ServicesAboutProcessClientsFAQ
AI Development & Integration

AI chatbot development & LLM integration — production-ready, not a demo.

We build AI-powered products for funded startups — custom enterprise AI chatbots, Claude API integrations, RAG pipelines, and intelligent workflow automation. OpenAI, Anthropic, Google, and open-source models. Full-stack, production-grade.

All services
OpenAI / GPT-4Claude APIGeminiLangChainRAG PipelinesLLMsAI Chatbots
AI development and integration — enterprise AI chatbot and LLM integration
What We Build

AI development services for production products.

From enterprise AI chatbot development companies to solo founders adding intelligence to existing products — we handle the full technical build with OpenAI, Claude API, Gemini, and open-source models.

🤖

Enterprise AI Chatbot Development

Custom AI chatbots for customer support, internal knowledge bases, sales qualification, and operations. GPT-4, Claude, or Gemini backends with multi-turn memory, tool use, and escalation logic. Not a chatbot widget — a real product.

🔗

Claude API Integration

We integrate Anthropic's Claude API into your existing products — customer-facing assistants, document analysis pipelines, code generation tools, and enterprise knowledge systems. Prompt caching, tool use, and agentic workflow design included.

📚

RAG Pipeline Development

Retrieval-augmented generation pipelines that give your LLM access to your internal documents, knowledge base, or database. Vector stores (Pinecone, Weaviate, pgvector), chunking strategies, and re-ranking for accuracy.

⚡

AI Workflow Automation

Intelligent automation that replaces manual processes. Document extraction and classification, email triage, report generation, data enrichment, and multi-step agent pipelines using LangChain or custom orchestration.

📊

Predictive Analytics & ML

Custom ML models for churn prediction, demand forecasting, fraud detection, and recommendation engines. Model training, API deployment, and integration into your existing data infrastructure.

🌐

AI-Native Product Development

We build AI-first products from the ground up — from architecture design to production deployment. LLM-powered SaaS, AI agents, and multi-model applications with full backend infrastructure.

Our Process

How we build your AI integration.

01

Use-case scoping

We map your AI use case: what decision or task the model must perform, what data it needs access to, latency requirements, and how it integrates into your existing product. We surface the failure modes early.

02

Model selection & architecture

We recommend the right model for your task — GPT-4o, Claude Sonnet, Gemini, or open-source — and design the full architecture: RAG pipeline, tool use, memory layer, and safety guardrails. Fixed quote before code is written.

03

Build, eval, and tune

We build and run evaluation suites at every sprint — accuracy, latency, cost per query, and edge-case coverage. You see the system performing against real inputs before the project closes.

04

Production deployment & monitoring

Deployment to your cloud with observability, cost controls, rate limiting, and prompt versioning. We set up dashboards so you can track AI quality metrics after launch — not just uptime.

FAQ

Questions about AI development & integration.

An AI chatbot development company builds the full stack — not just a widget. That means: the backend LLM integration (OpenAI, Claude, Gemini), the conversation memory and context management, the retrieval layer (RAG) if your chatbot needs access to your data, the API to connect it to your product, and the admin tooling to monitor and improve it. ByteBridge handles every layer.
Claude is Anthropic's family of large language models — known for long context windows, precise instruction following, and strong reasoning on complex tasks. Claude API integration means adding Claude's capabilities into your product: document analysis, customer support automation, code generation, or intelligent data extraction. We recommend Claude for tasks requiring careful reasoning, long-document processing, or safety-critical outputs.
RAG (Retrieval-Augmented Generation) lets your LLM answer questions using your own documents, databases, or knowledge base — rather than relying on its training data alone. You need a RAG pipeline if your AI product must be accurate about your specific business context: internal policies, product docs, customer records, or proprietary data. We design the chunking strategy, vector store, and retrieval logic for your use case.
Cost depends on the complexity of the use case, the number of integrations, and the volume of data in the retrieval layer. A focused single-purpose AI chatbot (customer support or internal knowledge assistant) typically runs $15,000–$40,000. A multi-modal, multi-tool AI agent with custom RAG pipelines and enterprise integrations is higher. We give fixed-scope quotes after a discovery call.
Yes — this is one of the most common AI development engagements we run. We audit your existing product architecture, identify integration points, and build the AI layer (API endpoints, prompt logic, retrieval, memory) to fit into your current stack without a full rewrite.
It depends on your task. GPT-4o is strong on structured outputs and broad capability. Claude excels at long documents, careful reasoning, and instruction following. Gemini 1.5 Pro has the largest context window for document-heavy use cases. Open-source models (Llama, Mistral) are right when you need cost control and on-premises deployment. We make a specific recommendation after reviewing your requirements.

Ready to add real intelligence to your product?

Tell us what you want your AI to do. We’ll recommend the right model, architecture, and approach — and give you an honest consultation on what’s possible within your timeline and budget.

Hire AI engineers