ByteWave

AI chatbots for business

ByteWave deploys AI chatbots for business: 24/7 customer support, lead generation, FAQ automation, internal assistants. Multilingual support, integration with company knowledge base, escalation to human agents. GPT-4, Claude, Gemini and self-hosted models. POC in 2-3 weeks, production deployment in 4-8 weeks.

What we build

Solutions tailored to the business

01

Customer support chatbot with RAG

Intelligent assistant answering customer questions based on the company knowledge base: documentation, FAQ, terms, product data. Retrieval-Augmented Generation provides precise answers from current documents. Escalation to a human agent when AI is uncertain.

02

Lead-gen chatbot

Assistant qualifying leads on the website. Collects needs, budget, timelines, routes to the right sales rep. Integration with CRM (HubSpot, Pipedrive, Salesforce) and marketing automation. Works 24/7, captures inquiries outside business hours.

03

Internal assistants

Chatbots for teams: HR (leaves, policies, benefits), IT (helpdesk, passwords, procedures), legal (contract templates, policies). Integration with Slack, Microsoft Teams, intranet. Reduces support team load by 40-60%.

04

Multi-channel deployment

One chatbot, many channels: website, Messenger, WhatsApp, Instagram, Slack, Teams. Consistent knowledge, unified responses. Admin panel for knowledge base editing, conversation monitoring, NPS analytics.

Technologies

Tech stack that scales

AI models

OpenAI (GPT-4o, GPT-4o-mini)
Most popular stack for chatbots. Excellent multilingual support, function calling, vision. GPT-4o-mini as a cheaper alternative for simpler queries.
Anthropic Claude
Claude Sonnet and Haiku models. Large context window (200k tokens), excellent for document analysis. Safer responses, fewer hallucinations.
Open-source models (Llama, Mistral)
Hosted locally or in private cloud when confidentiality is required (finance, medical, legal). No data leakage to API providers.
Google Gemini
Native multimodal, fast inference, good integration with Google Workspace. Competitive token pricing for large volumes.

Frameworks and RAG

LangChain / LlamaIndex
Frameworks for building LLM applications: prompt chains, agents, tools, conversation memory, RAG. Industry standard for advanced chatbots.
Vector databases (Pinecone, Weaviate, pgvector)
Storing document embeddings for semantic search. Pinecone managed, pgvector for integration with existing Postgres.
Embeddings (OpenAI, Cohere)
Embedding models for RAG. Multilingual support. Optimization for cost and result quality.
Reranking and retrieval
Hybrid search (BM25 + dense), reranking with cross-encoder models, query rewriting. Increases RAG answer relevance by 20-40%.

Integrations and deployment

Web widget / SDK
Embed on a website with a single script tag. Customizable UI matching brand, dark mode, animations, optional sound effects.
Messenger / WhatsApp / Slack
Integration with messaging platforms. Meta Cloud API, Twilio, Slack API. One chatbot, many channels, consistent knowledge base.
CRM and ticketing
HubSpot, Pipedrive, Salesforce, Zendesk, Freshdesk. Automatic lead and ticket creation, escalation to a human agent with full conversation context.
Monitoring and evaluation
LangSmith, Helicone, custom dashboards. Tracking token cost, latency, response quality. A/B testing prompts, user feedback loop.
How we work

From idea to deployment

01

Discovery

Analysis of needs, business and technical requirements. We define goals and project scope.

02

Design

UX/UI design, system architecture, prototyping. We visualize the solution before coding.

03

Development

Iterative product building with regular demos. Agile, transparent process, constant contact.

04

Deployment

Testing, deployment, user training. Technical support and growth after launch.

Frequently asked questions

Questions? We have answers

A classic chatbot follows rigid scripts or relies on the model's general knowledge (with hallucinations). A RAG chatbot first retrieves the most relevant fragments from the company knowledge base (docs, FAQ, terms) and responds based on them. Provides current, precise, company-tailored answers without hallucinations.

POC (2-3 weeks, simple FAQ chatbot) starts from 1,800-2,800 EUR. Production chatbot with RAG and integrations is 3,500-7,000 EUR. Advanced deployment with multi-channel, CRM and custom models is 7,000-14,000 EUR. Plus monthly API costs (typically 50-500 EUR depending on volume).

Yes. All major models (GPT-4, Claude, Gemini) excel at multilingual support including Polish, English, German, Ukrainian. For industries requiring specialized vocabulary (legal, medical, finance), optional fine-tuning on company documents.

Yes. By default, data is processed by OpenAI/Anthropic API in zero data retention mode (not used for training). For confidentiality-sensitive industries (finance, medical, legal), we deploy local models (Llama, Mistral) or in private client cloud. Full GDPR compliance, conversation audit, retention policy.

POC with simple FAQ - 2-3 weeks. Production chatbot with RAG and CRM integration - 4-8 weeks. Advanced multi-channel deployment with fine-tuning and monitoring - 2-4 months. First working version usually within 2 weeks.

Have a project idea?

Let's talk about how we can make it happen.

Get in touch