Most AI projects fail not because the technology doesn't work, but because the gap between a working prototype and a production system is enormous. We bridge that gap. Our team has shipped AI systems that process millions of requests, handle adversarial inputs gracefully, and maintain accuracy over time as data distributions shift. We don't just integrate APIs — we build the infrastructure around them that makes AI reliable, observable, and maintainable.
Capabilities
What We Deliver
Autonomous AI Agents
Intelligent agents that execute multi-step workflows, make decisions, and take action on behalf of your team — with full transparency into their reasoning.
LLM-Powered Applications
Custom applications built on large language models — chatbots, copilots, content generators, and conversational interfaces tailored to your domain and data.
RAG & Knowledge Systems
Retrieval-augmented generation pipelines that connect your AI to your data — internal knowledge bases, documentation, databases — for accurate, contextual responses.
Model Fine-Tuning
Custom-trained models optimized for your specific use case. We fine-tune foundation models on your data to achieve accuracy that off-the-shelf solutions can't match.
AI Feature Integration
Add intelligent features to your existing products — smart search, auto-categorization, content recommendations, predictive analytics — without rebuilding from scratch.
Semantic Search & Retrieval
Vector-based search systems that understand meaning, not just keywords. Find relevant information across millions of documents in milliseconds.
Process
How We Work
Discovery & Assessment
We evaluate your data, existing systems, and business objectives to determine the right AI approach. Not every problem needs a custom model — sometimes a well-engineered pipeline with existing models is the smarter path.
Architecture & Prototyping
We design the system architecture and build a working prototype that proves the concept works with your actual data. This is where we validate accuracy, latency, and cost before committing to full production development.
Production Development
We build the complete system — error handling, monitoring, fallback logic, rate limiting, caching, and all the infrastructure that separates a demo from a product. Every component is tested against edge cases and adversarial inputs.
Use Cases
Real-World Scenarios
How startups and enterprises use ai development to solve real business problems.
Ship your AI-powered MVP
You have a product idea that requires AI at its core. We help you go from concept to a production-ready MVP — with the architecture designed to scale when your user base grows from 100 to 100,000.
Add AI to your existing product
Your product works, but your users are asking for intelligent features. We integrate AI capabilities — smart search, automated workflows, content generation — into your existing stack without disrupting what's already working.
Automate document processing
Your team spends hours reading, categorizing, and extracting data from documents. We build intelligent document processing pipelines that handle contracts, invoices, reports, and correspondence — extracting structured data and routing it to the right systems automatically.
Build an internal AI copilot
Your team wastes time searching for information across Confluence, Slack, email, and internal tools. We build a custom AI copilot that understands your organization's knowledge and gives employees instant, accurate answers through natural language.
Why Zaidom for AI Development
Production-first mindset
We've seen too many AI projects that work in notebooks but fail in production. Every system we build includes monitoring, error handling, and graceful degradation from day one.
We ship our own AI products
Elliphy, our autonomous trading platform, runs AI agents in production 24/7. We don't just advise on AI — we build and operate it ourselves.
Full-stack AI engineering
We don't just train models. We build the entire stack — data pipelines, inference servers, APIs, frontends, monitoring dashboards — everything needed to put AI in the hands of real users.