AI-POWERED PRODUCTS
AI-powered products that hold up in production.
Your competitors are already shipping AI features. We add AI to your product or build AI-first from the ground up, with tools that hold up in production.
THE PROBLEM
Why most AI integrations fail
Most AI features get bolted on, not built in. The answers come back inconsistent and off-brand. The bill climbs because nobody planned for token usage or caching.
Good AI is an engineering problem, not a feature request. It comes down to prompt design, caching, error handling, and the right model. Sometimes that means no AI at all.
HOW WE BUILD
AI integration that works in production
AI audit & strategy
Days 1–3We audit your product and your data before any code. You get a strategy: which models, which approach — RAG, fine-tuning or direct prompting — and the running costs.
Prototype
Week 1Week one gives you a working prototype of the core AI feature — not a demo, but a real integration against your data. You test it and we iterate.
Production integration
Weeks 2–3We wire the proven feature into your product with error handling, fallback behaviour, caching and monitoring — the parts that decide whether it survives real traffic.
Optimisation & handover
Week 4We tune prompts for consistency and cost, document the integration, and hand over with clear guidance on evolving the AI as the models improve.
TECH STACK
The AI tools that work in production
The landscape moves fast. These are the tools we use and when we reach for each.
OpenAI API
When: Most language tasks — chat, summarising, extraction, generation.
Why: Strong performance, solid docs, and a model range that balances quality against cost.
Anthropic Claude API
When: Long documents, precise instruction following, safety-critical work.
Why: Follows complex instructions without drifting, and holds up better on long documents.
LangChain / LangGraph
When: Multi-step AI workflows, agent-based systems, RAG pipelines.
Why: Scaffolding for complex workflows, so we don't rebuild it. LangGraph adds stateful agents.
Pinecone / pgvector
When: RAG — when the AI answers from your own data.
Why: Retrieves the right context from large document sets, which cuts hallucination sharply.
Vercel AI SDK
When: Streaming AI responses in Next.js applications.
Why: Streaming responses in a few lines, with built-in chat UI components.
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 tell you what AI cannot do as clearly as what it can. A bad fit, and you hear it in the scoping call.
Our honesty promise
Dev Empire guarantee
Every integration ships with usage tracking from day one. You always know what the AI costs per query, per user and per month.
Our cost transparency promise
Dev Empire guarantee
We're looking for our first AI integration clients
Founding rate, plus a direct line to the founders as your AI features evolve.
FAQ
Common questions
Ready to add AI to your product?
We'll review your use case, recommend an AI approach, and send a fixed-price quote within 24 hours.
OpenAI · Anthropic · LangChain · Fixed price · 2–4 week delivery