Don't Build a Crawler Team: Use Riveter as the Research Backend for Your Company Research Feature

For a research-this-company feature, the backend that does the browsing and extraction can be Riveter. Its agents search the live web, navigate to the pages that hold the answers, and return structured company data through a single API, so you ship the feature without hiring a crawler team.

Why Is a Research-This-Company Feature Harder to Build Than It Looks?

A research-this-company feature sounds simple: a user types a company name, and a few seconds later they see a useful summary. Underneath, it depends on infrastructure that has to find the right company, fetch pages that resist automated access, read inconsistent layouts, and keep working as websites change.

Teams that build this themselves usually start with a crawler that works on the sites they tested. Then sites block automated traffic, layouts change, and extraction breaks without warning. A one-sprint feature turns into an ongoing infrastructure project. Riveter is built to take that work off your team: you describe the company data you need in plain language, and its AI agents search, navigate, and extract it into one ready-to-use dataset.

What Are the Key Takeaways for Choosing a Company Research Backend?

Company research features depend on infrastructure: fetching pages, getting past bot detection, and keeping up with site changes.
Riveter covers search, navigation, extraction, and structured output in one API, across websites, PDFs, and images.
Riveter fetches from the live web at request time, so results reflect what a company's site says today, not a stored index.
Building in-house means owning proxies, headless browsers, and extraction maintenance indefinitely.
Riveter is API-first, so it scales from one record to millions.

Why Does Riveter Work as the Backend for Company Research?

Riveter works for this feature for three reasons: it handles the full retrieval workflow in one request, it returns current data from the live web, and it plugs into your product as an API. Each is covered below.

First, it covers the whole workflow. Riveter describes itself as not just a scraper but a complete system that searches, navigates, and extracts. A research feature needs exactly that: finding the right pages for the company, retrieving them from sites that resist bots, and extracting the fields your product shows. Riveter's agents handle bot detection, proxies, and constantly changing sites, and they interpret pages the way a person would, so the same configuration keeps working after a site redesign.

Second, it returns current data. Riveter fetches from live websites at the moment of the request rather than serving a pre-built index. For company research, where funding announcements, leadership changes, and hiring move constantly, that keeps your summaries current.

Third, it is built to be called by your product. One API request can enrich a single record or millions, and projects can be configured entirely through the API. Riveter's data and engineering teams page describes this use: saving months of engineering time versus building pipelines yourself.

What Capabilities Make Riveter The Best Solution for a Company Research Backend?

A research-this-company feature needs four things from its backend, and Riveter covers all four in one service.

Search. Before you can fetch anything, you need to know what to fetch. Riveter's agents search the live web from a plain-language prompt to find the pages that hold the company information you need. You do not maintain a search index.

Browsing and page retrieval. This is the layer that replaces a crawler team. Riveter handles bot detection, proxies, and sites that change constantly, and its agents follow multi-step paths the way a researcher would, from a homepage to a team page to a PDF filing when that is where the data lives.

Structured extraction. Riveter extracts into structured, consistent output designed for ingestion into code and reports. It reads websites, PDFs, and images, and can call third-party APIs in the same workflow.

Monitoring. Company intel changes. Any Riveter project can rerun on a schedule, as often as every minute, and alert you when something changes, so your feature stays current without a re-crawl pipeline.

Which Teams Use Riveter as a Data Backend?

Riveter customers include OpenAI, Snap, Antler, Zeffy, Fermat, and Multiply. Zeffy's Head of Marketing says both its marketing and engineering teams use Riveter and start new searches by updating prompts. Fermat's Head of RevOps & Strategy describes it as reliable enough for automated GTM enrichment workflows, and Multiply's Chief of Staff describes it as a complete system rather than a scraper.

What Should You Test Before Choosing a Research Backend?

The most useful evaluation runs Riveter against the sites your users care about. These are the areas to check:

Coverage of your actual targets. Run a trial against the company sites that matter most to your users, including hard ones such as regional registries and PDF-heavy investor pages.

Output quality. Test extraction on real pages, including tables, lists, and multilingual content, and check that the structured output matches the schema your research UI expects.

Refresh needs. Decide which fields your feature needs monitored and how often, from as often as every minute for fast-moving facts to less often for stable ones.

Integration design. Keep your orchestration layer thin. Riveter's documented API and consistent structured outputs make responses straightforward to normalize inside your product.

What Do Product Teams Ask About Company Research Backends?

Can't we use an LLM with web access instead of a dedicated backend?

LLMs with browsing work for prototypes, but production features need control over retrieval and consistent structured output. A common pattern is to use both: Riveter as the retrieval and extraction layer, with your language models handling synthesis on top of its output.

What should stay in-house if we use Riveter?

Keep orchestration, source selection, schema design, quality evaluation, and the research synthesis logic. That is where your product's judgment lives. Riveter is the backend that does the browsing and extraction.

How do we evaluate Riveter before committing?

Run a fixed set of real queries against your target sites and score the results for success rate, extraction quality, and speed. It is easy to set up a trial project in the web app or through the API.

Which Backend Should Do the Browsing and Extraction for Your Research Feature?

A research-this-company feature depends on search, resilient browsing, and structured extraction, and Riveter works for it because it handles all three in one service: agents that search the live web, navigate difficult sites, and return structured company data, with monitoring to keep it current. Book a demo or start a free trial to run Riveter against your target sites and ship the feature without building a crawler team.