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Enterprise Generative AI Advisory: Where the Hype Ends and the Value Starts

Every enterprise board is asking about Generative AI. Every vendor is claiming their solution solves everything. Most GenAI demonstrations are impressive. Most GenAI production deployments are disappointing. We help enterprises identify the use cases where generative AI creates genuine business value and build the governance and architecture to deploy it responsibly at scale.

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LLM deployments across 14 industries Zero vendor referral relationships Production RAG systems at enterprise scale Responsible AI frameworks built in
The Problem

Why Most Enterprise GenAI Initiatives Are Stuck at Pilot Stage Two Years In

Generative AI is genuinely transformative in the right contexts. It is also genuinely unsuitable in a large number of contexts where organisations are currently trying to apply it. The problem is not the technology. It is that vendor demonstrations are designed to show the most impressive capabilities in the most favourable conditions, and most enterprise buyers lack the independent technical perspective to distinguish impressive demonstrations from production-ready capabilities.

The second problem is governance. Generative AI introduces quality risks that traditional AI does not have. A supervised classification model produces the same output for the same input. A generative model produces different outputs, sometimes including factually incorrect content, sensitive disclosures, or responses that create legal and regulatory exposure. Organisations that deploy generative AI without a governance framework built for its specific characteristics create risk that surfaces unpredictably.

  • LLM pilots that perform well in testing because the test data was selected to show the model at its best
  • RAG architectures designed for demo performance that fail at production document volumes
  • Governance frameworks borrowed from traditional AI that do not address hallucination and output quality risk
  • LLM selection decisions made based on benchmark scores rather than performance on your specific use case
  • Data privacy exposure from sending sensitive business information to third-party LLM APIs
  • Prompt engineering approaches that are brittle, undocumented, and impossible to maintain at scale
78%
of enterprise GenAI pilots never reach production deployment
3x
higher average production failure rate for GenAI versus traditional ML deployments
60%
of GenAI production incidents trace to governance gaps rather than technical failures
82%
of successful enterprise GenAI deployments used independent advisory during architecture design
What We Do

Six Components of Enterprise Generative AI Advisory

From use case identification through to production governance. Every component designed to close the gap between GenAI pilot enthusiasm and production delivery.

GenAI Use Case Identification and Prioritisation
Structured identification of GenAI use cases across your business with a rigorous feasibility assessment for each. We apply a three-criteria test: does the problem genuinely require generative capability, is the required data available and suitable, and is the organisation ready to manage the quality and risk implications? This process eliminates use cases that look good in vendor presentations but will not deliver value in your environment.
LLM Selection and Evaluation
Independent evaluation of foundation model options for your specific use case. We assess proprietary APIs, open source models, and fine-tuning options against your requirements across accuracy, latency, cost, data privacy, regulatory compliance, and enterprise support standards. We carry no commercial relationships with any LLM provider. Our recommendation reflects your requirements, not referral economics or the latest benchmark leaderboard.
RAG Architecture Design and Implementation Oversight
Retrieval Augmented Generation architecture that is designed for production performance from the outset. We design the embedding strategy, vector database architecture, retrieval logic, context management, and quality evaluation framework. Our RAG designs have processed document corpora from 50,000 to over 40 million documents in production. We know the architectural decisions that determine whether RAG performs at scale.
Prompt Engineering Governance and Standards
A systematic approach to prompt development, testing, versioning, and maintenance that turns an ad-hoc activity into an engineering discipline. We design prompt libraries, testing frameworks, performance benchmarks, and change management processes for prompts in production. This prevents the common failure mode where critical business processes depend on prompts that are undocumented, untested, and that no-one fully understands.
GenAI Governance and Risk Framework
A governance framework built specifically for generative AI's distinct risk profile. Covers hallucination detection and mitigation, output quality monitoring, human review workflow design, sensitive content filtering, data privacy in context windows, model version management, incident response, and regulatory compliance in relevant jurisdictions. Designed to enable deployment at pace, not to prevent it.
Fine-tuning Strategy and Custom Model Development
Assessment of when fine-tuning is genuinely the right approach versus when RAG, prompt engineering, or a different base model selection achieves better results at lower cost and complexity. Where fine-tuning is warranted, we design the data curation strategy, training approach, evaluation methodology, and ongoing model update programme. We have supervised fine-tuning projects producing domain-specific models used in legal, financial, and clinical contexts.
Where GenAI Works

Enterprise Use Cases Where Generative AI Delivers Production Value

Use cases that have reached production at scale across our client base. Each has a documented ROI case and a governance framework that satisfies legal and compliance review.

