AI features & LLM app development
We add AI where it actually helps users: streaming chat that answers from your content, adaptive recommendations, translation and on-device models. We treat model output as a draft that needs guardrails, evaluation and a good UI.
Top Rated Plus on Upwork · $200K+ earned · 44 Upwork jobs · 10+ engineers
Sound familiar?
- You want an AI assistant inside your app, not a separate demo
- Answers need to come from your own content
- You are unsure whether you need RAG or fine-tuning
What you get
- LLM integrations with streaming responses
- RAG pipelines over your documents and data
- Prompt and output evaluations
- Recommendation and personalisation logic
- Translation and NLP models
- On-device ML (Core ML) for mobile
How it works
- 01
Define the job the AI should do
- 02
Prepare data and retrieval
- 03
Build into the product
- 04
Evaluate and add guardrails
- 05
Monitor and improve
Tech we use
- Anthropic (Claude)
- LangChain
- Qdrant
- Hugging Face
- Python
- Firebase Functions
- Core ML
What clients say
“Ahsan is without doubt one of the best React-Native devs I have worked with. Super smart, takes initiative, pays attention to detail. Very good communicator. I will certainly be on the lookout to hire him in my upcoming project. Well done Ahsan.”
“Ahsan delivered excellent work on a React Native mobile app with AI-powered backend integration.”
“Ahsan was a pleasure to work with and delivered outstanding results on our mobile app stabilization project.”
Questions
Should I use RAG or fine-tuning?
Usually start with RAG so the model answers from your data; fine-tune once real usage shows you need a specific behaviour.
Which AI products have you shipped?
HelloBible's AI study chat, Evolve AI's adaptive workout coaching and Olive Arabic's AI tutor and translation model.
Get a quote for ai
Tell us your budget and timeline. We reply within one business day. Prefer to talk? Message us on WhatsApp or email contact@algoastics.com.