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Generative AI Consultant
A generative AI consultant that gets you past the demo and into production.
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Trusted by
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
01
Leadership wants a generative AI strategy but nobody agrees on what to actually build?
02
AI prototype worked in demo, now struggling to make it reliable in production?
Plain answer
RaftLabs works as a generative AI consultant for product and engineering teams across the US, UK, Europe, Canada, and the UAE evaluating or building with LLMs. We cover use case assessment, model selection, RAG architecture, and production readiness review. Engagements are fixed-price with a defined deliverable.
What to remember
- Engagements are fixed-price with a defined deliverable, covering use case assessment, model selection, RAG architecture, and production readiness review.
- A focused use case assessment covers a single application; a broader strategy engagement covers multiple use cases and architecture design; a production readiness review assesses an existing AI system before launch.
- Model selection covers GPT-4o, Claude, Gemini, Llama, and open-source options evaluated against your actual inputs, latency, and cost per query.
The pilot everyone loved, until it met real traffic.
A team ships a generative AI pilot that nails every question in the demo. Leadership greenlights it. Then it hits production volume: answers drift, per-query cost balloons, a hallucination slips through with no fallback, and nobody built the evaluation harness to catch any of it.
Nothing was wrong with the model. What was missing was the architecture decision made before the first prompt, the one that separates a project that ships from one that gets quietly cancelled.
Decide it up front, or firefight it after launch.
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.
According to Gartner's July 2024 research, 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, citing poor data quality, escalating costs, and unclear business value as the primary causes. The architecture decisions made before the first line of production code determine whether a project lands in the 70% that ship or the 30% that get cancelled.
RaftLabs has shipped production software since 2015 for teams across the US, UK, Europe, Canada, and the UAE. Recent generative AI work runs on Claude, GPT, and Gemini, grounded in RAG and shipped with real evaluation harnesses rather than demo prompts. One team scopes the engagement, designs the architecture, and stays through production, with data, privacy, and compliance requirements scoped from the start, not retrofitted before launch.
Proof
- generative AI systems live for real users
- In production
- Claude, GPT, and Gemini, grounded in RAG
- defined deliverable agreed before work starts
- Fixed price
- Every engagement
Generative AI pays off when the work is reviewable and the volume is real.
Everything on the left should already be true for your use case. Even one thing on the right, and prompt engineering or a rule-based tool is the smarter first step.
A fit
A high-volume, repetitive text or document workflow where the current manual effort is measurable.
AI-generated output can be reviewed before it's used, and the cost of a wrong answer is acceptable and reviewable.
You're a product or engineering leader evaluating LLMs or already building, with budget for a fixed-price engagement.
Not a fit
Accuracy must be 100% and AI errors carry serious consequences with no human review.
A simpler rule-based system would already solve the problem.
The underlying data doesn't support the use case yet.
What we build
What we cover
Use case assessment and prioritisation
Structured evaluation of your proposed generative AI use cases against four dimensions: value, feasibility, risk, and effort. The output separates use cases worth building next from ones worth waiting on and ones that don't survive honest feasibility analysis.
Model selection and evaluation
Comparative evaluation of frontier and open-source models against your use case: output quality on your actual inputs, latency at your expected volume, and cost per query. The recommendation covers a primary model, a fallback for availability events, and the conditions where a cheaper or faster model is acceptable. We model this across GPT-4o, Claude, Gemini, and Llama, with token cost modelling and a primary-plus-fallback selection.
RAG and knowledge system design
Architecture design for retrieval-augmented generation systems before you build them, because chunking, embedding, and vector database choices are expensive to change once production queries are running. The design covers chunking and embedding strategy, vector database selection, and a retrieval evaluation framework.
Agent and multi-agent architecture
Architecture design for AI agent systems that plan, use tools, and execute multi-step tasks. Failure mode analysis is the most valuable part: agents that loop, hallucinate tool arguments, or act unsafely each need a specific guardrail before deployment. The output is an architecture document your team can build from, specifying tool definitions, memory architecture, a LangGraph or custom state machine, and multi-agent orchestration.
Production readiness review
Structured assessment of an AI system in development against what production requires: evaluation coverage, latency and cost at realistic traffic, hallucination handling, graceful degradation when the model API fails, monitoring completeness, and data privacy compliance. The output is a prioritised fix list with severity ratings, ordered by the probability and consequence of each issue failing in production.
AI governance and evaluation framework
Design of the repeatable processes that let you ship model, prompt, and pipeline changes without quality regressions. Golden test sets, CI/CD evaluation, and LLM-as-judge scoring back every model and prompt update, RAGAS metrics gate RAG deployments, and output guardrails enforce content policies and PII redaction before responses reach users.
AI models and infrastructure we work with
Model selection is a technical decision with significant cost implications at production scale. We evaluate models against your specific use case on your actual inputs before recommending one. The table below is the starting frame, not the answer, the right pick depends on your inputs, latency budget, and volume.
