Spend less on AI agents. Keep the context that makes them work.
Zerix prepares long, repetitive context before your model sees it—prioritizing critical facts and brittle artifacts, then showing the verified savings on every request.
ลดค่าใช้จ่าย AI Agent โดยยังเก็บบริบทที่ทำให้งานสำเร็จ — Zerix จัดบริบทที่ยาวและซ้ำซ้อนให้ฉลาด พร้อมแสดงยอดที่ประหยัดและตรวจสอบได้ในทุกคำขอ
Customer Context
messages + code + budget target
Preserve
Code and brittle spans stay byte-identical
Prepare
Repeated prose is handled with care
Return
Prepared context returns; your provider key stays in your app
Downstream LLM Response
answer + _zerix_metrics receipt
presented_messages
tokens_in
tokens_out
tokens_saved
quality_score
tier_used
Long context needs a control layer
Every model has a context window limit. When you approach it, teams need a repeatable way to compress safe prose while preserving brittle content.
Facts get buried
Long-running sessions make decisions, constraints, and citations harder to keep visible.
Context Gets Heavy
Large prompts increase downstream work and make every request more expensive to inspect.
Bills become hard to explain
Sending everything on every turn hides which context was useful and which was noise.
Illustrative context load over time
Context in. Cleaner context out.
Zerix prepares context, never replaces your model, and returns the result to your app. Your app calls the model you choose.
Preserve
Detect code, structured data, facts, and brittle spans that must remain intact.
Compress
Condense safe prose through the engine boundary and prepare a smaller presented context.
Return
Return prepared context to the customer's app. Their app calls the selected model with a provider key that stays customer-side.
Show the value without guessing.
Zerix avoids fixed public savings promises before attestation. The live product shows token movement and cost basis from each request receipt.
Every serious claim comes from a receipt
The dashboard can calculate customer-specific ROI after a real request runs. Public pages stay honest and label demo math as illustrative.
Savings, quality, latency, and ROI benchmarks remain pending attestation until verification is complete.
Who Is This For?
Like compressing a 100-page meeting transcript into 30 pages while keeping every decision intact. Your AI remembers the important stuff, forgets the filler.
Solo Developers
Building AI apps with expensive model tiers. Test whether long context can be reduced without changing your product surface.
Startups
Shipping AI features fast. Zerix fits into an existing server-side chat request path with minimal product disruption.
Enterprise Teams
Running heavy model workloads. Long conversations, retrieval pipelines, and support histories are the best first tests.
When It Helps Most
- Long conversations (50K+ tokens) — this is where the biggest savings are
- RAG systems with large retrieved context
- Customer support chatbots with conversation history
- Agent workflows that accumulate tool outputs
- Any app where context grows over time
When It Might Not Help
- Short one-shot queries under 5K tokens — limited room for savings
- Tasks requiring exact word-for-word recall of every message
- Conversations that are already short and focused
Attestation Status
Public savings, quality, latency, and ROI benchmark numbers are pending attestation. The demo surfaces use per-request metrics returned by the API instead of fixed marketing claims.
Built for every AI workflow
Any application that sends context to an LLM benefits from Zerix.
AI Chatbots
Conversations degrade after 50+ turns. Zerix keeps quality high across thousands of messages.
RAG Pipelines
Retrieved documents are noisy. Zerix filters to what matters, boosting answer accuracy.
Multi-Agent Systems
Agents share context. Zerix ensures only relevant information passes between them.
Customer Support AI
Long ticket histories. Zerix preserves every decision and fact from the conversation.
Code Assistants
Large codebases in context. Zerix prioritizes the most relevant code for the current task.
Enterprise Knowledge
Meeting transcripts, docs, emails. Zerix extracts and preserves what actually matters.
How to Connect
Start with /v1/compress, then call your model from your application with your provider key.
Prepare context with Zerix, then pass the returned messages to the OpenAI SDK from your application.
Request schemanpm install openai
import OpenAI from 'openai'
const zerixResponse = await fetch('https://api.zerix.ai/v1/compress', {
method: 'POST',
headers: {
authorization: 'Bearer' + ' <ZERIX_API_KEY>',
'content-type': 'application/json'
},
body: JSON.stringify({ messages })
})
const prepared = await zerixResponse.json()
// This provider call and key stay in your application.
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })
const result = await client.chat.completions.create({
model: 'gpt-4o-mini',
messages: prepared.messages
})
console.log(result.choices[0]?.message)
console.log(prepared._zerix_metrics)Best for: Startups, solo developers, custom AI apps
The difference is the boundary.
Zerix is not another answer model. It is the context layer before the answer model: compress safe text, preserve what cannot break, and return the receipt.
Direct model call
Every turn sends the full context again. Zerix returns prepared context and a receipt to your app, then your app calls the selected model.
Raw API middleware
Routing is handled, but token movement and quality boundaries can stay opaque. Zerix includes request metrics so billing and savings can be reviewed per call.
Generic context tooling
One-size compression can be risky for code, structured data, and brittle facts. Preserve-first routing keeps fragile spans intact and compresses only what is safe.
Pricing follows verified savings
Zerix records requests, token movement, and verified savings for metering. Fixed public savings claims remain pending attestation.
Metered PAYG
Charge from a percentage of verified savings with configured floor and cap. Zerix is not sold as a replacement model.
View PricingPublic savings, quality, latency, and ROI numbers stay pending attestation until engine verification is complete.
FAQ
Honest answers before you connect.
What is live, what is illustrative, and what stays pending until verification.
No. Zerix returns prepared context to your app, and your app sends it to the model you select. That model still produces the final answer.
New accounts receive $1 in Zerix credit. A referred friend receives the same non-stackable $1 signup credit; the referrer receives $1 only after that friend completes a first paid top-up of at least $5 and uses $1 of paid Zerix usage. Credits are non-withdrawable and expire after 30 days.
No. The public simulator is an illustrative, no-signup experience. It shows transparent example math and labels demo numbers as illustrative.
Long conversations, agent traces, support histories, retrieval-heavy prompts, and other workflows where context grows over time are the best fit. Tiny one-shot prompts may have limited savings.
No. Your model-provider key stays in your application and never touches Zerix in the launch product. Use a Zerix key for /v1/compress, then call your model from your side.
After attestation gates pass. Until then, the public site avoids fixed savings, quality, latency, and ROI claims and relies on per-request receipts.
See the savings receipt before you sign up
Try a bounded example without a signup wall. When you are ready to integrate, create a workspace, add a key, and see verified savings alongside your usage.
$0.50 per 1M verified tokens saved. No verified savings, no Zerix fee.