Generative AI Consulting
Services

Generative AI consulting services help organizations decide where and how to use generative AI before committing to implementation.

Typical activities include use-case discovery, readiness assessment, business-case development, architecture recommendations, model evaluation, governance planning and pilot roadmapping.

A generative AI consultant evaluates business requirements, workflows, data, systems and organizational constraints to identify viable GenAI opportunities and recommend an implementation approach.

The output is a decision the business can act on, not a technology overview.

Typical deliverables include a use-case inventory, prioritization matrix, readiness assessment, architecture recommendation, model evaluation criteria, governance requirements, pilot scope, KPI framework, implementation roadmap and an executive business case.

The exact deliverables depend on the engagement.

We evaluate potential use cases against business value, user value, technical feasibility, data readiness, implementation effort, operational requirements and adoption complexity.

Scoring every candidate on the same dimensions is what makes the shortlist defensible to a budget holder.

No. Model selection should follow the business requirement.

The consulting process can compare suitable managed and open models based on quality, deployment, latency, context and operating requirements.

Not necessarily. Part of readiness assessment is identifying which data is required, its current condition, ownership, accessibility and the gaps that need to be addressed before implementation.

Discovering that the data is not ready is a valid and valuable outcome.

A readiness assessment reviews whether your organization has the business ownership, data, systems, infrastructure, security controls, governance processes and skills required for the proposed GenAI initiative.

We first establish the current process baseline. Then we define the metric the proposed application should influence, estimate realistic adoption and operating costs, and establish a success threshold for the pilot.

Without a baseline measured before the pilot, any improvement claim afterwards is unverifiable.

Yes. Consulting can define the high-level application architecture and determine whether the use case is best served by an LLM application, RAG, Agentic AI, generative media or another approach.

Detailed implementation belongs to the relevant engineering service.

RAG is appropriate when the proposed application needs reliable access to proprietary or frequently changing information.

For implementation, use our dedicated RAG development services.

Agentic AI is appropriate when the application must progress through a workflow, select tools or perform controlled actions rather than only generate information.

For implementation, use our agentic AI development services.

Consulting determines what should be built, why it should be built and how the initiative should be structured.

Development turns the approved use case and architecture into a production application - see generative AI development services.

Generative AI consulting focuses on foundation models, LLM applications, RAG, generative media and Agentic AI.

General AI consulting can cover a broader range of artificial intelligence including traditional machine learning, predictive analytics, optimization and computer vision.

Yes. The evaluation should be based on representative business tasks and requirements such as quality, context, latency, deployment, privacy and operating cost.

We do not standardize on one provider, because the right answer changes by workload and by how tightly you want to depend on a single vendor.

Yes. A GenAI consulting engagement can define the governance requirements for a specific initiative, including ownership, human oversight, access, model controls, evaluation and logging.

Organization-wide AI governance is a broader programme and should be scoped as its own engagement rather than folded into a use-case roadmap.

Yes. Once the business case, architecture and pilot are approved, the initiative can move into the relevant delivery team: generative AI development, LLM development, RAG development, agentic AI development, or AI integration and implementation.

The implementation backlog produced during consulting is written so an engineering team can pick it up without re-running discovery.

The duration depends on the number of use cases, stakeholders, systems and depth of assessment required.

A focused workshop may establish initial priorities quickly, while an enterprise readiness and roadmap engagement requires deeper business, technical and governance analysis.

That is a legitimate and often valuable outcome. Ruling a use case out early costs a fraction of discovering the same thing after a build.

Where a problem is better solved by conventional software, automation or classical machine learning, the roadmap says so and points at the approach that fits.