Generative AI Solutions
We build production-grade generative AI systems tailored to your domain. From fine-tuned large language models and retrieval-augmented generation pipelines to autonomous AI agents, we turn cutting-edge research into enterprise-ready products. Our team implements guardrails, hallucination detection, and human-in-the-loop workflows to ensure reliability at scale.
What is Generative AI Solutions?
Generative AI solutions build custom LLM-powered applications using fine-tuning, RAG architectures, and multi-agent orchestration. Services include domain-specific model training with LoRA and QLoRA techniques, hybrid retrieval systems combining semantic search with knowledge graphs, and autonomous AI agent development that plans, reasons, and executes complex multi-step enterprise tasks.
Engineering Targets
Figures below are the benchmarks we design and test against on this type of build. They are targets, not a warranty — what your platform actually achieves depends on your data, scale and integration surface, and we agree the numbers that matter with you before work starts.
Why This Matters
Generative AI is transforming every industry — but off-the-shelf solutions don't understand your business. Systems trained and grounded on your own data answer in your domain language and cite your own sources, which is what makes the output usable rather than merely plausible.
What You Get
Capabilities
Custom LLM Fine-Tuning
Domain-specific model training on your proprietary data using LoRA, QLoRA, and full fine-tuning techniques for maximum accuracy.
RAG Architecture
Hybrid retrieval systems combining semantic search with knowledge graphs for context-aware AI responses.
AI Agent Orchestration
Multi-agent systems that plan, reason, and execute complex multi-step tasks autonomously.
Our Approach
How We Deliver
Use Case Triage
We rank candidate use cases by data readiness, business value and review burden.
Grounded Prototype
A working prototype on your real documents, evaluated against a scored test set.
Hardening
We add guardrails, fallbacks, cost limits and logging before any external exposure.
Release and Measure
Staged rollout with quality tracking, so regressions are caught after each model change.
Real-World Applications
Use Cases
Technology Stack
Explore More
Related Services
Artificial Intelligence
Agentic AI and machine learning solutions — autonomous AI agents, multi-agent systems, and enterprise AI automation that transform your business.
- Agentic AI development
- Multi-agent AI orchestration
- Autonomous AI agent systems
Computer Vision & Image AI
Visual intelligence systems for object detection, medical imaging, quality inspection, and document processing.
- Object detection and recognition
- Medical image analysis (X-ray, MRI, CT)
- Industrial quality inspection
NLP & Conversational AI
Intelligent chatbots, voice assistants, and text analytics that understand human language at scale.
- Conversational AI and chatbot development
- Sentiment analysis and opinion mining
- Named entity recognition (NER)
Common Questions
Frequently Asked Questions
How do you stop a generative AI system from hallucinating?
Grounding comes first: responses are retrieved from your own documents through a RAG pipeline with citations back to source passages, so any answer can be checked. On top of that we add output validation against schemas, confidence thresholds, refusal behaviour for out-of-scope questions, and human review on high-risk actions. Every generation is logged, so a failure can be traced and the retrieval or prompt corrected.
Should we fine-tune a model or use retrieval-augmented generation?
RAG is usually the right starting point when the problem is access to knowledge that changes — policies, contracts, product documentation — because you update the index rather than retrain. Fine-tuning with LoRA or QLoRA suits fixed tasks where tone, output format or a specialised vocabulary matter more than freshness. Many production systems use both, and we test the options against a scored evaluation set before committing.
How much does a generative AI project cost?
Pricing is structured in stages rather than as a single figure. Discovery and use-case triage is fixed price; the grounded prototype is scoped against a defined dataset and evaluation set; production hardening is quoted once the architecture is settled. Running costs — inference tokens, vector storage and embedding refreshes — are modelled separately, so you can see the ongoing bill before committing to a build.
Can you run generative AI on our own infrastructure instead of a public API?
Yes. We deploy open-weight models into your own cloud tenancy or on-premise hardware where data residency, confidentiality or regulator expectations rule out third-party inference endpoints. The application layer is written against an abstraction, so the model provider can be changed later without rewriting the product. Deployment scripts, the evaluation harness and the prompt assets are handed over with the codebase.
Are there projects Masarrati will not take on?
Yes. Masarrati does not build gambling or betting products of any kind — no casino platforms, sports betting, lottery, sweepstakes, prediction markets, or loot-box mechanics — whether as a full product, a feature, or an integration. Masarrati also does not build interest-based (riba) financial products: no lending apps, credit products, or platforms whose revenue depends on charging interest, on web, mobile, or any other platform. These are firm ethical commitments, not capacity constraints. For interest-free finance, we actively build Sharia-compliant fintech — Murabaha and Ijara structures, Takaful models, and Zakat tooling.
Industries We Serve
Related Industries
Healthcare
HIPAA-compliant digital solutions for modern healthcare delivery and patient care.
Islamic Fintech
Shariah-compliant financial technology for the modern Islamic economy.
Information Technology
Enterprise IT solutions that drive efficiency, security, and digital innovation.
Education
EdTech solutions that make learning accessible, engaging, and measurable.
From Our Blog
Related Insights
Prompt Injection Is the New Perimeter: A Security Playbook for AI Agents in UAE Enterprises
OWASP keeps prompt injection at number one for 2026, indirect attacks dominate real incidents, and the UAE is pushing agents into government and enterprise workflows faster than anywhere. Securing them is an architecture problem — here is the playbook.
AI AgentsThe UAE's Federal AI Authority and the Autonomous Government Mandate: What Enterprises Should Build Now
In six weeks the UAE merged AI oversight into a single federal authority, directed half of government services towards autonomous AI within two years, and saw US compute export rules ease. Read together, that is a demand signal and a standards signal.
Artificial IntelligenceThree Frontier Releases in Fifteen Days: What the July 2026 Model Wave Means for Enterprise AI Architecture
GPT-5.6, Gemini 3.6 Flash and Claude Opus 5 shipped within a fortnight, all converging on near-frontier capability at mid-tier prices with million-token contexts. The architectural implications matter more than the benchmarks.