--- title: "Post-Call Analytics with GPT-4o-mini: Sentiment, Lead Scoring, and Intent" description: "Build a post-call analytics pipeline with GPT-4o-mini — sentiment, intent, lead scoring, satisfaction, and escalation detection." canonical: https://callsphere.ai/blog/post-call-analytics-gpt-4o-mini-pipeline category: "Technical Guides" tags: ["AI Voice Agent", "Technical Guide", "Post-Call Analytics", "GPT-4o-mini", "Sentiment", "Lead Scoring", "NLP"] author: "CallSphere Team" published: 2026-04-08T00:00:00.000Z updated: 2026-05-06T01:02:47.305Z --- # Post-Call Analytics with GPT-4o-mini: Sentiment, Lead Scoring, and Intent > Build a post-call analytics pipeline with GPT-4o-mini — sentiment, intent, lead scoring, satisfaction, and escalation detection. ## The cheap AI that earns its keep Running the Realtime API for live conversation is expensive. Running GPT-4o-mini over the transcript afterwards is nearly free — and it is where most of the operational insight actually comes from. Sentiment, intent, lead score, satisfaction, escalation reason: all of it falls out of one structured JSON call per transcript. This post walks through the post-call analytics pipeline CallSphere runs in production, including the exact schema, the prompt, and the queue architecture that keeps it off the hot path. ``` call ends │ ▼ queue.publish(post_call, {transcript, metadata}) │ ▼ worker pulls │ ▼ GPT-4o-mini call with JSON schema │ ▼ UPSERT call_analytics │ ▼ trigger downstream (CRM, dashboards) ``` ## Architecture overview ``` ┌────────────────────┐ │ Voice agent runtime│ └─────────┬──────────┘ │ on_call_end ▼ ┌────────────────────┐ │ Queue (SQS/Redis) │ └─────────┬──────────┘ ▼ ┌────────────────────┐ │ Analytics worker │ │ • GPT-4o-mini call │ │ • JSON validation │ └─────────┬──────────┘ ▼ ┌────────────────────┐ │ call_analytics │ └─────────┬──────────┘ ▼ dashboards, CRM, alerts, exports ``` ## Prerequisites - A queue for background jobs. - Postgres (or any OLAP store) for the analytics table. - An OpenAI key with GPT-4o-mini access. - The call transcript in a structured `[{role, text}]` format. ## Step-by-step walkthrough ### 1. Define the output schema ```python ANALYTICS_SCHEMA = { "type": "object", "properties": { "summary": {"type": "string"}, "sentiment": {"type": "string", "enum": ["positive", "neutral", "negative"]}, "sentiment_score": {"type": "number", "minimum": -1, "maximum": 1}, "intent": {"type": "string"}, "lead_score": {"type": "integer", "minimum": 0, "maximum": 100}, "satisfaction": {"type": "integer", "minimum": 1, "maximum": 5}, "escalated": {"type": "boolean"}, "escalation_reason": {"type": ["string", "null"]}, "next_action": {"type": "string"}, "tags": {"type": "array", "items": {"type": "string"}}, }, "required": ["summary", "sentiment", "intent", "lead_score", "satisfaction", "escalated", "next_action"], } ``` ### 2. Write the worker ```python from openai import AsyncOpenAI client = AsyncOpenAI() PROMPT = """ You are an analyst reviewing a completed phone call between a customer and an AI voice agent. Return a JSON object matching the provided schema. Be concise and accurate. Do not invent facts. If something is unclear, say so in the summary. """ async def analyze(transcript: list[dict]) -> dict: text = "\n".join(f"{t['role']}: {t['text']}" for t in transcript) resp = await client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": PROMPT}, {"role": "user", "content": text}, ], response_format={"type": "json_object"}, temperature=0.1, ) return json.loads(resp.choices[0].message.content) ``` ### 3. Persist and index ```sql CREATE TABLE call_analytics ( call_id TEXT PRIMARY KEY, summary TEXT, sentiment TEXT, sentiment_score REAL, intent TEXT, lead_score INT, satisfaction INT, escalated BOOLEAN, escalation_reason TEXT, next_action TEXT, tags TEXT[], created_at TIMESTAMPTZ DEFAULT now() ); CREATE INDEX ON call_analytics (sentiment, created_at); CREATE INDEX ON call_analytics (lead_score DESC) WHERE lead_score >= 70; ``` ### 4. Trigger downstream actions ```python async def on_analytics(result: dict, call_id: str): if result["lead_score"] >= 75: await hubspot_log_hot_lead(call_id, result) if result["escalated"]: await pager_alert(call_id, result["escalation_reason"]) ``` ### 5. Handle failures gracefully Validate the JSON against the schema. On failure, retry once with a "fix your previous output" prompt. On repeated failure, park the event in a DLQ for manual review. ```mermaid flowchart LR LEAD(["Inbound lead"]) AGENT["AI voice or chat qualifier"] BANT["BANT capture budget, authority, need, timing"] SCORE{"Lead score and routing rules"} HOT(["Hot — book AE meeting"]) WARM(["Warm — SDR sequence"]) NURT(["Nurture — drip and content"]) CRM[("CRM and SLA timer")] LEAD --> AGENT --> BANT --> SCORE SCORE -->|Hot| HOT --> CRM SCORE -->|Warm| WARM --> CRM SCORE -->|Cold| NURT --> CRM style AGENT fill:#4f46e5,stroke:#4338ca,color:#fff style HOT fill:#059669,stroke:#047857,color:#fff style WARM fill:#0ea5e9,stroke:#0369a1,color:#fff style NURT fill:#f59e0b,stroke:#d97706,color:#1f2937 ``` ### 6. Sample and spot-check Every day, have a human reviewer grade 10 random analytics outputs for accuracy. Drift in the base model shows up here first. ## Production considerations - **Cost**: GPT-4o-mini is ~$0.15/1M input tokens. A 5-minute call is roughly $0.001 to analyze. - **Latency**: this runs async, so latency does not affect the caller, but keep the worker under 10s to avoid backlog. - **PII**: redact credit cards and SSNs before sending the transcript to the LLM. - **Schema evolution**: version the schema and store the version alongside the row. - **Bias monitoring**: spot-check scores across demographics to avoid systematic skew. ## CallSphere's real implementation CallSphere runs exactly this pipeline for every call across every vertical. The voice plane uses the OpenAI Realtime API with `gpt-4o-realtime-preview-2025-06-03`, PCM16 at 24kHz, and server VAD. When a call ends, the transcript plus metadata is published to a queue, and a worker calls GPT-4o-mini with a JSON schema almost identical to the one above, then writes the result into per-vertical Postgres. The healthcare vertical tunes the schema for insurance and clinical intent signals (14 tools), real estate uses tighter lead-scoring and tour-booking intent (10 agents), salon optimizes for rebooking and upsell (4 agents), after-hours escalation focuses on urgency classification (7 tools), IT helpdesk combines intent with RAG-hit quality (10 tools + RAG), and the ElevenLabs sales pod tracks objection categories (5 GPT-4 specialists). All of them feed the same admin dashboard. CallSphere runs 57+ languages with analytics computed identically across them. ## Common pitfalls - **Running analytics synchronously**: it blocks the next call. - **Trusting the JSON without validation**: small JSON errors blow up downstream. - **Mixing verticals in one prompt**: every vertical needs its own schema. - **Ignoring drift**: spot-check or you will miss regressions. - **Logging raw PII**: use field-level encryption for the summary column. ## FAQ ### Why GPT-4o-mini and not the full model? Cost. GPT-4o-mini is accurate enough for analytics and 10-20x cheaper. ### How do I compute trends over time? Roll up nightly into a summary table; do not re-query raw every time. ### Can I use the same output to route follow-ups? Yes — the `next_action` field is designed for it. ### What about multi-language calls? GPT-4o-mini handles 50+ languages well for sentiment and intent. ### How do I correlate analytics with business outcomes? Join `call_analytics.call_id` to your CRM deal closure data. ## Next steps Want sentiment, intent, and lead scoring on every call? [Book a demo](https://callsphere.tech/contact), explore the [technology page](https://callsphere.tech/technology), or see [pricing](https://callsphere.tech/pricing). #CallSphere #PostCallAnalytics #GPT4oMini #VoiceAI #Sentiment #LeadScoring #AIVoiceAgents --- Source: https://callsphere.ai/blog/post-call-analytics-gpt-4o-mini-pipeline