Deploy a customer-facing AI assistant on infrastructure dedicated to your company. It continuously tunes its own retrieval, shows you every decision it makes, and runs close to your users on a global edge network. No prompt engineer needed.
A RAG assistant is only as good as the day it was tuned. Your product keeps moving, and answer quality decays unless someone keeps tuning it. Most teams don’t have that person.
Same corpus, same questions, graded weekly. Continuous optimization is the difference between a line that climbs and one that decays.
You rename a product. Users search for the new name. The index still speaks last year’s language.
Organizations add content. They almost never remove it. The model surfaces the confident, authoritative, outdated answer.
Every document you add competes for the same retrieval slots. More knowledge means worse retrieval.
The model isn’t hallucinating. The answer is in the context. It just can’t find it.
Connect your documentation, shape a retrieval pipeline, and ship it to the edge. From there SearchCrucible tunes and watches itself.
Point SearchCrucible at your technical docs: sitemaps, Notion, Google Drive, PDFs, Backstage, Zendesk and more.
Start from an accuracy preset, or open the node editor to shape retrieval, reranking and generation yourself.
Ship a pipeline version and it rolls out to your Rust edge worker, everywhere at once.
From there SearchCrucible runs the tuning loop for you, analyzing chunking and prompts and keeping what scores better. Nothing to re-tune by hand.
See the loopEvery query is traced end to end, with quality metrics over time and the gaps in your docs surfaced for you.
See the traceSearchCrucible runs the tuning loop you’d otherwise hire for. It experiments with retrieval strategies, chunking, and prompts, grades answers against your own questions, and keeps whatever wins. Quality climbs instead of decaying.
Answers are generated close to your users on a globally distributed edge network, not from a single central region. You don't pick regions or run servers. The experience is fast wherever your customers are.
Your assistant runs on infrastructure provisioned for your company alone, not a shared multi-tenant pool. You get the isolation enterprises expect, and we handle the operations.
Your environment runs alone. Separate compute, retrieval index, and data, with nothing shared.
Provisioned for your company alone. No shared pool, no noisy neighbours.
No shared-tenant retrieval layer. Your data and traffic never mix with anyone else’s.
A clear isolation boundary makes the security review shorter and the path easier.
Most AI assistants hide what happens between question and answer. SearchCrucible shows every chunk retrieved, every score, every prompt version, and the full evaluation history. When an answer is weak, you see why.
"…create a second key, deploy it, then revoke the old key once traffic has fully drained…"
"…keys created before v3 must be migrated with `keys:migrate` before they can be rotated…"
"…every request must include a bearer token in the Authorization header…"
# system You answer questions about Acme only from the provided sources. Cite each claim inline. If the sources don't cover it, say so and link the docs. # context {{ retrieved_chunks }} · 2 of 3 used
The optimizer promoted v14 after it beat v13 by +1.4pp on your evaluation set. Every prompt the assistant has ever run is versioned and diffable.
SearchCrucible is still in development. Leave your email and we’ll reach out when early access opens.