AI & Automation · AI Consulting
Generative AI Strategy Consulting Services in Dubai
Turn generative AI ideas into a practical, secure, and build-ready plan. 10turtle provides Generative AI strategy consulting for businesses in Dubai and across the UAE. We help leadership and technology teams decide where generative AI can create real business value, which use cases are worth pursuing, which AI models fit each use case, whether to build, buy, or integrate, whether to use RAG, fine-tuning, prompting, or a combination, how sensitive data should flow through the system, what security and governance controls are required, how much the solution may cost to operate, and what architecture a development team should build. The goal is not another AI presentation. The goal is a decision-ready plan that a development team can actually execute.
Vendor-neutral acrossOpenAIAnthropicGoogle GeminiOpen-source LLMsRAGVector databases
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What it is
What Is Generative AI Strategy Consulting?
Generative AI strategy consulting is advisory work that determines how and where generative AI should be used in a business. Generative AI can create text, images, video, audio, code, summaries, and structured content. But the availability of the technology does not mean every business problem needs it.
A GenAI strategy answers: Where does generative AI fit? What should we build? Which model should we use? Should we build or buy? Should we use RAG or fine-tuning? How do we protect our data? How do we control AI output? What should the implementation cost? The current 10turtle service positions Generative AI strategy as the decision layer before the build, covering use-case fit, model selection, build-vs-buy, RAG-vs-fine-tuning, secure architecture, and output controls.
What's included
What You Receive
GenAI Use-Case InventoryA structured list of potential applications. We identify use cases where generative AI can provide a meaningful advantage, such as AI customer support, internal knowledge assistants, AI sales assistants, proposal generation, document summarization, content workflows, research assistants, AI copilots, and AI-powered search. The focus is on the business problem first.
Model ComparisonA practical comparison of appropriate AI models. We compare models based on capability, accuracy, context requirements, latency, cost, privacy, deployment options, licensing, and integration requirements. Potential options include OpenAI, Anthropic, Google Gemini, open-source LLMs, private models, and cloud AI platforms.
Build vs Buy AnalysisAn assessment of custom development versus existing products. Buy when an existing product already solves the business problem well. Build when the workflow is unique, private data is central, the AI experience is part of your product, or existing tools do not fit. Integrate when the best answer is connecting an existing AI capability to your current software.
RAG vs Fine-Tuning DecisionA documented recommendation based on the actual use case. RAG connects the model to your current business information. Fine-tuning changes how the model behaves for specialized tasks and consistent formats. Prompting may be enough when the model already performs the task correctly. Some applications need RAG, fine-tuning, and prompting together.
Security and Data Flow PlanHow sensitive information should move through the system. We evaluate what data the model receives, where data is processed and stored, who can access it, which third-party providers are involved, and whether data is retained. Possible approaches include private deployments, self-hosted models, controlled APIs, retrieval boundaries, data redaction, access controls, encryption, and environment separation.
Risk and Governance FrameworkControls around AI output, data, and usage. A governance strategy can cover approved AI use cases, data access, model selection, human oversight, output review, security, privacy, monitoring, vendor management, incident handling, and documentation. Guardrails can include input and output validation, structured responses, content filtering, source requirements, confidence checks, human review, restricted actions, and escalation rules.
Costed Implementation PlanA build-ready plan with expected technical and operational considerations. Depending on scope, the strategy engagement can also include prioritized use cases and an architecture recommendation. The final output is a decision and architecture brief that a build team can execute.
How we work
Generative AI Strategy Process
1Discovery
We understand business objectives, current workflows, AI initiatives, data, technology, and constraints.
2Use-Case Assessment
We identify where GenAI may create measurable value.
3Feasibility and Data Review
We assess data, accuracy requirements, integration, risk, and cost.
4Model and Platform Selection
We compare suitable models and platforms.
5Architecture
We define the model, RAG, APIs, data flow, integrations, and security.
6Build vs Buy
We evaluate custom development against existing products.
7Risk and Governance
We define controls around data, accuracy, security, human oversight, and AI output.
8Costed Plan and Handoff
We provide the development and operating considerations needed for the next decision. The final strategy and architecture are documented so a build team can execute it.
Why it matters
Why Choose 10turtle for Generative AI Strategy?
