What exactly does SearchCandy Labs sell?

TGM, a managed retrieval layer that organizes your documents into a file-level graph and returns complete passages with their source file and version, rather than anonymous text chunks.

How do I get an account?

By booking a call through the website. After the call the team provisions your account and emails you a login link. There is no public self-serve signup.

Does it call an LLM to do the retrieval?

No. The vendor states retrieval runs without LLM calls, which is why there are no per-query LLM bills.

Can I use my own embedding model?

Yes. The service is hosted by SearchCandy Labs but supports bringing your own embedding model, and a Python library connects your AI application to it.

Search Candy Labs의 월간 트래픽과 성장 추세는 어떠한가요?

Search Candy Labs은(는) 현재 월 0회 방문을 기록 중이며, 전월 대비 트래픽 변화량은 -52(성장률 -100%)입니다. 주요 유입 시장은 글로벌(점유율 —)이며, 여기에는 월 직접 방문 0회와 월 자연 검색 방문 0회가 포함됩니다.

Search Candy Labs은(는) 무료인가요? 가격 모델은 무엇인가요?

Search Candy Labs은(는) 가격 문의 가격 모델로 운영됩니다. Contact for pricing. There is no public self-serve price list; onboarding starts with booking a call, after which SearchCandy Labs sets up the account. Hosting is included in the managed offering. 지원되는 결제 및 체크아웃 플랫폼에는 등록되지 않음 등이 있습니다.

Search Candy Labs — 월간 트래픽 (0/월), 가격, API 및 대안 비교

Open AI Tools

Search Candy Labs — 월간 트래픽 (0/월), 가격, API 및 대안 비교 | Open AI Tools

Search Candy Labs 소개

File-level document graph

TGM groups content by file rather than slicing everything into uniform chunks, keeping short files intact and splitting long ones at sentence ends.

Version-aware retrieval

Every version of every file is retained, so you can query what your AI would have seen on a specific past date and get the same passages back.

Passages with provenance

Each result arrives with its source filename and version attached, turning 'where did that answer come from?' into something you can check rather than guess.

Two-step lookups for chained questions

For questions that need two facts, TGM re-searches using the first finding to locate the second, rather than stopping after a single pass.

No LLM calls in retrieval

Retrieval runs without invoking a language model, which eliminates per-query LLM bills and keeps latency predictable.

Managed hosting plus Python client

The service is hosted for you, and a Python library wires it into your existing AI application; bring your own embedding model if you prefer.

SearchCandy Labs builds TGM, a retrieval layer that organizes your documents into a graph instead of a pile of chunks. Every passage your AI receives comes with its source file and version attached, so answers stay traceable and reproducible.

Search Candy Labs의 임팩트 높은 활용 사례

AI engineer

Replace brittle chunking pipelines

If you have been tuning chunk sizes and overlap by trial and error, TGM removes those knobs entirely and organizes files on its own.

Legal and compliance teams

Trace every answer to a document version

Because passages carry their file and version, you can show exactly which contract wording supported a given answer and on which date it applied.

Support operations

Ground a help-desk assistant in your own docs

Point TGM at handbooks and policy files so a support bot cites the real policy text instead of paraphrasing from a shredded fragment.

RAG builders

Answer multi-hop questions

For questions that require linking two facts across documents, the second-look search retrieves the connecting passage that a single pass would miss.

Search Candy Labs 대안 및 경쟁사 베스트 12선

동일한 주요 카테고리에 속한 경쟁 AI 도구의 나란히 보는 텔레메트리 비교

Search Candy Labs 대안 전체 보기 →AI 제품 설명 생성기 도구 전체 살펴보기 →

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●	Search Candy Labs	0	-100%	—	가격 문의	현재 도구
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