ShieldLabs VS Stract API

ShieldLabs's answer

Identification that holds. ShieldLabs recognises a returning visitor with up to 99% accuracy, through cleared cookies, incognito mode, IP rotation and months between visits, because the device identifier is computed from the device rather than stored in the browser. That cuts both ways: the same recognition that exposes one person running twenty accounts also lets you greet a good returning customer without making them prove who they are again.

Anonymity detection with up to 99% accuracy, at any level rather than a binary VPN yes or no:

• Network Intelligence covers VPN, proxy, Tor, Apple Private Relay and datacenter IP.
• Device Intelligence covers anti-detect browsers, browser automation, OS mismatch and undetectable OS.

Each arrives as a separate named signal, so you know which kind of anonymity was found, not just that something was.

The risk score comes apart. Every score from 0 to 100 arrives with the named signals that produced it, so you can see why a visit scored what it did instead of trusting a number you cannot inspect.

Patterns, already correlated. The work across accounts, devices and identities is done before you open the dashboard, pointing to multi-accounting, account sharing, account takeover and account farms operating behind one connection. A starting point for investigation that is waiting when you arrive.

The full detection stack is on every tier. The API and webhooks are included from the first plan, pricing is published, and signup is self-serve all the way through.

Stract API's answer:

Stract API is different because it lets the people closest to the questions get answers straight from their documents, with no templates, no custom tooling and no manual data entry. You upload your files, describe the data you need in plain language, and Stract API runs every document in the batch from start to finish. It returns clean, structured results you can put to work right away. You choose the model that fits the job, from fast, economical options for high-volume work to the newest, most capable LLMs for your highest-stakes analysis. Under the hood, it draws on techniques from the latest AI research to improve the quality of what gets extracted. The same analysis costs less than 10% of the equivalent staff time, so your team can skip the grind and go straight to analysis. And because sensitive work is the norm for many of your users, Stract API is fully SOC2 compliant, so your documents stay yours.

ShieldLabs's answer

Two groups, and they come in through different doors.

Developers and technical founders, most often the CTO at a team of five to fifty. The product is API-first, so whoever evaluates it is usually whoever integrates it. They want the raw signals and intend to write the decision logic themselves rather than buy a service that makes the call on their behalf.

Growth, marketing and analytics people, who may never open the API. Once the snippet is in, the analytics dashboard is theirs: which traffic sources bring anonymized and high-risk visitors, what share of sessions is clean, how it all moves week to week. For anyone buying traffic that is a direct argument about ad spend. For anyone reporting on product metrics it is the difference between counting sessions and counting people.

Installation takes one person about five minutes, and the dashboard needs no code after that, which is why the buyer and the daily user are often not the same person.

By sector it clusters where a free tier, a promotion or a signup carries real marginal cost:

• SaaS and AI products, where usage-based infrastructure bills turn each abusive signup into real money
• Ecommerce and marketplaces, where promotions and first-order discounts get farmed
• Fintech, where a signup is an account that moves money
• Gaming, where one person running many accounts distorts the economy for everyone else
• Crypto and Web3, where airdrops and referral programmes attract farms at scale

It also fits teams with no fraud problem at all. Recognising a good returning visitor is the same capability pointed the other way, whether that means personalising an experience or not asking someone to prove who they are twice.

Stract API's answer:

The primary audience for Stract API is small-to-mid-sized teams, along with enterprises, that run recurring, document-heavy workflows and need to pull consistent information out of growing batches of files faster than manual review allows. These teams are typically staff-constrained, facing rising intake volumes of contracts, case files, forms or survey responses, and working from question lists that keep changing over time. Buyers tend to be operations leaders, legal and contract review leads, insurance claims intake leads, research leads, marketing and CRM operations managers, and engineering teams responsible for pipeline automation. Day-to-day users include the document reviewers who need the extracted information and the developers and analysts who feed that structured output into downstream systems. The audience spans insurance and claims, legal (contracts, discovery and case files), journalism (FOIA releases, court records and meeting minutes), marketing (reviews, support tickets and call transcripts), social science (surveys, interviews and field notes) and academic research. Stract API is a weaker fit for organizations that only need occasional one-off extraction from a handful of documents, or teams committed to strictly template- or regex-driven extraction rather than plain-language field specifications.

ShieldLabs's answer

ShieldLabs came out of four gaps that repeat across this category.

Who the tooling was built for. Everything that reliably identifies visitors and detects anonymity has been priced and packaged for enterprise buyers: a demo to book, a quote to wait for, a procurement cycle to survive. The teams losing the most to free-tier farming, promo abuse and fake signups are small and self-serve, and that path is not built for them.

What anonymity detection had been reduced to. In most stacks it is a side feature: a binary VPN yes or no bolted onto a product built for something else, usually resting on IP reputation lists that go stale as fast as residential proxy pools rotate through consumer addresses. Anti-detect browsers, the working tool of anyone doing multi-accounting at volume, are rarely a first-class signal at all.

