Production RAG in one call.
A question goes in. A grounded, cited answer comes out. The retrieval pipeline between them (chunking, embedding, indexing, reranking, synthesis) is Voxell's problem now, not yours.
already embedding? → your corpora
want this without an API? → Spaces publishes it as a page instead
Agents need answers. RAG stacks return chunks.
Every agentic pipeline that touches a vector database inherits the same build: a chunking strategy, an embedding bake-off, index provisioning, hybrid-search weights, a reranker, context assembly, citation plumbing, and an eval harness to prove any of it works. Each stage is latency, cost, and failure surface inside your agent's loop, and none of it is your product.
Answers is built for the pipeline that calls it: deterministic JSON, citations your agent can verify before it acts, and a grade on every source, so confidence is a number, not a vibe. Ask with mode=vectors when the agent wants raw chunks; mode=answer when it wants the finished object.
The retrieval stack, replaced by one endpoint your agent calls as a tool.
Forge vectors in. Grounded answers out.
The #1 model does the reading
Forge encodes your corpus and every query with Ingot-8B-R3, first on MTEB English v2. Retrieval quality is the ceiling on answer quality. We set the ceiling.
Edge speed or Max precision
Standard corpora are edge-served for latency. Max corpora run precision retrieval on Voxell GPUs. Both return the passages that actually answer, ranked.
The finished object
Retrieved passages become one grounded response with citations attached, ready for an agent to verify and act on, not a context window to babysit.
Sovereign inference
rolling outEmbedding and retrieval already run end to end on Voxell-operated infrastructure. Generation joins them next: a sovereign model on Voxell GPUs, so a query and its corpus never transit a third-party model API. One trust boundary, from document to answer.
Bring the corpus you already have.
Migration starts with an assessment, not a rewrite. Point Answers at your existing documents; Forge ingests them and VQS grades every passage for how well it can support an answer.
You see which sources are weak before your agent finds out in production. Then fix, replace, or drop them with evidence instead of guesswork. Proof, not vibes.
VQS per passage, per corpus. The weak third of most corpora is where wrong answers come from.
Pricing that doesn’t tax idle data.
You pay to embed a document once, then a flat toll per query. Not monthly rent on every vector sitting in storage, so a corpus you rarely ask about costs about nothing to keep.
That means the bill tracks how much you use the corpus, not how big it got. See what’s included →
Why not just paste it into a chat?
For one document and one question, a chat window is fine. It falls apart the moment you have more documents than fit in a prompt: a support team with 50,000 articles, a contract set spanning years, a product answering from a live knowledge base. You cannot paste all of it into a chat.
Answers pre-processes your whole corpus into searchable meaning once, then retrieves only the few passages that matter when a question arrives. The model sees the right context instead of everything, so it is faster, cheaper, and it scales. And because retrieval quality is the ceiling on answer quality, it runs on embeddings that are benchmarked rather than assumed.
Team plans pool seats, not accounts.
Every seat — standard or premium — includes both Answers and Spaces, with usage pooled across the org, single sign-on, and central billing. Self-serve, seats activate instantly. Enterprise adds SAML SSO with SCIM provisioning, audit logs, and private networking on top. See Team pricing →
First answer in five minutes.
Create a key, post a document, ask a question. The first call is the demo.
Get an API key$ curl https://api.voxell.ai/v1/answers/{corpus}/query · free at Turbo