Keep AI agents effective as context grows.
As conversations, tool traces, and handoffs accumulate, important constraints become harder to use. Zerix manages each request before the model call and returns request-level evidence your team can inspect.
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?
Start with the context your application already has, then inspect the receipt for each request before deciding where Zerix fits.
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 that accumulate context over time
- 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, focused one-shot queries
- 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 growing AI context.
Applications with long or repetitive context can evaluate Zerix request by request.
AI Chatbots
As conversations grow, Zerix prepares eligible context and returns request-level metrics.
RAG Pipelines
Zerix prepares retrieved context for the next model call and returns request-level metrics.
Multi-Agent Systems
Zerix can prepare shared context before it moves between agents; inspect the receipt for each request.
Customer Support AI
Zerix prepares long support histories and returns request-level metrics your team can inspect.
Code Assistants
Zerix prepares code-bearing context before the model call and returns request-level metrics.
Enterprise Knowledge
Zerix prepares long transcripts and documents and returns request-level metrics.
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,
// $15 is the customer's own model list price per 1M input tokens; replace it with yours.
pricing: { customer_model_input_price_per_mtok_usd: 15 }
})
})
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: '<your-model-id>',
messages: prepared.messages
})
console.log(result.choices[0]?.message)
console.log(prepared._zerix_metrics)Best for: Startups, solo developers, custom AI apps
Pricing combines a processed-token base with a realized-dollar savings share.
Zerix records requests, token movement, and verified savings for metering. Fixed public savings claims remain pending attestation.
Metered PAYG
Zerix charges a $0.25 base per 1M input tokens processed plus 25% of realized dollar savings. If realized dollar savings are $0, the savings share is $0.
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.
Available signup or referral credit is shown from your account’s live billing configuration after eligibility checks.
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.
Zerix charges a $0.25 base per 1M input tokens processed plus 25% of realized dollar savings. If realized dollar savings are $0, the savings share is $0.