by TypeGen (LX AI)
TypeGen is a focused studio for developers who need TypeScript types from messy samples. Paste JSON or CSV, choose interfaces, type aliases, or a Zod schema, and receive a draft plus assumption notes and ambiguity flags. The product exists for the common gap between "we have an example payload" and "we have a reviewable contract."
Key takeaways
Modern stacks talk in TypeScript, but partners still speak in ad-hoc JSON. Analytics exports still arrive as CSV. Internal services evolve fields without updating shared packages. Engineers burn hours reconstructing shapes by eye, then discover in staging that a field was sometimes null.
Full contract-first programs (OpenAPI first, codegen second) remain the gold standard when you control both sides. Many teams do not. TypeGen is for the transitional cases: you need a credible first draft today, with enough structure that a reviewer can challenge assumptions instead of starting from a blank file.
A TypeGen run is not just a blob of types. The useful unit of work includes:
0/1 as boolean candidates.That separation matters. Rule-based hits should never pretend to be live AI. Live AI should never pretend to be a silent success when the key is missing.
Platform and API teams typing partner webhooks and third-party responses. Speed matters when onboarding a new integration weekly.
Agencies that inherit client CSV dumps and need client-facing TypeScript apps without a week of typing toil.
Indie builders who want a repeatable studio instead of reinventing a private script every time a sample changes.
Who should not rely on it alone: regulated industries that need certified schemas, teams with living OpenAPI already generating types, or anyone expecting a tool to replace human review of security-sensitive payloads.
Versus hand-writing types: faster first draft, weaker domain insight. Always keep humans for naming, branding of domain types, and intentional widenings.
Versus OpenAPI codegen: weaker when a complete spec exists; stronger when only samples exist. Do not replace a healthy contract pipeline with sample inference.
Versus ad-hoc ChatGPT pastes in a browser tab: TypeGen keeps a productized path with quotas, demo labeling, checkout, and fail-closed status codes. That operational honesty reduces the chance someone mistakes a mocked reply for a live model.
The differentiator is not "AI magic." It is a disciplined draft with assumptions you can audit.
Free tier covers light exploration with limited daily runs and watermarked export. Pro is $19 per month or $190 per year — annual billing shows a monthly-equivalent price and savings, and checkout uses the same cycle you selected. Enterprise can add BYOK, DPA/NDA, and higher caps.
Checkout is always dynamic via /api/checkout. Session links expire; they are never hard-coded into marketing pages.
If you explicitly request demo mode, you get a labeled demo. If no API key is configured, you get HTTP 503 with AI_NOT_CONFIGURED. If quota is exhausted, you get 429 before any model call. If the upstream model fails, you get 502. Successful live runs return source: "Model-assisted".
This matrix exists because silent mock under HTTP 200 is a trust failure. Typing tools feed production code; lying about the source of a draft is worse than refusing the request.
Sample data is used to produce your result. Minimize secrets in pastes. Retention is intentionally short; see the Privacy page for rights and subprocessors. Hosting runs on Vercel; payments go through Waffo Pancake as merchant of record.
TypeGen is not a compliance certification product. Security page honesty notes apply: decision-support, not legal advice, no guarantee of complete coverage.
Integrations marketing only lists what ships today: web studio, export of structured results, REST-backed tool API, and Waffo checkout. Connectors that are not built are not advertised as available. If you need a specific export target, ask — demand drives the queue, not vapor feature lists.
Open the studio, paste a redacted sample, pick a style, and run. Read the assumptions. Resolve the flags. Copy the draft into a PR with a short note about what you verified and what remains unknown. That workflow is the product.
For deeper technique, read the companion how-to on inference pitfalls. For policy language, see Security and FAQ. For plans, see Pricing with monthly and yearly cycles kept in sync.
First, label the source of every generative answer. Demo is demo. Model-assisted is model-assisted. Rule-based checks say rule-based. Mixing those labels to look smarter is forbidden.
Second, fail closed when the live path cannot run. A red error teaches operators to configure keys and quotas. A quiet fake 200 teaches them to trust garbage.
Third, keep checkout dynamic. Payment session URLs expire. Marketing pages link to /api/checkout with an explicit cycle so the price you see is the price you pay.
Fourth, refuse efficacy theater. We will not claim that TypeGen guarantees perfect types, eliminates review, or replaces legal counsel. Decision-support is the ceiling of the claim.
Morning: a partner emails a new webhook example. An engineer redacts tokens, pastes into TypeGen, selects interfaces, and saves the draft beside the ticket.
Midday: a second sample arrives for the failure path. Re-run. Diff the types. Update the PR description with which ambiguities remain open.
Afternoon: reviewers rename vendor fields into domain types, add a Zod parse at the HTTP edge, and attach two fixtures to the test suite. The studio draft is gone from the critical path; the reviewed types remain.
That is the intended loop. TypeGen is the morning accelerator, not the afternoon source of truth.
Client sends a CSV of subscribers. The agency lead pastes a sanitized slice, chooses Zod, and exports a schema the junior developer can validate against. They schedule a call to confirm empty-cell semantics before locking optionality.
Because agencies juggle many clients, the value is consistency: same studio, same honesty labels, same checkout for their own Pro seat — not a pile of one-off notebooks.
Useful internal metrics: time from sample receipt to first reviewed PR, count of ambiguity flags resolved before merge, rate of production type mismatches after adoption.
Vanity metrics we avoid: "types generated," "AI accuracy percent," or any claim that a single score proves correctness. Sample representativeness dominates accuracy; a score without that context misleads buyers.
In-product AI Help answers from a product knowledge base with guardrails that escalate refund and human-request phrases. Feedback widgets capture structured reports. Admin review requires the typegen_token cookie for authorized emails.
Email remains lixingliangsy@163.com. Hub navigation returns to lxsaihub.com so users can discover sibling tools without dead ends.
If you evaluate TypeGen for purchase, ask three questions. Does the demo path label itself? Do error paths refuse to fake success? Does yearly pricing sync the checkout cycle with the displayed monthly-equivalent? Those questions tell you more about operational trust than a feature checklist.
Then try one real redacted payload from your backlog. If the assumption notes surface a debate your team needed to have, the tool earned its place. If you hoped it would end debate forever, you wanted a different product — and we will not pretend to be that product.
Finally, remember that TypeScript types are a social contract inside your repository. Tools can draft the contract from samples; teams still negotiate the meaning. TypeGen shortens the drafting step and keeps the negotiation honest.
Modern stacks talk in TypeScript, but partners still speak in ad-hoc JSON. Analytics exports still arrive as CSV. Internal services evolve fields without updating shared packages. Engineers burn hours reconstructing shapes by eye, then discover in staging that a field was sometimes null.
A TypeGen run is not just a blob of types. The useful unit of work includes:
Platform and API teams typing partner webhooks and third-party responses. Speed matters when onboarding a new integration weekly.
Versus hand-writing types: faster first draft, weaker domain insight. Always keep humans for naming, branding of domain types, and intentional widenings.
Free tier covers light exploration with limited daily runs and watermarked export. Pro is $19 per month or $190 per year — annual billing shows a monthly-equivalent price and savings, and checkout uses the same cycle you selected. Enterprise can add BYOK, DPA/NDA, and higher caps.