Customer feedback analysis AI

Support tickets, app-store reviews, and NPS comments pile up faster than any team can read them by hand. unoblox lets you run that text through GPT, Claude, DeepSeek, or Qwen models from one OpenAI-compatible endpoint, billed per token in rupees, so a feedback-analysis pipeline is a normal line item on a GST invoice rather than a foreign software subscription.

What feedback analysis actually asks of a model

Most feedback pipelines need three things: a sentiment or category label per item, a short extracted reason such as delivery delay, pricing confusion, or praise for support, and occasionally a one-line summary of a whole batch. None of this needs the largest model in the catalog — it needs a model that follows a structured-output instruction consistently across thousands of short texts at a low per-call cost.

Choosing a model for volume versus nuance

Workload	Model	₹ per 1M tokens (input / output)
High-volume ticket tagging	DeepSeek V4 Flash	₹9.07 / ₹18.14
Bulk review classification	Qwen3 235B-A22B	₹9.07 / ₹55.44
Mixed-language, open-weight option	Qwen3.8-27B	₹16.32 / ₹48.96
Nuanced escalation triage	GPT-5 mini	₹25.2 / ₹201.6

Anything you want to test outside this table, including the free Qwen3 1.7B tier, see live ₹ pricing on /models before committing a full backlog to it.

Asking for structured output, not prose

Feedback pipelines are easiest to build when the model returns JSON you can insert straight into a table:

POST https://api.unoblox.ai/v1/chat/completions
Authorization: Bearer ub-gw-xxxxxxxxxxxxxxxx
Content-Type: application/json

{
  "model": "deepseek-ai/deepseek-v4-flash",
  "response_format": {"type": "json_object"},
  "messages": [
    {"role": "system", "content": "Return JSON: sentiment, theme, one_line_reason."},
    {"role": "user", "content": "Delivery was two days late and support took a day to reply."}
  ]
}

JSON mode works the same way across every model on the endpoint, so you can start on a cheap model and move a batch to a stronger one without rewriting the parsing code.

Clustering feedback with embeddings

Tagging one ticket at a time is useful, but spotting a new recurring complaint across ten thousand reviews is a clustering problem, not a classification one. unoblox exposes a native /v1/embeddings endpoint you can call on each piece of feedback, then cluster the resulting vectors to surface themes nobody wrote a rule for yet. This runs as a separate, cheap call per item and pairs well with a chat model that labels each discovered cluster in one shot afterwards.

Keeping a feedback pipeline affordable as volume grows

• Route the first pass, tagging every single item, to the cheapest model that hits your accuracy bar.
• Reserve a stronger model for the subset flagged as negative, urgent, or ambiguous.
• Batch calls where your rate limits allow it, rather than one request per ticket in a tight loop.
• Watch the monthly GST invoice against ticket volume so cost per ticket analysed becomes a number you actually track.

Frequently asked questions

Will a cheap model misclassify feedback badly? Cheaper models can be less consistent on ambiguous or sarcastic text; the usual pattern is to route anything the model itself flags as low-confidence or borderline to a stronger model rather than trusting one pass on everything.

Can this run inside our existing data warehouse job? Yes — it is a plain HTTPS call, so it fits into any scheduled job, ETL step, or serverless function that can make an HTTP request and parse JSON.

Does analysing customer feedback this way keep data in India? Only if you route it to an unoblox-hosted small model; GPT, Claude, and similar external models process the text on their own infrastructure. unoblox's India-specific benefit for those is rupee billing and a GST invoice, not data residency.

How do we avoid the model inventing themes that are not in the text? Constrain it with an explicit list of allowed categories in the system prompt rather than an open-ended one, and spot-check a sample of outputs before trusting the pipeline unattended.

Is there a free tier to prototype the pipeline? Yes, Qwen3 1.7B is free (₹0), which is enough to build and test your prompt and JSON schema before switching to a paid model for production volume.

Get started in rupees → https://unoblox.ai/sign-in