We help businesses move from GenAI experimentation to a practical implementation plan that a development team can actually execute.
Vendor-Neutral
We evaluate models based on your use case rather than following model hype.
Build-Aware
The strategy is designed so a development team can actually implement it, accounting for models, data, APIs, retrieval, security, and production architecture.
Security by Design
Data exposure and AI output controls are considered before deployment. If an existing product solves the problem better than a custom build, the strategy should say so.
Who this is best for
The right fit
Best fit when
This service is a good fit when you see potential in GenAI but do not know where to start, leadership has multiple AI ideas, you are unsure which model to choose, you are deciding between RAG and fine-tuning, you are unsure whether to build or buy, sensitive data is involved, a GenAI pilot has stalled, you need a secure architecture, or you need a costed plan before funding development.
You might not need this
You may not need this engagement if the use case is already defined, the model is already selected, the architecture is already agreed, you simply need development, or you need an existing AI feature integrated. In those cases, a direct AI Development and Integration engagement may be more appropriate. For agent-specific strategy, the Agentic AI Strategy service is more appropriate.
FAQs
Frequently Asked Questions
What is Generative AI strategy consulting?
It is advisory work that determines where generative AI should be used, which models and platforms fit, how the system should be architected, and what security and governance controls are required.
What does a GenAI strategy include?
It can include use-case discovery, use-case prioritization, model selection, build-vs-buy analysis, RAG-vs-fine-tuning decisions, architecture, data strategy, security, governance, and cost planning.
Which generative AI models do you work with?
Potential options include OpenAI, Anthropic, Google Gemini, and open-source LLMs. The model is selected based on capability, cost, latency, licensing, privacy, and deployment requirements.
How do I choose the right LLM?
There is no universal best model. The right model depends on use case, accuracy, context, latency, cost, data, licensing, and deployment. We recommend testing shortlisted models against real business tasks rather than choosing based on benchmark headlines alone.
Should we use RAG or fine-tuning?
RAG is generally suited to current or proprietary knowledge. Fine-tuning is suited to specialized and stable behavior. Prompting may be enough for simpler use cases. The correct choice depends on your requirements.
Should we build or buy a GenAI solution?
If an existing product solves the problem well, buying may be more efficient. Custom development can make sense when the workflow is unique, the AI experience is core to your product, or existing products cannot meet the requirements.
How do you protect our data?
The architecture can use appropriate data boundaries, access controls, private infrastructure, self-hosted models, controlled APIs, and other security mechanisms depending on the requirements. The data flow is designed before implementation rather than treated as an afterthought.
Can you create a private GenAI architecture?
Yes. Private or controlled deployments can be evaluated when data sensitivity, compliance, or infrastructure requirements make public-model usage unsuitable.
Can GenAI strategy cover AI agents?
It can identify where agents may be relevant, but autonomous agents have a separate strategy because they introduce additional action, permission, orchestration, and operational risks. For agent-specific strategy, the Agentic AI Strategy service is more appropriate.
Does this service include development?
The strategy engagement is the advisory layer. It ends at an agreed architecture and plan. Development can then be scoped separately if required.
How long does GenAI strategy consulting take?
The timeline depends on number of use cases, organization size, data complexity, stakeholders, architecture depth, and governance requirements. A focused strategy can be much faster than an enterprise-wide GenAI program.
How much does Generative AI strategy consulting cost in Dubai?
There is no single fixed price. Cost depends on number of use cases, assessment depth, model evaluation, architecture complexity, data review, governance requirements, and stakeholder involvement. A defined scope can be agreed before the engagement begins.
In their words
What working with our AI team is like
Standards we build to
Security & Compliance Standards
ISO 27001 Certified
SOC 2 Type 2
PCI DSS Compliance
GDPR Compliance
CCPA Compliance
ISO 27018 Certified“We follow the principles of GDPR, CCPA, and ISO standards certified to ensure security, privacy, and compliance across all operations.”
Turn GenAI Ideas Into a Build-Ready Plan
Generative AI is easy to demonstrate. The difficult part is deciding where it belongs in a real business. 10turtle can help you determine which GenAI use cases are worth pursuing, which model fits, whether to build or buy, whether you need RAG or fine-tuning, how your data should flow, what controls are required, and what the implementation should look like. You will leave with a practical strategy and architecture your development team can act on.
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