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Building Automated Call Quality Assurance with LLM APIs in 2026

Dhiraj··Updated 6 July 2026

Founder of Bolti, writing about voice AI for Indian businesses.

Bolti, a voice AI platform for building production-ready conversational phone agents, allows companies to connect callers to intelligent voice agents with sub-second latency. Beyond handling live conversations, modern support operations in 2026 require checking 100% of customer interactions for compliance, agent performance, and script adherence. By combining live voice platforms with post-call LLM analysis, you can move away from manual random sampling and build a fully automated call quality assurance api pipeline.

You can get started with Bolti's developer-friendly setup using our free trial with 50 minutes of call time, or scale globally with our simple ₹6/min pay-as-you-go pricing.

What is an automated call quality assurance api?

An automated call quality assurance api is a programmatic interface that ingests call recordings or transcripts, evaluates them against custom evaluation rubrics, and outputs structured compliance scores. Instead of QA managers manually listening to 2% of calls, an API-driven QA pipeline evaluates 100% of calls instantly.

[Live Call on Bolti] ──▶ [Webhook Trigger] ──▶ [Fetch Transcript] ──▶ [LLM Evaluation API] ──▶ [Dashboard/CRM update]

By leveraging Large Language Models (LLMs) via API, you can analyze qualitative aspects of a call—such as empathy, script adherence, and customer frustration—with the same consistency as a human reviewer, but at a fraction of the cost and time.

How to structure a post-call QA pipeline

Building a production-grade QA pipeline requires connecting your telephony and live voice platform to an analytical LLM layer. Since Bolti handles the live conversation, the post-call evaluation runs asynchronously immediately after the call ends.

Here is the step-by-step architecture for your pipeline:

  1. Capture the webhook event: Configure your system to listen for Bolti's post-call webhooks. When a call completes, Bolti sends a payload containing the unique conversation_id, call duration, and termination reason.
  2. Retrieve the transcript: Use Bolti's REST API to fetch the complete, timestamped transcript of the conversation. Because Bolti's underlying voice pipeline processes STT -> LLM -> TTS -> Telephony in real-time, highly accurate, diarized transcripts (separating the agent and caller) are ready instantly.
  3. Send to the evaluation LLM: Pass the transcript along with a system prompt containing your QA rubric to an LLM API (such as OpenAI, Gemini, or Claude).
  4. Parse and store results: Instruct the LLM to return a structured JSON schema containing scores, compliance flags, and short summaries, then save this data to your database or CRM.

Writing the LLM evaluation prompt

The accuracy of your automated QA depends entirely on the clarity of your evaluation prompt. LLMs perform best when they are given explicit, binary rules rather than vague guidelines.

Here is an example of a structured prompt you can send to your evaluation LLM:

{
  "system_instruction": "You are an expert QA auditor for a customer support team. Analyze the provided transcript and return a JSON object evaluating compliance.",
  "rubric": {
    "greeting_check": "Did the agent greet the caller with 'Thank you for calling, my name is...'? (true/false)",
    "verification_check": "Did the agent verify the caller's account details before sharing sensitive info? (true/false)",
    "sentiment_score": "Rate the customer's sentiment from 1 (extremely frustrated) to 5 (highly satisfied).",
    "compliance_violation": "Did the agent make any unauthorized promises or share incorrect pricing? (true/false)",
    "summary": "Provide a 2-sentence summary of the call resolution."
  }
}

By forcing the LLM to output structured JSON matching this schema, your backend database can easily parse the results to trigger immediate alerts for compliance violations.

Why use Bolti for live voice and QA data?

While you can run QA on traditional legacy telephony systems, legacy platforms make it difficult to access clean, real-time data. Bolti is built from the ground up for modern developers, making it the ideal foundation for automated QA systems.

  • Telephony-grade noise cancellation: Bolti strips background line noise before transcribing. This ensures that the generated transcripts are highly accurate, preventing false negatives in your compliance checks.
  • Integrated HTTP tools: If your agent needs to trigger actions during the call, Bolti's workspace HTTP tools allow the live agent to look up customer records or update CRM states mid-conversation. These tool execution logs are saved alongside the transcript for complete auditing.
  • Open API and REST access: Every action on the Bolti platform can be controlled programmatically. You can easily fetch call logs, transcripts, and metadata via our REST endpoints to feed your downstream QA systems.

Set up your first automated QA agent

Automating your call quality assurance starts with generating high-quality live conversational data. With Bolti, you can spin up an intelligent, multilingual phone agent, assign a phone number, and begin making or receiving calls in under 10 minutes.

Sign up today to get 50 free minutes of call time and test our developer-friendly APIs. Create your free Bolti account and start automating your support operations.

Frequently Asked Questions

Can I use my own telephony provider with Bolti?

Yes. Bolti supports Bring Your Own Carrier (BYOC). You can register your own SIP trunk from providers like Twilio, Plivo, Exotel, or Vobiz to route calls while using Bolti's real-time voice pipeline.

How much does Bolti cost to run at scale?

Bolti offers a straightforward pay-as-you-go pricing model at ₹6/minute. There are no hidden platform fees, and every new account gets 50 free minutes to start testing.

Does Bolti provide diarized transcripts for QA?

Yes. Bolti's voice pipeline separates the caller's audio from the agent's audio in real-time, providing clean, diarized transcripts that clearly distinguish who said what, making post-call LLM analysis highly accurate.

Does Bolti support Indian regional languages for QA?

Yes. Bolti is built for multilingual environments, supporting Hindi, Marathi, Tamil, Telugu, Bengali, Gujarati, English, and over 80 global languages. Your post-call QA pipeline can analyze transcripts in any of these languages.