AI Chatbot Development for Telecom Operators

Call center operators drown in repetitive calls. Balance checks, tariff changes, blocking, "internet not working"—the same script hundreds of times a day. Costs rise, customers get frustrated waiting. A properly built AI chatbot reduces contact center load by 40–60% and frees agents for complex cases.

How to reduce telecom call center load with an AI bot?

A bot handles up to 80% of routine queries without human involvement. This requires integration with billing (Amdocs, Billing.ru, Hydra) and CRM (Salesforce, SAP CRM). The user gets an instant answer, and the system gets a unified interaction point. We use an LLM on top of a RAG pipeline: the request passes through intent classification (BERT-based), then retrieval from a knowledge base (ChromaDB with 1536-dim embeddings), and only then response generation. This yields intent recognition accuracy above 90% compared to 60% for traditional IVR menus.

Typical telecom bot scenarios

Account management: balance, itemization, top-up, tariff change, service activation. Authentication: phone number + SMS OTP. Technical support: guided troubleshooting for internet or mobile issues. Diagnostic tree: router reboot, cable check, settings reset, field technician dispatch ticket. Sales: tariff selection based on needs, promotion info, tariff migration.

How internet problem diagnostics work?

"Internet not working"
→ Check network status in the area (monitoring API)
  → If outage in area: "Technical work is underway in your area. Estimated restoration: 15:00. We'll send a notification."
  → If no outage:
    → Guided troubleshooting: connection type, router indicators
    → Remote diagnostics (if equipment API is available)
    → Field technician dispatch ticket with auto-selection of convenient time

Why an AI chatbot is more effective than a traditional feedback form?

A traditional feedback form means a ticket and a reply in hours. A chatbot answers in seconds, using RAG to access the operator's knowledge base. Query understanding via few-shot prompting handles complex requests.

When to replace an agent with a bot and when to keep a human

A bot is indispensable for routine queries: balance, tariff, password reset. Complex issues—complaints, emergencies, equipment problems—are escalated with full context. Our experience shows the optimal split: the bot handles 70% of queries, and 30% are escalated.

BSS/OSS integrations

Billing systems (Amdocs, Billing.ru, Hydra): balance, itemization, service management. OSS (network management systems): outage status, signal quality at address. CRM (Salesforce, SAP CRM): customer history, open tickets. Each layer has its own REST endpoint with rate limiting and circuit breaker.

Reducing churn through the bot

The bot detects churn signals ("I want to disconnect", "too expensive", "switching to another operator") and triggers a retention flow: a limited-time special offer. Conversion of retention offers through the bot: 15–25%—comparable to call center at lower cost. Metrics: containment rate (target 55–65%), AHT reduction for agents via agent assist, churn rate among bot-interacting customers drops 5-10%.

Step-by-step deployment algorithm

1. Audit call center logs—identify top 10 scenarios by frequency.
2. Define scenarios and response formats—write flows in flow notation.
3. Integrate via REST API—connect to BSS/OSS/CRM.
4. Train model on N historical chats—fine-tuning + RAG.
5. A/B testing—compare metrics with control group.
6. Phased rollout—start with 10% traffic, increase share.

What the work includes

• API and bot scenario documentation.
• Knowledge base administration guide.
• Agent training on agent assist.
• 30 days post-release support.

Estimated timelines

Basic bot for 5-10 scenarios: 4 to 6 weeks. Full system with retention flow and BSS/OSS integration: 8 to 12 weeks. Costs are determined individually—reach out, we'll assess your project.

Over 5 years in telecom, 15+ projects, an AI team with NLP and MLOps expertise. We guarantee stable operation and transparent analytics. Contact us for a consultation. Order chatbot development and get a free task audit.

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.