AI-Automated Ticket Creation from Call Transcripts

After finishing a conversation with a client, the operator spends 2–5 minutes manually filling out a ticket. At 200 calls per day, that's up to 16 man-hours lost. Beyond time waste, errors creep in: missing fields, wrong categories, lost details. We replace this routine with an AI agent that analyzes the transcript and creates a structured ticket in seconds—complete with category, priority, and extracted data. The operator simply clicks "Confirm"—in 80–90% of cases no edits are needed. The system works with any helpdesk that supports an API and uses modern LLMs: GPT-4, Claude, or LLaMA.

AI-Powered Ticket Creation from Transcripts

Our core task is turning an unstructured conversation into a formalized ticket. The AI model extracts the essence, category, priority, and additional attributes. The architecture is straightforward: after the call ends, the transcript is sent to an LLM, which returns a JSON object with ticket fields.

How the AI Extracts Data from the Dialogue

Once the conversation finishes, the AI model receives the transcript and generates a ticket with these fields:

class AutoGeneratedTicket(BaseModel):
    subject: str                     # brief problem description
    description: str                 # detailed description with context
    category: str                    # type of issue
    priority: Literal["P1","P2","P3","P4"]
    customer_sentiment: str          # emotional state of the customer
    resolution_provided: bool        # whether issue was resolved
    follow_up_required: bool         # if additional action needed
    follow_up_description: str | None
    extracted_entities: dict         # order numbers, products, amounts
    tags: list[str]                  # for search and analytics

Each field is filled based on context. Priority is determined by sentiment and keywords—for example, if the customer says "urgent," priority is raised to P1. Categories are mapped from phrases: "email not arriving" → "Email notifications".

Why This Is Faster Than Manual Entry

Manual creation: select category (3–10 seconds), write description (60–120 seconds), add tags (10–30 seconds). Total: 2–5 minutes. AI does the same in 20–30 seconds, including transcription and generation. Comparison:

Savings: AI is 10× faster and cuts operational costs by 60–80%. For a team handling 200 tickets daily, this saves approximately $2,000–$5,600 per month in operator costs. Request a demo to see the system in action.

Helpdesk Integration

After generation, the ticket is automatically created in your system via API. Example for Zendesk:

zendesk.tickets.create(
    subject=ticket.subject,
    comment={"body": ticket.description},
    priority=ticket.priority.lower(),
    tags=ticket.tags,
    custom_fields=[{"id": CATEGORY_FIELD_ID, "value": ticket.category}]
)

Source: Zendesk API documentation

The operator receives a notification: "Ticket created automatically—please review and adjust if needed." The system supports Zendesk, Jira Service Management, Freshdesk, Bitrix24, OTRS, and custom REST APIs. If you need another system, let us know and we'll add it.

Enhanced Automation Features

Other key benefits include automated ticket creation, AI helpdesk automation, and ticket generation from transcripts. LLM ticket extraction is accurate, reducing operator time and enabling helpdesk workflow optimization. With support for Zendesk API ticket creation and support ticket automation, our AI agent call analysis ensures seamless transcript to ticket conversion. This enhances customer service automation and overall helpdesk workflow optimization.

What's Included in the Development

We deliver a turnkey solution in 2–4 weeks:

1. Audit current helpdesk workflows and issue types; collect example dialogues.
2. Design prompts and ticket schema: fields, priorities, categories.
3. Integrate with your helpdesk via REST API or webhooks.
4. Configure LLM (GPT-4, Claude, or LLaMA) for data extraction with few-shot examples.
5. Test on historical dialogues—aim for ≥90% accuracy per field.
6. Train operators on how to review and correct auto-created tickets.
7. Prepare documentation and codebase ready for expansion.
8. Provide 3 months of post-deployment support: monitoring, fine-tuning, prompt updates.

Timeline by stage:

Typical Manual Ticketing Errors

• Missing customer_sentiment field — impossible to gauge urgency.
• Incorrect category due to ambiguous phrasing.
• Loss of context when rephrasing.

AI eliminates these errors: it always fills every field based on full context.

Experience and Guarantees

We have been developing AI solutions for over 5 years, with more than 50 projects in retail, fintech, and logistics. We hold a software development license and certifications from OpenAI and Hugging Face. All integrations come with a 6-month warranty. If issues arise, we respond within 2 hours. Each project undergoes two-stage testing: on synthetic data and on real dialogues. We log accuracy metrics for each field and provide a report. If accuracy drops below 90% on a new issue type, we fine-tune the prompts at no extra cost.

Want to automate ticket creation? Contact us for a free project assessment. Get a consultation on model selection and implementation timeline.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.