From AI demo to a real production decision.
Without buying a GPU, without waiting for an AI team, compare models on your own data, run an evaluation and decide on the same day which model is good enough for your use case.
How to run an AI POC with Parel: a 1-day hands-on guide
Run a working POC in 1 hour with your Parel API key, a copy-paste CSV and Python script. Three models compared side by side, ready decision table at the end.
Auto-classifying customer support tickets with AI and routing them to the right team
Have AI read each incoming support ticket, categorize it (billing, technical, sales etc.) and route it to the correct team. Production-grade flow: hybrid routing, critical-ticket escalation, drift monitoring. Working Python.
Deploying a quantized 70B open model on Parel (BYOM)
Deploy Llama-3.3-70B-AWQ, Qwen3-72B-AWQ or DeepSeek-V3.5-AWQ on a rented GPU in 8-12 minutes. No Docker, no vLLM flags. OpenAI SDK compatible chat endpoint.
Connecting Claude Code to Parel: cheap routine + expensive refactor
Use tm_qwen3coder ($0.18/M tokens) for routine coding, claude-opus-4-7 only for hard refactors. Replace the $200/month subscription with a variable Parel bill. 30-second setup.
Cutting your OpenAI bill with Qwen: cost vs quality
Will switching from OpenAI to open-source Qwen halve your bill? Is the quality drop acceptable? An eval-driven playbook that measures the trade-off on 100 examples.
Parel Signals
Short notes on how to update your POC decision when a new model release ships, when a price drops or when latency spikes. Track the impact on your product, not the AI news.