AI-Native Engineering
Initializing AI stack…
From GPT-4 and Claude to custom fine-tuned models — we architect, integrate, and maintain LLM-powered systems that deliver measurable ROI for GCC and global enterprises.
Every engagement comes with these capabilities tailored to your requirements.
Production-grade integrations with OpenAI, Anthropic Claude, Google Gemini, and open-source models — complete with multi-provider failover, semantic caching, streaming responses, and full observability so your AI features stay online and cost-efficient at scale.
Domain-specific fine-tuning on GPT-4o, Mistral, Llama 3, and Gemma using your proprietary data — delivering 30–60% accuracy gains over generic LLMs on niche tasks at 70% lower inference cost.
Enterprise-grade Arabic and English chatbots powered by LLMs — with intent recognition, persistent multi-turn memory, seamless human escalation, and CRM integration across web, WhatsApp, and Slack.
Semantic search and RAG pipelines built on Pinecone, Weaviate, and pgvector — replacing keyword search with natural-language understanding that retrieves the right answer from millions of documents in under 200ms.
Machine learning models that forecast demand, detect anomalies, score leads, and surface risk signals from your operational data — deployed as real-time APIs integrated directly into your existing dashboards and workflows.
Deep domain expertise across regulated and high-growth sectors in the GCC and globally.
A proven engagement process with complete visibility at every stage.
We audit your data pipelines, existing infrastructure, and regulatory constraints to produce a scored AI readiness report and ROI projection.
Workshop with stakeholders to rank AI opportunities by value, feasibility, and compliance risk — producing a signed-off implementation roadmap.
Cleansing, chunking, embedding, and indexing your proprietary data into production vector stores with access controls and audit logging.
Systematic evaluation of model options (cost, accuracy, latency) followed by prompt engineering, few-shot examples, and instruction fine-tuning.
Building secure API endpoints, authentication, rate limiting, and frontend integrations — tested against edge cases and adversarial prompts.
LLM observability via Langfuse or custom dashboards, A/B testing prompts, and monthly model refreshes to maintain accuracy over time.
Answers to the questions our clients ask most before engaging.
Real outcomes from real engagements.
IoT sensor data pipeline and ML-powered predictive maintenance system predicting equipment failures 48 hours in advance — reducing unplanned downtime by 35% for an energy infrastructure operator.
AI-powered citizen knowledge base with Arabic-English RAG pipeline serving 2M+ monthly queries for a GCC government ministry — deflecting 78% of call centre volume while maintaining 99.2% response accuracy.
Real-time shipment intelligence platform processing 500K daily tracking events with ML-powered exception prediction — cutting delivery exceptions by 40% for a regional logistics operator.
Deep-dive articles from our engineers on this service area.
AI code tools speed up development, but they're assistants, not replacements. Here's what they handle well, where they fail, and when custom AI development makes sense.
Read MoreAI identity management frameworks prevent unauthorized access and audit who touches models, data, and outputs. Learn what enterprise AI access control actually requires.
Read MoreAI audit, compliance & risk management frameworks keep enterprise AI deployments secure and auditable. Learn what governance layers matter and how to implement them.
Read More
