AI Solutions
We build AI products that survive contact with real users — retrieval systems, multi-agent workflows, evaluation pipelines, and model integrations designed for production, not demo day.
Founders and product teams in the UAE, Germany, and across Europe come to us when off-the-shelf AI tools stop scaling and they need engineers who understand both LLMs and software delivery.
What's included
Everything we deliver on this engagement
- AI product strategy — choosing RAG, fine-tuning, or agent architectures for your use case
- Retrieval-augmented generation with vector stores and document pipelines
- Multi-agent orchestration with tool calling and human escalation paths
- Evaluation suites, guardrails, and regression testing for model outputs
- GPT-4, Claude, and open-model routing with cost and latency controls
- Python and Node.js inference services with observability built in
- Admin interfaces for prompt management, logs, and quality review
- Post-launch tuning based on real user conversations and failure modes
Our process
How we deliver ai solutions
- 01
Define the AI product
We clarify the job-to-be-done, success metrics, and data sources before selecting models or frameworks.
- 02
Prototype & evaluate
A working slice with eval datasets so you see accuracy and failure modes on your real content, not synthetic examples.
- 03
Productionize
APIs, auth, rate limits, logging, and deployment on Docker or cloud — the unglamorous work that keeps AI reliable.
- 04
Iterate with data
Weekly improvements to prompts, retrieval, and guardrails driven by production telemetry.
Tech stack
Tools we use for ai solutions
- GPT-4
- Python
- Node.js
- Next.js
- Supabase
- Firebase
- Docker
- OpenAI API
FAQ
Common questions about ai solutions
- How much does custom AI development cost?
- Focused AI features (RAG search, single agent) often start at $15k–$35k. Full AI-native products with multiple workflows and eval infrastructure typically run $40k–$80k+ scoped in phases.
- How long does it take to build an AI product?
- A production RAG feature or internal copilot usually ships in 6–10 weeks. Multi-agent platforms with compliance needs run 12–16 weeks with staged rollouts.
- Do you fine-tune models or use RAG?
- We recommend RAG first when your knowledge changes frequently. Fine-tuning makes sense for stable tone, format, or domain vocabulary. Often we combine both.
- Can you integrate AI into our existing Laravel or Next.js app?
- Yes — that is most of our work. We add inference services behind your existing auth, billing, and UI rather than rebuilding everything around AI.
Related
See this work in context
Ready to scope ai solutions?
Tell us about your product, timeline, and constraints. We reply within one business day with next steps — no generic pitch deck.
Your next big launch
starts here.
Book a free 30 minutes discovery call — we'll map your idea, timeline, and the fastest path to ship.
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