Explainability. Most detection products return a number. When a paying customer trips over that number, the team has nothing to inspect and nothing to tell the user.

The work that begins after the purchase. A product that hands over raw signals has moved the problem rather than solved it, because someone still has to build the model, correlate across accounts and devices, and keep the whole thing current. Time to a first useful answer gets measured in weeks.

So the product was built around four commitments:

• Anonymity as the product, not a checkbox. Device and network signals together, up to 99% accuracy in detecting anonymity from a clean visit through VPN, proxy, Tor and Privacy Relay to anti-detect browsers and browser automation, each surfaced under its own name.

• Pricing published, stack whole. The full detection stack on every tier, self-serve from the first plan through the top one, so the path from landing page to first signal runs inside a single session.

• Every score decomposable into the named signals behind it, so the customer's code owns the decision while ShieldLabs owns the evidence.

• Usable on day one. One JavaScript snippet, about five minutes to the first signal, patterns already correlated rather than left as an exercise, and an analytics dashboard that reads without code, so the person who needs the answer is not waiting on the person who can write the query.

Stract API's answer:

Stract API grew out of founder Matt Moreno's repeated frustration with the same problem. At a construction tech startup in Google's accelerator, his team built an LLM feature that matched a crew's plain-language daily report to labor and material options. It worked, but it was fragile. Parsing the output was hard, bad output was constant, and any change to the structure broke it. When he asked a Google director whether a product existed to extract arbitrary structured data from unstructured text, the director said no, but that Google probably should build one. Later, on a venture to automate land title research, he again spent tens of hours building flexible document extraction. That work pulled him away from the core mission, and he wished a third-party service would handle it. When he had time this summer (2026), he found few existing solutions. Remembering the director's words, he built Stract API, which took five months to reach MVP. It lets users describe the information they want, from simple facts to judgment-heavy analysis, and returns a spreadsheet, CSV or JSON from their documents.

ShieldLabs's answer

Depth at a self-serve price. The level of anonymity detection that usually sits behind an enterprise conversation is available on the entry tier here, and API access is included on every plan rather than unlocked further up.

You can be running today. One JavaScript snippet, about five minutes to the first signal, and the whole path from the pricing page to a working integration is self-serve: no demo to book, no sales call, no quote to wait for. Evaluation happens on your own schedule and on your own traffic.

The score is explainable. When a legitimate customer gets caught by your rules, looking up why is a query rather than a guess.

Patterns arrive pre-computed. The correlation work across accounts and devices is done before you open the dashboard.

Traffic quality is a first-class view, not a by-product of scoring. The analytics dashboard gives you:

• an overall risk score for your traffic, and how it moves over time
• the split across Clean, Low, Medium and High risk bands
• the same breakdown per traffic source, so you can see which channels bring anonymized and high-risk visitors

Your analytics tool tells you where traffic came from. This tells you what arrived. For anyone buying traffic, that turns a monthly ad invoice into something you can argue with.

Pricing is published and flat:

• the rate per 1,000 identifications drops as you move up the tiers, so growth makes each identification cheaper
• yearly billing saves 20%
• the free tier is 5,000 identifications with no card and no expiry, enough to measure signal quality on your own traffic before committing to anything

Your code makes the decision. ShieldLabs returns the signals and the score, so the logic specific to your business stays in your codebase where you can change it.

Stract API's answer:

A person should choose Stract API over its competitors because it gets them from a pile of documents to usable data with the least effort and risk. They describe what they need in plain language, so they don't build templates or learn a tool's vocabulary. They can use the point-and-click UI or the API for automated workflows, so the same product works for a one-off project and for a recurring pipeline. They also keep control over the tradeoff that matters most to them. They pick the model for each job, from fast, economical options for high-volume work to the newest, most capable LLMs for high-stakes analysis, instead of paying top-tier prices for everything. The extraction itself uses techniques from the latest AI research to improve quality. The cost is under 10% of the equivalent staff time, and results come back fast even on huge batches. Finally, Stract API is fully SOC2 compliant, which matters to teams handling case files, sources and survey data. Together, that means a team clears its "Needs Attention" pile and gets to analysis faster, without trading security or depth for speed.

ShieldLabs's answer

What you integrate is deliberately small. One ES module loaded from the CDN, and everything after that arrives through a REST API and signed webhooks. It assumes nothing about your stack.

There is an install guide for whatever you already use: JavaScript, React, Next.js, Vue, Angular, Svelte, Preact, React Native WebView, plus WordPress, Shopify and Tilda.

On WordPress, Shopify and Tilda that means no developer at all. The module goes where the platform already keeps custom code, and that is the whole integration.

Nothing to maintain afterwards. Detection keeps improving without you shipping an update, so what you install today gets better on its own.

Stract API's answer:

The primary technologies used in build Stract API are LLMs, Typescript, React, and Java.

Stract API's answer:

Legal services and title companies are growing segments for Stract API