Integration
Automate with Groq
Groq's LPU delivers AI inference at speeds other providers can't match. NVS uses it where latency is non-negotiable — real-time ticket triage, instant chat responses, and sub-second classification at scale.
Use Cases
Incoming live chat messages are classified by Groq in under 500ms — instantly routing urgent issues to a human agent before the customer even finishes typing their follow-up message.
Trigger
Chat Message
intercom.trigger
New message arrives in Intercom live chat, firing the triage webhook instantly.
Classify Urgency
groq.llama3
Groq runs Llama 3 in <500ms to classify message as critical, normal, or bot-handleable.
Switch
Route by Class
nvs.switch
Critical messages escalate to a human; others get an AI draft reply or auto-response.
critical
Output
Page Human Agent
slack.message
On-call agent paged instantly with conversation link and urgency summary.
normal
Output
Draft AI Reply
intercom.reply
Groq drafts and sends a helpful auto-reply within 1 second of the message.
Thousands of records — feedback entries, support logs, or product reviews — are classified by Groq in parallel at a fraction of the cost and time of standard LLM providers.
Trigger
Batch Ready
google-sheets.trigger
A new batch of unclassified rows appears in the Google Sheet processing queue.
Fetch Unprocessed
google-sheets.read
All rows with empty "Category" column are fetched for processing.
Loop
For Each Record
nvs.loop
Each record is sent to Groq individually — high throughput from fast token generation.
Classify with Groq
groq.mixtral
Mixtral on Groq returns category, sentiment, and confidence score in milliseconds.
Output
Update Sheet Row
google-sheets.update
Category, sentiment, and confidence written back to the corresponding row.
Unstructured text payloads from any webhook source are parsed by Groq into clean, typed JSON fields — ready to insert into your database or CRM without any brittle regex logic.
Trigger
Webhook Received
nvs.webhook
External system sends a payload containing unstructured or semi-structured text data.
Extract with Groq
groq.llama3
Groq extracts name, intent, entities, and key fields into structured JSON output.
Validate Schema
code.javascript
Output validated against expected schema; missing fields flagged for retry.
Output
Insert to Database
supabase.insert
Validated structured record inserted into Supabase with all extracted fields populated.
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