A customer calls, gives their name, asks to reschedule the meeting, confirms the address. The agent frantically opens the card, switches between fields, forgets something, then asks again. Sound familiar? We solved it — AI automatically fills the customer card after a call, extracting all structured data from the transcript. Our system works with any CRM via REST API and achieves up to 95% extraction accuracy at model confidence levels. It's built on 10+ years of experience in AI and NLP, and more than 50 successful CRM automation projects.
Every manual entry takes 2–3 minutes of agent time and risks an error: forgotten email, mixed-up address, lost agreement. AI handles it in 2–3 seconds, and the agent only needs to confirm the data. As a result, call handling time is reduced by 80%, and input errors virtually disappear. The system does not overwrite old data when merging — smart merge preserves existing information if confidence in the new value is low.
How AI Extracts Data from a Call
The system uses NER (Named Entity Recognition) based on GPT-4o. After the call, the transcript is fed to the model, which returns a JSON with card fields and a confidence score for each field. Example extraction:
async def extract_entities_from_call(transcript: str) -> dict: """Extract structured data from a dialogue""" response = await client.chat.completions.create( model="gpt-4o", messages=[{ "role": "system", "content": """Extract the following data from the call transcript (if mentioned): - customer_name: full customer name - address: delivery/residence address - email: email address - phone_secondary: additional phone number - order_details: order/request details - complaint_description: problem description - preferred_contact_time: convenient time to contact - product_interest: products/services of interest - next_appointment: date/time of next contact - notes: important notes Return JSON. Fields not mentioned in the conversation — null.""" }, { "role": "user", "content": transcript }], response_format={"type": "json_object"} ) return json.loads(response.choices[0].message.content) We use Named Entity Recognition (NER) for entity extraction, and confidence scoring evaluates the reliability of each field. Learn more about NER on Wikipedia. Confidence thresholds are configurable to business specifics.
How to Set Confidence Thresholds
Each extracted value receives a confidence score based on the probability assigned by the model. For fields with confidence below 60%, the UI displays a yellow indicator — the agent must verify the value. Fields with confidence above 85% are auto-filled but remain editable. Prompts are tuned so the model returns null for absent data, preventing false fills. This confidence scoring system avoids errors from incomplete or ambiguous customer responses.
Why Is AI 10x Faster Than Manual Entry?
An agent typically spends 2–3 minutes filling a card after a call. AI does it in 2–3 seconds, and the agent only needs to confirm the data — that takes 10–15 seconds. Call handling time is reduced by 80%, and input errors drop to nearly zero. We guarantee the system does not overwrite old data when merging thanks to smart merge.
| Data Type | Example | Confidence | UI Color |
|---|---|---|---|
| Customer name | Ivan Petrov | 0.95 | green |
| Delivery address | Lenin St., 10 | 0.80 | yellow |
| Desired date | tomorrow | 0.50 | yellow |
| Not mentioned | null | 0.0 | gray |
| Action | Time | Errors |
|---|---|---|
| Manual entry | 2–3 min | ~5% |
| AI + confirmation | 10–15 sec | <1% |
What Is Included in the Work?
- Analysis and design: review current CRM fields, create prompts for LLM, define confidence thresholds.
- NER module development: integration with OpenAI GPT-4o or local LLMs (LLaMA, Mistral), implement confidence scoring and smart merge.
- CRM integration: configure REST API, create operator UI widget with color indicators (green/yellow/gray).
- Testing and calibration: test on real calls, tune confidence thresholds, A/B testing.
- Documentation and training: operator instructions, API description, operation manual.
- Support: maintenance for 3 months after launch, prompt adjustments when business processes change.
How We Guarantee Quality
Each extracted field includes a citation from the transcript — the operator sees where the value came from. Confidence below 60% sends the field to mandatory review. We also fine-tune on your dialogues to improve accuracy on specific vocabulary. Order a pilot project on 50 calls — evaluate the accuracy yourself.
Estimated Timelines
- NER + fill for one CRM: 2–3 weeks.
- Multi-platform system with UI and support for multiple CRMs: 1.5 months.
- Fine-tuning for business specifics: +1–2 weeks.
Pricing is calculated individually based on the number of fields, integration complexity, and the need for fine-tuning. Contact us — we will evaluate your project in 1 day and offer an optimal solution.