Leadership wants a generative AI strategy but nobody agrees on what to actually build?
AI prototype worked in demo -- now struggling to make it reliable in production?
Generative AI Consulting Services
Generative AI is real. So is the failure rate on generative AI projects -- typically caused by unclear use cases, wrong model choices, or production systems that do not hold up outside a demo environment.
We help product and engineering leaders identify which generative AI applications are worth building, select the right models and architecture, and design the production system before anyone starts writing prompts.
Use case assessment -- which generative AI applications justify the investment
Model selection across GPT-4o, Claude, Gemini, Llama, and open-source options
RAG, fine-tuning, and agent architecture design for your specific requirements
Production readiness review for AI systems already in development
RaftLabs provides generative AI consulting for product teams and engineering leaders evaluating or building with LLMs. Our consulting covers generative AI use case assessment and prioritisation, model selection (GPT-4o, Claude, Gemini, Llama), architecture design for RAG pipelines, multi-agent systems, and fine-tuning, production readiness review, and AI governance and evaluation frameworks. We help teams build the right thing -- and avoid the failure modes that kill most generative AI projects.
Most generative AI projects fail on the production side
The demo is easy. A GPT-4 API call with a well-crafted prompt produces impressive output in an afternoon. The production system -- consistent, evaluated, cost-managed, and monitored -- takes months to get right.
Most generative AI consulting failures happen because teams skip the architecture work and go straight to prompting. The result: impressive demos, unreliable production systems, and engineering time spent firefighting instead of building.
What we cover
Use case assessment and prioritisation
Structured evaluation of your proposed generative AI use cases against four dimensions: value (what business outcome improves?), feasibility (does the data and technology support it?), risk (what are the failure modes and their consequences?), and effort (build complexity and ongoing maintenance). Most assessments reveal 1--2 use cases worth building immediately and several that should wait for better data or lower model costs.
Model selection and evaluation
Comparative evaluation of frontier models (GPT-4o, Claude 3.5/3.7, Gemini 1.5 Pro) and open-source models (Llama 3, Mistral, Qwen) against your specific use case, quality requirements, latency targets, and cost constraints. Token cost modelling at your expected volume. Hosted vs. self-hosted trade-off analysis. The right model is not always the most expensive one.
RAG and knowledge system design
Architecture design for retrieval-augmented generation systems: document processing pipeline, embedding model selection, vector database choice (Pinecone, Weaviate, pgvector), retrieval strategy (semantic, hybrid, re-ranking), and context window management. Evaluation framework for retrieval quality -- separate from generation quality. Most enterprise RAG systems fail on retrieval, not on generation.
Agent and multi-agent architecture
Architecture design for AI agent systems: tool use, function calling, memory and state management, agent orchestration (LangGraph, custom), and multi-agent coordination patterns. Failure mode analysis -- agents that loop, hallucinate tool inputs, or produce unsafe actions need guardrails designed before deployment. See our agentic AI development page for implementation.
Production readiness review
Assessment of an AI system already in development against production requirements: evaluation framework, latency and cost benchmarks, hallucination handling, error states and graceful degradation, monitoring and logging, and data privacy compliance. Produces a prioritised list of issues to address before launch -- not a general critique, a specific fix list.
AI governance and evaluation framework
Design of repeatable evaluation processes for generative AI outputs: golden test sets, automated quality checks, human review sampling, and regression testing for model updates. Governance policies for acceptable use, output review requirements, and feedback capture. The infrastructure that lets you confidently update models and prompts without releasing regressions.
Tell us what you are trying to build or evaluate.
Use case, current state, and the decision you need clarity on. We will structure the right consulting engagement.
Related services
Generative AI Integration -- adding generative AI to existing applications
Custom AI Development -- building AI-native products from scratch
RAG Pipeline Development -- retrieval-augmented generation systems
Multi-Agent Systems -- AI agent architectures and orchestration
AI PoC Development -- proof of concept before committing to full development
Frequently asked questions
Generative AI consulting covers the strategic and architectural decisions that determine whether a generative AI project succeeds or fails -- use case selection, model choice, architecture design (RAG vs. fine-tuning vs. prompt engineering), evaluation framework, cost modelling, and production requirements. It is the work that prevents teams from building impressive demos that fall apart in production, or spending development budget on use cases that don't justify the investment.
Worth pursuing: use cases with high-volume, repetitive text generation (document drafting, email composition, support response suggestion) where current manual effort is measurable. Use cases where AI-generated content can be reviewed before use (draft, not final output). Use cases where the cost of wrong answers is acceptable and reviewable. Not worth pursuing: use cases where accuracy is 100% required and AI errors have serious consequences without review. Use cases where the underlying data does not support the use case. Use cases where simpler rule-based systems would work.
Prompt engineering (system prompts, few-shot examples): try this first for any use case. It requires no training data, deploys immediately, and works well for a wider range of tasks than expected. RAG (retrieval-augmented generation): when you need the model to answer questions about your specific documents, knowledge base, or product data that the base model does not know. Fine-tuning: when you need consistent output format or style that prompt engineering cannot reliably achieve, and you have hundreds to thousands of high-quality examples. Most production use cases use RAG for knowledge grounding and prompt engineering for format control.
Production readiness for generative AI requires: an evaluation framework (automated tests on representative inputs with pass/fail criteria, not just manual review), latency and cost benchmarks under expected load, hallucination detection for high-stakes outputs, graceful degradation when the model returns low-confidence or out-of-scope responses, and a feedback loop for capturing failures in production. Systems that pass demos but lack evaluation frameworks are not production-ready.
A focused use case assessment for a single application takes 1--2 weeks. A broader generative AI strategy engagement covering multiple use cases, architecture design, model selection, and build roadmap takes 3--6 weeks. For teams with an AI system already in development, a production readiness review takes 1--2 weeks and typically surfaces 5--10 specific issues to address before launch.
A focused use case assessment for a single application runs $6,000--$15,000. A broader AI strategy engagement with multiple use cases and architecture design runs $15,000--$40,000. A production readiness review for an existing AI system runs $8,000--$20,000. All engagements are fixed-price with a defined scope and deliverable.