Add AI to your product without rebuilding it from scratch
We take existing Node.js and React products and add working AI features: LLM integrations, chatbots, RAG pipelines, OpenAI API wiring, workflow automation and AI-powered UX layers. No hype, no rewrites — just AI that actually ships and works in production.
What does “AI integration” actually mean for a product company?
Most SaaS products and startups don’t need to train models or build AI from the ground up. What they actually need is to take the models that already exist — GPT-4o, Claude, Gemini, Mistral — and wire them properly into their product. That means building the right backend layer, prompt logic, context handling, retrieval system (RAG), error handling, streaming responses, cost controls and a user-facing interface that feels natural. That’s AI integration. It’s engineering work, not magic. And it’s where Valant specializes — we’re a JavaScript shop, so we plug AI into your existing Node.js and React stack without asking you to switch to Python or rebuild anything.
AI integration services we deliver
From a single API integration to a full AI product layer — we scope based on what your product actually needs right now, not what sounds impressive in a pitch.
LLM API integration into existing products
You have a working product. You want to add AI. We wire the language model into your backend, set up proper prompt architecture, handle context windows, streaming, retries, token cost controls and edge cases. Works with Node.js, Express, Fastify, NestJS — whatever your stack already uses.
RAG pipeline development
Retrieval-Augmented Generation is how you make AI answer based on your data — not just what the model was trained on. We build the full pipeline: document ingestion, chunking strategy, embeddings, vector database setup (Pinecone, Weaviate, pgvector, Qdrant), retrieval tuning and LLM response generation. The result is an AI that knows your product, your docs, your codebase — whatever you feed it.
AI chatbot and conversational assistant development
Not the generic widget you drop in from a SaaS tool, but an actual chatbot built into your product — with your UX, your data, your tone of voice and the logic that makes sense for your use case. We build the backend (conversation state, context management, function calling, tool use) and the frontend chat UI in React, wired together cleanly.
AI workflow automation and LLM agents
AI agents are LLMs that don’t just answer — they act. They call APIs, read documents, make decisions, pass tasks to other agents and return structured outputs. We build agentic workflows for internal operations, content pipelines, data processing, classification, extraction and anything repetitive that a language model can now handle better than manual work.
AI developer staff augmentation
You already have an engineering team, but nobody on it has shipped LLM features before. We plug in one or two developers who know both the JavaScript stack and AI engineering — prompt design, RAG, token economics, evals, model selection, latency optimization. They work inside your team, in your repo, in your sprint process.
Tech stack we work with
We’re a JavaScript-first team, so AI engineering happens in the same stack your product already runs on — no separate Python service required unless the architecture genuinely calls for it.
LLM providers
We’re model-agnostic and pick based on your cost, latency and quality needs.
- OpenAI GPT-4o, GPT-4o-mini, o1
- Anthropic Claude 3.5 Sonnet, Haiku
- Google Gemini Pro, Flash
- Mistral, Llama 3, Mixtral (self-hosted)
- AWS Bedrock, Azure OpenAI, Vertex AI
AI frameworks and tools
Production-proven tooling, not just demos with LangChain tutorials.
- LangChain, LangGraph, LlamaIndex
- Vercel AI SDK for streaming in Next.js
- OpenAI function calling and tool use
- Prompt engineering, evaluation frameworks
- LangSmith, Arize for observability
Vector DBs and storage
We set up and tune the retrieval layer so answers are actually accurate.
- Pinecone, Weaviate, Qdrant
- pgvector (Postgres extension)
- Redis for caching and fast lookups
- S3-compatible object storage for docs
- Hybrid search (dense + sparse vectors)
Backend
Everything runs in your JavaScript/TypeScript backend — no stack fragmentation.
- Node.js, TypeScript, Express, NestJS, Fastify
- REST API and GraphQL
- Streaming responses via SSE and WebSockets
- Queue-based background AI processing
- Docker, AWS, GCP, Vercel deployments
Frontend
The AI feature needs a UI that users actually want to use, not an afterthought.
- React, Next.js, TypeScript
- Streaming chat UI with real-time tokens
- AI-powered search and autocomplete
- Inline AI suggestions and co-pilot UI patterns
- Markdown rendering, code highlighting
AI product patterns
The implementation patterns that separate working AI from demos that break in production.
- RAG with reranking and hybrid search
- Multi-turn conversation with memory
- AI agents with tool use and function calling
- Structured output and JSON mode
- Cost controls and tiered model routing
Who typically hires us for AI integration
Usually it’s product companies that have something working and want to add AI, or startups that want to build AI-first from the start.
SaaS companies adding AI features
You shipped a product, it’s growing, and now competitors are adding AI. You need to move fast but can’t afford to break what works. We integrate AI into specific parts of your product — search, recommendations, summaries, onboarding, support — without touching everything else.
Startups building AI-native products
You’re building something new where AI is the core value, not an add-on. We help architect the right approach from the start — model selection, context strategy, cost structure, data pipeline — and ship the first working version without over-engineering it before you have users.
