Support Poly - AI Multi-Lingual Model
Multi-Lingual
by Neurometric🎧 Customer Support
Support-Poly is a task-specific model that translates support tickets while maintaining technical accuracy in the response.
Support Poly is designed for Customer Support workflows where speed, consistency, and control matter more than generic creativity. Instead of relying on a broad model to guess intent each time, this task-specific SLM is optimized for multi-lingual and predictable output quality. That makes it easier to adopt in production pipelines where teams need reliable formatting, lower latency, and reduced hallucination risk.
Common integrations include Zendesk, Intercom, and help-center content so every response follows your support policies. This makes deployment practical for sales ops, customer success, legal, and operations teams that need answers grounded in their own data. Because the model is small and focused, teams can run it with efficient infrastructure while still meeting quality targets for the use case.
Compared with general-purpose models, focused SLMs like this one typically deliver better precision for narrow tasks, lower cost per request, and easier governance. You can start with the free tier, test the model against your business data, and scale usage through Neurometric APIs or ClawPack workflows when performance meets your benchmark.
Model: Qwen/Qwen3-4B-Instruct-2507
This task-specific SLM uses Qwen/Qwen3-4B-Instruct-2507 with a specialized system prompt. The model weights are identical to the base Qwen model — the task specialization comes from the system prompt and has been validated through our testing pipeline. Need better accuracy? We can fine-tune the model on your data for your specific use case.
Quickstart
Copy-paste code to use Support-Poly in your project
from openai import OpenAI
client = OpenAI(
base_url="https://api.neurometric.ai/v1",
api_key="YOUR_API_KEY"
)
resp = client.chat.completions.create(
model="neurometric/support-poly",
messages=[
{"role": "user",
"content": "{\n \"message\": \"Bonjour,\\nAprès la rotation du certificat IdP hier, nos utilisateurs ne peuvent plus se connecter via SSO.\\nLe navigateur affiche: 'Invalid Signature on SAML Response'.\\nDans Azure ..."}
]
)
print(resp.choices[0].message.content)Test Results
Example Input
Message: Bonjour, Après la rotation du certificat IdP hier, nos utilisateurs ne peuvent plus se connecter via SSO. Le navigateur affiche: 'Invalid Signature on SAML Response'. Dans Azure AD, le nouvel X.509 a bien été publié. Côté application, nous avons mis à jour le metadata URL. Pouvez-vous confirmer si votre SP met en cache l'ancien certificat ou si un redémarrage est nécessaire? Cordialement. Product: SSO Gateway Subject: Échec de connexion SSO (SAML) après rotation du certificat Ticket Id: SR-7781 Environment: Production Source Language: fr Target Language: en
Expected Output
Hello,
After the IdP certificate rotation yesterday, our users can no longer log in via SSO.
The browser shows: 'Invalid Signature on SAML Response'.
In Azure AD, the new X.509 certificate has been published. On the application side, we updated the metadata URL.
Can you confirm whether your SP is caching the old certificate or whether a restart is required?
Best regards.
Actual Model Response
Hello,
After the IdP certificate rotation yesterday, our users can no longer log in via SSO. The browser displays: "Invalid Signature on SAML Response".
In Azure AD, the new X.509 certificate has been successfully published. On the application side, we have updated the metadata URL.
Could you please confirm whether your SP caches the old certificate or if a restart is required?
Best regards,
Environment: Production Product: SSO Gateway
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Capabilities
Related models
Frequently asked questions
Does Support Poly work with Zendesk?
Yes. Support Poly is built to work with Zendesk, Intercom, and support ticket exports. Most teams connect existing APIs or scheduled exports and map records into prompt-ready JSON.
What data format does this model accept?
You can send plain text or structured JSON. For multi-lingual, teams usually pass normalized records with field names, context notes, and output constraints.
Why use a task-specific SLM for multi-lingual?
Task-specific SLMs are tuned for one business outcome, so they are often more consistent and cost-efficient than general models for repetitive production workflows.
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Use via API
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Need better performance?
We can fine-tune this model on your data for higher accuracy on your specific use case.