AI-POWERED PRODUCTS
Your product should be as smart as your business.
AI is no longer optional — it is the feature your competitors are already shipping. We integrate AI into your existing product or build AI-first products from the ground up, using the tools that actually work in production.
THE PROBLEM
Why most AI integrations fail
Everyone wants AI in their product. Very few get it right.
The team integrates GPT with no thought for prompt engineering, and the responses are inconsistent, off-brand, and sometimes embarrassingly wrong. The AI feature is bolted on as an afterthought rather than woven into the product's core workflow. The costs spiral because no one thought about token usage, caching, or model selection.
Good AI integration is an engineering problem, not a feature problem. It requires careful prompt design, thoughtful UX, intelligent caching, proper error handling, and the right model for the right task. It requires knowing when to use GPT-4o versus GPT-4o-mini, when to use RAG versus fine-tuning, and when not to use AI at all.
Dev Empire has built AI features across enough products to know the patterns that work and the shortcuts that fail.
HOW WE BUILD
AI integration that works in production
AI audit & strategy
Days 1–3Before writing a line of code, we audit your product and your use case. What problem are you actually solving with AI? What data do you have? What does success look like? The output is an AI integration strategy — which models, which approach (RAG, fine-tuning, or direct prompting), and a realistic cost estimate for ongoing AI usage.
Prototype
Week 1We build a working prototype of the core AI feature in week one. This is not a demo — it is a functional integration against your real data and your real use case. You test it, give feedback, and we iterate before full integration.
Production integration
Weeks 2–3We integrate the validated AI feature into your product with proper error handling, fallback behaviour, caching where appropriate, and monitoring. We also implement usage tracking so you always know what the AI is costing you.
Optimisation & handover
Week 4We optimise prompts for consistency and cost, document the integration thoroughly, and hand over with clear guidance on how to manage and evolve the AI features as the models improve.
TECH STACK
The AI tools that work in production
The AI landscape changes fast. These are the tools we use, why we use them, and when we recommend each.
OpenAI API
When: The default choice for most natural language tasks — chat, summarisation, extraction, generation. GPT-4o for complex reasoning, GPT-4o-mini for high-volume, cost-sensitive tasks.
Why: Best-in-class performance, extensive documentation, reliable API, and a model range that lets you balance quality and cost intelligently.
Anthropic Claude API
When: Long-context tasks, document analysis, tasks requiring precise instruction following, and use cases where safety and predictability are paramount.
Why: Exceptional at following complex instructions without drift. Superior for analysing long documents. Often more consistent than GPT for structured output tasks.
LangChain / LangGraph
When: Multi-step AI workflows, agent-based systems, RAG pipelines.
Why: Provides the scaffolding for complex AI workflows without rebuilding from scratch. LangGraph adds stateful agent behaviour for more sophisticated automation.
Pinecone / pgvector
When: RAG implementations — when your AI needs to answer questions based on your own data.
Why: Vector databases allow your AI to search and retrieve relevant context from large document sets, dramatically improving answer accuracy and reducing hallucination.
Vercel AI SDK
When: Streaming AI responses in Next.js applications.
Why: Makes streaming responses trivially easy to implement, with built-in UI components for chat interfaces.
TIMELINES
What can you add AI to in 2–4 weeks?
2 weeks — AI feature addition
AI chatbot, document summariser, smart search, content generator
- ·Single AI feature integrated into existing product
- ·Prompt engineering
- ·Basic UI
- ·Error handling
3–4 weeks — AI-first feature set
AI assistant with memory, RAG system over company data, multi-step AI workflow
- ·Multiple connected AI features
- ·RAG pipeline
- ·Conversation memory
- ·Admin controls
- ·Usage analytics
4–8 weeks — AI-first product
AI writing tool, AI research assistant, AI-powered analytics product
- ·Product built around AI as the core value proposition
- ·Custom pipeline
- ·Full UI/UX
WHAT TO EXPECT
What a typical AI integration looks like
AI projects vary significantly. This reflects a standard 4-week RAG integration.
OUR COMMITMENTS
What we promise on every AI integration
We will tell you what AI cannot do as clearly as what it can. If your use case is not a good fit for AI, we will say so in the scoping call — before you spend a penny. We would rather lose a project than oversell a technology.
Our honesty promise
Dev Empire guarantee
Every AI integration we build includes usage tracking and cost monitoring from day one. You will always know what the AI is costing you per query, per month, and per user — and we will help you optimise it.
Our cost transparency promise
Dev Empire guarantee
We're looking for our first AI integration clients
Add AI to your product at a founding client rate — in exchange for your honest review and a case study once the integration is live.
FAQ
Common questions
Ready to add AI to your product?
Book a free 30-minute call. We'll review your use case, recommend the right AI approach, and send you a fixed-price quote within 24 hours.
OpenAI · Anthropic · LangChain · Fixed price · 2–4 week delivery