Intelligent FAQ Bot with RAG for Customer Support

Recently, a retailer with 20 support operators approached us. Each day they handled 500 typical requests—password reset, order status, returns. 70% of calls were repetitive. After deploying a RAG bot, workload dropped by 60%, and average response time fell from 5 seconds to 0.3 seconds. Savings amounted to millions of rubles per year. Now the bot answers 85% of questions without human involvement.

According to Gartner research, implementing RAG reduces support load by 30–50%. A company where 30 operators daily answer hundreds of stereotypical questions—"How do I reset my password?", "Where is my order?", "How do I change my plan?"—spends 50–70% of time on repetitive queries. As a result, average response time increases and operators burn out. We develop RAG bots that take over this routine, freeing people for complex cases.

How a RAG Bot Solves the Problem of Typical Questions

A modern FAQ bot is built not on rigid "question → answer" rules but on the RAG architecture (Retrieval-Augmented Generation).

1. Indexing the knowledge base — your articles, FAQs, instructions are split into chunks (200–500 words) and indexed in a vector store (ChromaDB, Pinecone, pgvector).
2. Retrieving relevant chunks — the user's question is converted to an embedding (1536-dim) and searches for the top 5 chunks by cosine similarity.
3. Generating an answer — an LLM (GPT-4o, Claude 3.5) synthesizes a reply based on the retrieved chunks, considering context and confidence.

Why a RAG Bot Is More Effective Than a Rule-Based Bot

What Problems We Solve During Implementation

• Poor knowledge base structure. Clients often store answers haphazardly—one article covering "all questions about payment". We audit, split content into atomic units (one question per article), and add metadata: tags, update date, product.
• Low quality of chunks. Long articles of 2000+ words are split using an overlap algorithm (10% overlap). Optimal chunk size is 200–500 tokens; otherwise, the LLM loses context.
• Lack of metrics. We set up a dashboard: number of queries, confidence distribution, top 20 unanswered questions. Weekly review—the knowledge base grows with actual needs.

Get a consultation on the RAG solution today.

What Is Included in Turnkey FAQ Bot Development

1. Knowledge base audit—review current materials, structuring, identifying gaps.
2. RAG pipeline design—selection of embedding model (text-embedding-3-small), vector database, LLM (GPT-4o), chunking configuration.
3. Channel integration—website (JavaScript widget), Telegram, WhatsApp, CRM (via API).
4. Confidence threshold and escalation setup—threshold 0.65, transfer dialog to operator with context.
5. Deployment and monitoring using MLOps—containerization (Docker), orchestration (Kubernetes), logging (MLflow).
6. Team training—how to update the knowledge base, analyze logs, fine-tune the model (fine-tuning for specific terminology).

Work Process: From Audit to Release

Total timeline: 30 to 45 days depending on integration complexity and knowledge base volume. The cost is calculated individually after the audit. For an audit of your knowledge base, contact us.

Typical Mistakes When Implementing a FAQ Bot

• Using an unstructured knowledge base—chunks don't fit well, the LLM gets confused.
• Ignoring the confidence threshold—the bot gives incorrect answers with low confidence.
• Lack of monitoring—problems are only discovered after user complaints.
• Chunks that are too long (over 1000 tokens)—the LLM loses context.

Why Trust Us with Development?

Our experience: 10+ years in AI/ML, 50+ deployed custom AI bots for support, telecom, and retail. We guarantee answer accuracy of 95%+ for a properly structured knowledge base and provide certificates for the LLMs used (SLA on p99 latency < 2s). Contact us—we'll evaluate your project in 2 days and offer a turnkey solution. Order AI bot development and reduce support load.

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.