Frontier models at a glance
| Model | Context window | Latency | Relative cost | Best fit |
|---|---|---|---|---|
| OpenAI GPT-4o | 128K tokens | Low | Mid | General reasoning, multimodal input, broad tool use |
| Anthropic Claude 3.5 Sonnet | 200K tokens | Low to mid | Mid | Long-context reasoning and careful instruction-following with low hallucination |
| Google Gemini 1.5 Pro | 1M tokens | Mid | Mid | Very long documents without chunking; teams already on Google Cloud |
| Meta Llama 3 (self-hosted) | 128K tokens | Depends on your GPU | Low per query at volume, high fixed infra | Data sovereignty and high volume where per-token API cost dominates |
Context windows and capabilities follow each provider's published model documentation (OpenAI, Anthropic, Google, and Meta, 2025). List pricing moves often, so we benchmark current per-token cost against your actual inputs during selection rather than quoting a rate that dates fast. A common production pattern pairs a small, cheap model for the routine bulk of requests with a frontier model for the hard minority.
Frontier and open-source models we deploy
OpenAI GPT-4o and GPT-4o mini
GPT-4o for general reasoning, instruction-following, and multimodal tasks (text, image, and audio input). GPT-4o mini for cost-sensitive workloads where latency and per-token cost matter more than peak reasoning capability.
Anthropic Claude (Sonnet and Haiku)
Claude 3.5 Sonnet for complex multi-step reasoning, long-context tasks (up to 200K tokens), and use cases requiring careful instruction-following with minimal hallucination. Claude Haiku for high-volume, low-latency workloads. Prompt caching reduces input token cost on cache hits for high-repetition system prompts.
Google Gemini 1.5 Pro and Flash
Gemini 1.5 Pro for teams already on Google Cloud or Google Workspace infrastructure. One million token context window makes it useful for long-document analysis tasks where other models require chunking. Gemini Flash for latency-sensitive applications requiring sub-second response times.
Meta Llama 3 and open-source models
Self-hosted Llama 3 for use cases with data sovereignty requirements that prohibit routing data through a third-party API. Inference on AWS (EC2 G5, p4d instances) or GCP (A100, H100). Open-source models eliminate per-token cost at volume but introduce GPU infrastructure and MLOps operational overhead. We assess the cost crossover point before recommending self-hosting.
Mistral and specialised models
Mistral models for European data residency requirements (Mistral AI is based in France and operates EU infrastructure). DeepSeek for coding-specific tasks. Domain-specific fine-tuned models where general frontier models underperform on technical vocabulary or domain-specific output formats.
Orchestration: LangChain, LangGraph, and cloud provider AI services
LangGraph for stateful multi-step agents with cyclic workflows and human-in-the-loop checkpoints. AWS Bedrock for teams wanting managed access to multiple frontier models without managing API keys for each provider. Azure OpenAI for organisations with existing Microsoft Enterprise Agreements and data residency within Azure regions. Evaluation and monitoring via LangSmith, Langfuse, or Weights and Biases.
Industry verticals we cover
Generative AI use cases differ by industry, not in the underlying technology, but in what data is available, what accuracy level is required, and what compliance constraints govern deployment. We have assessed and built generative AI systems across the following verticals.
Where we have built generative AI systems
Healthcare
Clinical documentation assistance, patient triage and intake chatbots, prior authorisation automation (pulling clinical documentation from EHR records and drafting payer submissions), clinical decision support tools grounded via RAG in medical knowledge bases, and radiology report summarisation. Compliance requirements: HIPAA data handling for PHI in LLM prompts, FDA SaMD classification for clinical AI outputs, and human-review requirements for AI-assisted clinical decisions.
Financial services and fintech
Document extraction and processing for lending (bank statements, pay stubs, tax returns processed without manual keying), regulatory document summarisation, investment research synthesis, customer support automation for account queries, and contract review. Compliance requirements: GDPR and CCPA for personal financial data in LLM prompts, explainability documentation for AI-assisted credit decisions in regulated markets.
Legal and professional services
Contract clause extraction and comparison, legal research acceleration (RAG over case law and regulatory databases), matter cost prediction, privilege review acceleration, and client-facing Q&A tools grounded in firm knowledge bases. Key constraint: accuracy requirements for privilege determinations are higher than most LLMs can meet without human review, we scope use cases where AI accelerates review rather than replaces it.
Logistics and supply chain
Automated exception notifications (translating operational anomalies into plain-language alerts for ops managers), carrier communication summarisation, customs documentation drafting, RFQ response generation, and internal knowledge retrieval for complex operational queries. The highest-value use cases are ones where unstructured operational data (emails, PDFs, event logs) needs to be synthesised quickly.
SaaS products and enterprise software
AI features embedded in existing SaaS products: document analysis, search augmentation with semantic retrieval, automated report generation, AI-assisted onboarding flows, and in-product AI assistants grounded in the user's own data. Architecture decision that matters most: whether the AI feature is a thin wrapper on a frontier model API or a RAG system grounded in the user's proprietary data, the second is more defensible and harder to replicate.
