Services
Fine-Tuning & Custom AI Training for Mission-Critical Web Platforms

Fine-Tuning & Custom AI Training for Mission-Critical Web Platforms

Stralya helps you move beyond generic AI. We fine-tune large language models and train custom models on your own data, workflows, and compliance requirements—so your cloud-native web platforms in Dubai deliver accurate, reliable, and secure AI experiences at scale.

Use cases & scope

What We Fine-Tune and Train for Your Web Platforms

Fine-tuning and custom training are most effective when they are tightly scoped around clear, high-value use cases. Stralya helps you identify where AI can create real leverage in your digital products—then builds models that perform reliably in those specific contexts.

Typical fine-tuning & custom AI training scenarios

Domain-specific conversational assistants for customer portals, investor dashboards, or government services, grounded in your own documentation and policies.

Smart routing and triage for support tickets, leads, or internal requests, trained on your historical data and business rules.

Document understanding and summarisation for contracts, RFPs, regulatory texts, and internal knowledge bases.

Classification and tagging models for large content libraries, listings, or knowledge repositories.

Evaluation and safety layers that score and filter model outputs for quality, bias, and policy violations before they reach end-users.

Optional add-ons & extensions

Evaluation and safety layers that score and filter model outputs for quality, bias, and policy violations before they reach end-users.

Custom analytics dashboards for monitoring model performance, feedback, and business impact.

A/B testing frameworks to compare different models, prompts, or fine-tuning strategies in production.

Internal enablement sessions and training for your product, data, and engineering teams in Dubai.

Security and compliance reviews aligned with your sector (finance, real estate, public sector, healthcare, etc.)

Every engagement is scoped to your reality: your stack, your data maturity, and your risk appetite. Whether you are launching a new AI-native product or upgrading an existing platform, we design the minimum robust architecture that can scale with your ambition.

Designed for High-Stakes Projects in Dubai and the GCC

Aligned with strategic digital mandates

We understand the pressure on Dubai organisations to deliver visible, high-impact digital experiences. Our fine-tuning and training work is always tied to clear business outcomes, not just technical curiosity.

A partner, not a generic vendor

Stralya is built as a boutique expert firm. You work directly with senior engineers and architects who take ownership of your project, from first workshop to long-term maintenance.

Performance, cost, and quality in balance

We optimise not only for model accuracy but also for latency, user experience, and cloud costs. This balance is critical when AI becomes a core feature of your platform, not a side experiment.

Premium standards, controlled growth

Our vision is to be the reference for strategic web and AI projects in the UAE. We take on fewer projects so we can maintain the level of rigor, communication, and technical excellence that high-stakes initiatives demand.

Process

How We Run Fine-Tuning & Custom Training Projects

Every fine-tuning or custom AI training engagement at Stralya is run as a structured, fixed-scope project. We combine data engineering, MLOps, and cloud-native web development so that your AI features are robust in real-world conditions—not only in a lab.

We start by clarifying the business objective, risk level, target users, and success criteria. Together we validate that fine-tuning or custom training is the right lever versus simpler options (prompting, RAG, rules). If we cannot guarantee value, we say no.

We inventory your available data (documents, tickets, CRM, product data, logs), assess quality and sensitivity, and define what can safely be used. We then clean, label, and structure datasets for training, validation, and testing, with clear governance rules.

We select the right model family and providers (open-source, cloud provider, or vendor APIs) based on performance, latency, cost, and compliance. We design a cloud-native architecture for training, inference, and monitoring that fits your existing stack.

We run controlled training or fine-tuning cycles, measuring performance against realistic test sets and edge cases. We iterate until we reach agreed acceptance thresholds for quality, robustness, and safety (hallucinations, bias, security).

We expose the model through stable APIs or microservices and integrate it into your web application’s UX. We handle authentication, rate limiting, logging, and observability so your team can operate the system with confidence.

Once in production, we monitor model behaviour, feedback loops, and costs. We provide SLAs for maintenance, retraining when your data evolves, and continuous improvements—so your AI stays aligned with your business over time.

Find Commonly Asked Questions

It depends on your use case and risk level. For many applications, a well-designed RAG pipeline and strong prompt engineering are enough. Fine-tuning becomes relevant when you need consistent tone, domain-specific reasoning, or task-specific performance that generic models cannot reach. During discovery, we will benchmark options and only recommend fine-tuning if it clearly adds value.

We are cloud-native and provider-agnostic. We work with AWS, Azure, and GCP, and integrate models from major vendors as well as strong open-source options when appropriate. Our priority is to choose a stack that matches your compliance, latency, and cost constraints while remaining maintainable by your team.

We design data flows to respect your internal policies and local regulations. This can include isolating training environments in your own cloud account, anonymising or pseudonymising data, and avoiding providers that reuse training data. We will never send your sensitive data to third parties without explicit approval and contractual protection.

Yes. Stralya has a strong “project rescue” culture. We can audit your current architecture, prompts, models, and data pipelines, then propose a pragmatic recovery plan. In many cases, we stabilise and improve existing systems before deciding whether a fresh fine-tuning or retraining effort is required.

For a focused use case with reasonably clean data, expect 4–8 weeks from discovery to initial production deployment. More complex, multi-use-case or heavily regulated environments can take longer, especially if data preparation and governance need significant work. We always provide a clear, phased roadmap before we start.

Yes. In line with Stralya’s project-first philosophy, we favour fixed-price, well-scoped engagements. Once we understand your objectives and data landscape, we define a clear scope, success criteria, and delivery plan, then commit on price and timelines.

Case Studies