Underlayer VS Stract API

Underlayer's answer

it's pre-launching

Stract API's answer:

Legal services and title companies are growing segments for Stract API

Underlayer's answer

Underlayer is training with no portal. You build a course by API, by MCP, or in the visual builder, and add it to your product with one iframe. Then signed webhooks tell your systems who finished. Learners never create an account or leave your app, and the player uses your theme, so it looks like part of your product. We charge per course view, not per seat, and every price and overage rate is published.

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.

Underlayer's answer

Most course tools, like Coassemble, Trainn and LearningStudioAI, assume training happens on their own site: learners sign up, log in and get enrolled, and you pay per seat. Underlayer takes the opposite approach:

No learner accounts. You pass in your own user IDs and we track progress against them. Built for developers. A full REST API covers every resource, and an MCP server lets AI agents create and edit courses. Results in your own tools. Signed webhooks send completions to your CRM, your product or your data warehouse. SCORM 1.2 and 2004 export for customers who still need to load courses into an LMS. Public pricing. Plans start at $149/month for 25,000 course views, with unlimited learners and no sales call required.

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.

Underlayer's answer

SaaS product, customer success and engineering teams who want onboarding, product education or compliance training inside their own app. It also suits platforms that train other people's users, like partner networks, marketplaces and field-ops tools, where a separate LMS login gets in the way.

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.

Underlayer's answer

Every company putting training in front of its users hit the same wall: the LMS wanted to own the learner. That meant a separate portal, a second login and a per-seat bill for people who never opened a course. We built Underlayer as the layer underneath instead. You keep the product and the user relationship. We handle building the course, playing it and tracking progress, and we stay out of the way.

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.

Underlayer's answer

Next.js 16 (App Router) with React 19 and TypeScript, Tailwind CSS v4, and Supabase (Postgres with row-level security, Auth and Storage). Claude from Anthropic generates courses with AI. Stripe handles billing and Resend sends email. There is also a remote MCP server with OAuth 2.1 for AI-agent access.

Stract API's answer:

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