off research datasets, plus AI training, eval and RAG corpora with full provenance.
- Company enrichment Company records filled in from websites, registries and hiring and tech signals, with a source per field.
Then we put agents on it.
Most agent pilots stall on data that is stale, incomplete or impossible to check. Ours start with data that is none of those things. We map the workflow, write the evals from real cases, then build the agent in your stack and stay to run it if you want.
- Agent discovery One week to map a workflow, audit its data and build a first eval set from real cases. Ends in a fixed-scope proposal.
- Build sprint The agent, its tools and its data layer, built in your stack and measured against those evals on every change.
- Forward-deployed engineer For bigger programmes: an engineer inside your repos and channels, backed by our agent fleet, shipping with your team.
- Agent-ready data and operations Your data served to agents over MCP or an API, and the agents we built run, monitored and fixed under an SLA.
Agents build. Code runs. A QA gate decides what ships.
The full process →- 1.
Scope
We map the sources, the fields and the most reliable way to get each one, and send you a fixed quote.
- 2.
Agents build
Coding agents write extractors against your schema and test them on live pages. An engineer reviews every change.
- 3.
QA gate
Every run passes deterministic checks, then a model from another family spot-checks a sample. Failures go back, not out.
- 4.
Run and monitor
Plain code runs the feed on schedule. When a site changes we are alerted, an agent drafts the fix, a person approves it.
Then, on every run
-
Fetch
Through the route chosen for that source.
-
Extract
Plain code reads each field from the page.
-
Check
Schema, volume, empties, duplicates, spot check.
-
Deliver
To your API, MCP server, warehouse or files.
Proof, not testimonials: the same pipeline runs our own dataset.
Nomad Watch tracks 3,169 cities, 6,794 embassies and 3,625 tax treaties. Agents research it, a lint gate and a cross-model judge check it, and every run lands as one reviewable change. We publish the pass rates, including the batches that failed.
Read the case study- 7,068
- agent runs in four weeks
- 3,463
- research tasks completed
- ~39,600
- dated source citations
- 6,794
- embassy records, all sourced
What you get that a scraper script doesn't.
- Fix-time SLA
- When a source breaks, the clock starts. Daily feeds carry a one-business-day fix target.
- Versioned schema
- Fields never change silently. New versions are announced and the old one keeps running.
- Freshness on every row
- Each value carries its retrieval time and source URL, so stale data is visible, not hidden.
- Reviewed changes
- Agents propose extractor fixes as reviewable changes. Nothing ships unreviewed.
- No invented values
- Language models never fill in data. Every field is observed on a page or marked as derived.
- Monitoring you can see
- Row counts, empty fields and layout drift are tracked on every run. You see what we see.
Fixed quotes. No usage bills.
A monthly price per feed that covers running and repairing it, plus setup. Both quoted in writing with the free sample; normal access costs included.
Single feed
Per source, per month
One source, refreshed weekly or daily, repaired when the site changes.
- Setup quoted up front, after the free sample
- Versioned schema and change log
- Breakage monitoring and repair
- CSV, JSON, Sheets or warehouse delivery
- Source URL and timestamp on every row
Team
RecommendedMonthly, several sources
A handful of sources on a daily refresh, with monitoring and agent delivery included.
- Everything in Single feed
- Daily refresh with a fix-time SLA
- Web monitoring with AI change summaries
- Hosted MCP server and API for your agents
- Shared Slack or email channel
Scale
Quoted per project
Many sources, high volume or hourly data, with contracts to match.
- Everything in Team
- Hourly or near-real-time feeds
- Premium access routes passed through or included
- Custom SLAs and data contracts
- Quarterly source and schema review
Founding customers: our first customers get 35% off for six months, or off their first agent build, in exchange for a case study. Details
Questions we get
Something else? Ask us directly.
Why not just ask ChatGPT, Claude or another browser agent to collect this?
For a one-off list of a few hundred rows, do that. It's cheaper. Agents get expensive and unreliable when the job repeats: on live-web benchmarks they finish roughly 30–60% of tasks, they cost real money per page, and they don't tell you when a site changed. We use agents to build and repair pipelines and plain code to run them.
What happens when a site blocks bots?
We pick the most reliable route per source: an official API or feed, open pages, licensed or paid access, or engineered collection through specialist infrastructure. If a source cannot be collected reliably, we say so before you pay.
How do you prevent hallucinated data?
Language models never generate values. Extractors read fields from pages, every value carries its source URL and timestamp, and each batch passes deterministic checks plus an independent model spot-check before delivery.
Can you deliver straight into our AI agents?
Yes. Any feed can be served as a hosted MCP server or REST API with narrow, documented tools, so ChatGPT, Claude, Cursor or your own agents can query it. It is included from the Team plan.
Do you build AI agents too?
Yes, on top of the data. We start with a one-week discovery that maps the workflow, audits its data and builds an eval set from real cases, then build the agent in your stack at a fixed price and can run it afterwards. If a rule or an off-the-shelf tool would do, we say so.
How fast can a feed go live?
The free sample takes 2 business days. Most single-source feeds go live within a week of approval; multi-source feeds in two to three weeks.
Who owns the data?
You do. You get the data, the schema and the source list. If you leave, we hand over the extractor documentation and a final export.
Get a free 100‑row sample
Tell us what you need to track. Within 2 business days you'll get a sample, the sources we'd use and a fixed quote, or an honest note that an off-the-shelf tool will do.
- Sample and fixed quote in 2 business days
- Source URL and timestamp on every row
- No commitment, no sales sequence
Prefer email? [email protected]