Product companies replacing manual workflows
There’s work your team does today — data entry, document parsing, content writing, classification, tagging, moderation — that LLMs can handle. We build the automation layer, plug it into your existing systems and hand over something that runs without babysitting.
Agencies and dev shops needing AI capability
Your clients are asking for AI features and you need developers who can deliver. We work as a white-label AI development extension — you manage the client, we build the AI layer. Or we provide developers you embed into your own team for specific projects.
AI integration vs AI development from scratch
Most companies need integration, not a custom AI model. Here’s what the difference actually means in practice.
| Factor | AI Integration (what most need) | AI development from scratch |
|---|---|---|
| Timeline | 2–12 weeks for first working version | 6–18 months minimum |
| Cost | $5,000–$100,000 depending on scope | $500,000+ for real model training |
| Who it’s for | SaaS products, startups, companies with existing products | Large companies with proprietary data at massive scale |
| Tech approach | OpenAI / Claude / Gemini API + RAG + prompt engineering + your stack | Custom model training, fine-tuning infrastructure, data pipelines |
| Ongoing cost | API usage costs (predictable, scales with usage) | GPU infrastructure, MLOps team, continuous training |
| Maintenance | Prompt updates, model version tracking, occasional tuning | Full ML engineering team permanently |
How we run an AI integration project
We keep it straightforward. No six-month discovery phases or endless planning cycles before a line of code gets written.
01. Discovery call and scoping
We talk through what you’re trying to build, what’s already in your stack, what the AI feature needs to do and what “done” looks like. Then we send a short, honest scope document — timeline, approach, team size, cost range. Usually takes a week from first call to proposal.
02. Architecture and proof of concept
Before committing to a full build, we validate the approach. Which model, which retrieval strategy, what the prompt architecture looks like, how it integrates with your backend. A working proof of concept usually takes 1–2 weeks and saves months of wrong direction later.
03. Iterative development in sprints
We work in 2-week sprints. You see progress every two weeks, you can redirect early, nothing gets built in a black box for three months. AI development specifically benefits from this — you learn what works with real usage data, not upfront assumptions.
04. Production deployment and handoff
We ship to production, not a staging demo. We handle deployment, monitoring setup, cost tracking, error alerting and documentation. Then we hand over clean, documented code your team can maintain — or stay on as the ongoing AI development partner.
Related services and pages
AI integration rarely lives in isolation — it connects to how you hire developers, how you structure your team and what your product needs overall.
Questions we get asked before starting an AI project
Real questions from real clients — not the FAQ that exists to fill space.
How long does it take to integrate OpenAI API into an existing product?
A basic integration — chat completion, streaming, context handling — typically takes 2-4 weeks. A production-ready RAG pipeline or a full AI feature with custom UI, evaluation and monitoring takes 6-12 weeks. The honest answer depends on what’s already in your stack and how well-defined the feature is.
Can you add AI to my existing Node.js and React app without rewriting it?
Yes, and this is specifically what we’re set up to do. We don’t come in and tell you to rewrite in Python. We integrate AI capabilities directly into your Node.js backend and React frontend. Most of the time this means adding new endpoints and new React components, not touching what already works.
Do you work with OpenAI only, or also with Claude and open-source models?
We work with the full range. OpenAI GPT-4o and GPT-4o-mini for most standard cases, Claude 3.5 Sonnet for complex reasoning tasks, Gemini for multimodal needs, and self-hosted Mistral or Llama when data privacy or cost is the primary constraint. The right choice depends on your use case.
How much does AI integration development cost?
A single AI feature integration starts from roughly $5,000-$15,000 for a focused scope. A full AI layer — RAG, chatbot, automation workflows, frontend — runs $25,000 to $100,000+. Staff augmentation (one dedicated AI developer) starts at rates competitive for the Ukrainian market. The pricing page has more detail on our engagement models.
What’s the difference between RAG and fine-tuning? Which do we need?
RAG (Retrieval-Augmented Generation) means the model looks up relevant information at runtime from your data. Fine-tuning means training the model itself on your data so it behaves differently. For most product use cases, RAG is the right answer — it’s cheaper, faster to update and doesn’t require retraining when your data changes. Fine-tuning makes sense for very specific tone or format requirements.
Can Valant be a long-term AI development partner, not just a one-time project?
Yes, and most of our AI clients end up going this route. The model landscape changes fast. Prompt improvements, model upgrades, new capabilities, cost optimizations — it’s ongoing work. We stay embedded as a dedicated team or keep 1-2 developers in your team long-term. We’re genuinely more useful as a partner than as a one-time vendor.
Want to add a working AI feature to your product?
Tell us what you’re building, what’s already in your stack and what the AI feature needs to do. We’ll come back with an honest scope, realistic timeline and a clear proposal — usually within a week from first call. No generic pitches, no six-week discovery phases.