Stract API VS LayerCall

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.

LayerCall's answer:

Most fraud APIs answer one question per call — is this IP a VPN, is this email disposable, is this phone real. LayerCall returns IP, email, phone, domain and device together, and scores the relationships between them. A brand-new domain paired with a datacenter IP and a throwaway mailbox is obvious in combination and unremarkable one field at a time.

Every response also carries the reasoning: a 0–100 risk score, an allow / review / block verdict, and the individual signals behind it, so a decision can be explained rather than only made.

It treats AI agents as a first-class case as well. Web Bot Auth signature verification establishes which agent is calling and whether it can prove it, and a policy engine decides what it is allowed to do — a question classical fraud signals cannot settle, because an agent arrives with a real browser, a real fingerprint and a real mailbox.

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.

LayerCall's answer:

Because of what comes back in the response, not what it costs.

Every result carries a 0–100 risk score, an allow / review / block verdict, and the individual signals behind it — so a decision can be explained to a customer, a colleague or an auditor rather than only made. Strictness is tunable per request without re-scoring, which means the same integration can be strict at signup and forgiving at login.

Two smaller things tend to matter more in production than they sound. When a data source is unavailable, the response says so instead of quietly scoring lower, so an incomplete answer stays distinguishable from a clean one. And test keys return fixed, fictional data that never bills and never touches live reputation data, so a test suite can assert on exact values without polluting anything.

Beyond that, it is worth comparing directly rather than taking our word for it: the live demo runs the real scoring engine with no signup, and the free tier needs no card.

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.

LayerCall's answer:

Developers and small product teams who need a trust decision at signup, login or checkout, and who would rather call one endpoint than integrate several vendors and reconcile their answers by hand.

In practice that means SaaS signups, marketplaces, fintech onboarding, and anyone whose free tier is being farmed by throwaway accounts.

A newer part of the audience is teams who suddenly have to decide what an AI agent may do on their site. That is a different question from classical fraud — an agent can be entirely legitimate and still need a policy — which is why agent verification sits in the same API rather than in a separate product.

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.

LayerCall's answer:

It started from a specific frustration: the signal that actually catches a fake signup is usually a relationship between fields, and the tools available answered one field at a time.

Blocking disposable email domains stops very little on its own. The signups that matter use real mailboxes, often on domains registered days earlier, arriving from addresses that look entirely ordinary. What gives them away is the domain's age set against the IP's provider set against whether the phone is a VoIP line — and assembling that meant several vendors, several response shapes, several bills, and writing the correlation by hand anyway.

LayerCall is that correlation as a product: one call, every signal, and the reasoning returned next to the score.

The AI-agent side came later, from the same observation in a new place. An agent has a real browser, a real fingerprint and a real mailbox, so nothing in a classical fraud stack has an opinion about it. What you need to know is which agent it is and whether it can prove it — a signature problem, not a fraud-signal problem.

Stract API's answer

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

LayerCall's answer:

TypeScript on Next.js, running on Vercel's Fluid Compute, with Postgres (Supabase) behind accounts, keys and usage.

The scoring path is deliberately boring. No third-party SDK sits in the request path; every external feed is fetched under its own timeout inside a request-wide deadline, so one slow source cannot hold up a response. A feed that fails degrades the result rather than failing the call, and the response names any signal that was unavailable so the caller can tell the difference between a clean answer and an incomplete one.

On the client side: official Node/TypeScript and Python SDKs, Express and Next.js middleware, a published OpenAPI spec, and an MCP server so AI tools can call the API directly.

Stract API's answer

Legal services and title companies are growing segments for Stract API