Retail and e-commerce
Product description generation at scale, personalised email copy generation, customer support deflection for order status and policy queries, review summarisation and sentiment analysis, and AI-assisted merchandising decision support. Cost modelling at retail scale (millions of product SKUs, high-volume customer queries) is a critical part of feasibility assessment, per-token cost at volume determines whether the unit economics work.
Tell us what you are trying to build or evaluate.
Use case, current state, and the decision you need clarity on. We'll structure the right consulting engagement.
How it works
From scope to decision
Every consulting engagement follows the same structure. Deliverable and price are fixed before work starts.
- Step 0101
Discover and scope
We map the problem, the data, and the existing systems. You leave with a written scope document and a fixed-price quote covering the full engagement. No work starts without your sign-off.
- Step 0202
Assess and select
Use case evaluation against value, feasibility, risk, and effort. Model selection tested on a sample of your actual inputs, not a generic benchmark. A focused single-use-case assessment or a production readiness review wraps here.
- Step 0303
Design the architecture
RAG or agent architecture, the evaluation framework, and the cost model, specified before any production code gets written. This is the work that decides whether the system holds up past the demo.
- Step 0404
Deliver and hand off
Written recommendations, a prioritised build roadmap, and the evaluation framework handed to your team. If you want us to build it next, the same team carries it into production, scoped separately.
Proof
Case studies
AI OCR for gas station operations
Two-way POS sync, AI invoice scanning, and inventory rules for a multi-site US operator.
Mobile POS app case study: card, tap, QR, and link payments for UAE merchants
A mobile POS and merchant-acquiring platform for a UAE FinTech operator: four ways to take a payment on a phone, two processors behind one ledger, and KYC built for a regulated operator.
What generative AI consulting costs
Every engagement is fixed-price with a defined scope and deliverable, agreed before work starts. Where you land depends on scope, not negotiation:
- Use case assessment
- A focused evaluation of a single application against value, feasibility, risk, and effort.
- AI strategy engagement
- Multiple use cases, model selection, and RAG or agent architecture design with a build roadmap.
- Production readiness review
- A structured assessment of an AI system already in development, surfacing the specific issues to fix before launch.
What it costs
Fixed scope, one clear deliverable.
A written scope and a firm quote before any work starts. Use case assessment, model selection, RAG architecture, or a production readiness review, priced to what you need.
Start with the smallest engagement that answers your question, a use case assessment or a readiness review, then scale into a full strategy engagement once you know what's worth building.
Starting investment
Fixed price
Start with the smallest engagement that answers your question, then scale into a full strategy engagement.
No hourly billing
Once we scope the engagement, that price is locked in writing. No hourly billing, no surprise invoices if the work continues.
One team, start to finish
The team that scopes your generative AI problem designs the architecture and stays through production. No offshore handoff after the contract is signed.
Work with us
Tell us where the work is stuck.
Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common 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.
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 covers a single application. A broader generative AI strategy engagement covers multiple use cases, architecture design, model selection, and a build roadmap. For teams with an AI system already in development, a production readiness review assesses the system and surfaces the specific issues to address before launch.
Three stages. Before experimentation: lowest risk, opportunity assessment plus roadmap. During pilots: viability, business case, data, success metrics. After failure: corrective, not strategic; you are paying to repair avoidable decisions. The rule: hire before the cost of being wrong exceeds the cost of advice. The pilot everyone loved, until it met real traffic: that is the hire signal. Common failure causes are wrong problem, unclear objectives, weak data infrastructure. Technology is rarely the binding constraint.
Signals you are buying hype: pilot purgatory, demos that never scale; RAG hallucinating on siloed data; no measurement. Focus on which specific bottleneck you are solving. The anti-upsell ritual: a credible consultant recommends a deterministic workflow when the process does not require AI, and explains which alternatives were considered and what each layer costs. A good consultant will tell you which side of the line you are on, even when it costs them the project. Change the conversation from what can AI do to what should our company do with AI.
One sentence: knowing what to build, what to buy, and what to skip. Identify where AI creates measurable value, scope and build production systems, provide senior technical leadership part-time, or train your team. Choose by production track record, not brand or slide deck: how many engagements reached production, which business metrics moved, which governance framework was used. Ask for an accuracy benchmark from a past project: a serious firm sends a report with numbers; a weak one sends a demo video.
Four rituals. One: the complexity question. Can they tell you when AI adds unnecessary complexity? Two: the production question. How many engagements reached production, with which metrics? Three: the ownership question. Who owns the models, code, and IP when the engagement ends, and is full ownership in the contract? Four: the failure plan. What happens when the model breaks in production, and who owns the incident? A serious engagement covers use case assessment, model selection, RAG and knowledge design, agent architecture, production readiness, and governance.
Scope decides the price. A focused use case assessment covers a single application. A broader strategy engagement covers multiple use cases and architecture design. A production readiness review assesses an existing AI system. All engagements are fixed-price with a defined scope and deliverable.