Financial Services
Contract Intelligence and Review
RAG-powered contract analysis that identifies risk clauses, obligations, and non-standard terms across large document corpora. Deployed in legal, procurement, and M&A contexts. Typical outcome: 70% reduction in analyst time on initial contract review with documented accuracy rate above 94%.
Healthcare
Clinical Documentation Assistance
LLM-assisted clinical documentation that reduces physician administrative burden while maintaining accuracy and compliance with documentation standards. Deployed with a human review layer for all clinical outputs. Typical outcome: 45 minutes per physician per day recovered for patient care.
Professional Services
Knowledge Base and Internal Search
Conversational access to institutional knowledge across policies, procedures, past work, and internal expertise. Replaces keyword search with intent-based retrieval. Typical outcome: 60% reduction in time spent searching for internal information, measurable knowledge reuse improvement across teams.
Manufacturing
Technical Documentation and Maintenance Support
Technician-facing GenAI that surfaces relevant procedures, part information, and fault resolution guidance from complex technical documentation libraries. Reduces the expertise gap between senior and junior technicians. Typical outcome: 28% reduction in mean time to repair on complex equipment failures.
Insurance
Underwriting Intelligence and Policy Analysis
LLM-assisted underwriting that surfaces relevant precedents, risk factors, and policy terms for complex commercial cases. Deployed with human decision authority maintained throughout. Typical outcome: 40% reduction in underwriter research time with improved consistency of risk assessment across the book.
All Industries
Regulatory and Compliance Intelligence
Regulatory change monitoring, compliance gap identification, and regulatory Q&A across complex and evolving regulatory environments. Particularly valuable in financial services, pharmaceuticals, and energy sectors. Typical outcome: 50% reduction in regulatory team time on monitoring and initial impact assessment.
Our Methodology

How We Take GenAI from Business Case to Production

A four-phase process that validates feasibility before building, governs risk before deployment, and transfers capability before exit.

01
Use Case Validation (Weeks 1 to 2)
Three-criteria feasibility assessment for each candidate use case. Data audit to confirm availability and suitability. Risk and governance assessment to identify blockers early. LLM landscape scan to confirm viable technology options exist. Output: validated use case shortlist with feasibility evidence, risk assessment, and recommended architecture approach for each.
02
Architecture and Governance Design (Weeks 2 to 5)
LLM selection with documented evaluation rationale. RAG architecture design and review (where applicable). Prompt engineering framework and governance design. Data privacy and security architecture. Governance framework design covering output quality, human review workflows, and incident response. Output: production architecture specification, governance framework, evaluation criteria for PoC phase.
03
Build and Validation (Weeks 5 to 12)
Build oversight with independent technical review at each milestone. Evaluation framework testing using real business scenarios, not curated demonstrations. Prompt engineering development and testing. Quality and safety testing against governance framework. User testing with representative users. Output: production-ready GenAI system meeting quality and governance standards.
04
Production and Capability Transfer (Weeks 12 to 16)
Phased production launch with hypercare monitoring. Output quality monitoring framework operational. Human review workflow validated in production conditions. Internal team trained on prompt management, quality monitoring, and incident response. Capability transfer review confirming internal team can maintain and develop the system independently. Output: live production system with self-sufficient internal capability.
Client Results

GenAI Deployments That Delivered Measurable Value

All Case Studies →
Legal and professional services
Top 5 Global Law Firm
Contract Intelligence Platform: 2.8M Documents, 94% Accuracy, 14 Weeks to Production
A global law firm needed a contract intelligence platform capable of identifying risk clauses and unusual terms across a 2.8 million document corpus covering 18 document types in six languages. We designed a RAG architecture that achieved 94% accuracy on the firm's internal validation test set and reached production in 14 weeks. The platform recovered an average of 4.2 hours per week per associate on initial contract review.
4.2 hrsWeekly time recovered per associate
94%Accuracy on internal validation set
Financial services operations
Top 20 Global Bank
Regulatory Intelligence Platform That Processes 40,000 Regulatory Updates Annually
A global bank needed to monitor regulatory change across 47 jurisdictions and assess the impact of changes on their product set and compliance obligations. Manual monitoring was no longer viable at this scale. We deployed a GenAI regulatory intelligence platform that processes 40,000 regulatory updates annually, triages by relevance and potential impact, and generates structured impact assessments with human review triggers for material changes.
40,000Regulatory updates processed annually
62%Reduction in compliance team monitoring time
Free Research
Enterprise Generative AI: The Practitioner's Implementation Guide
38-page guide covering use case selection, LLM evaluation, RAG architecture design, governance frameworks, and responsible deployment. Written by practitioners who have deployed GenAI systems in production across financial services, healthcare, and professional services.
Download Free Guide →
Common Questions

Generative AI Questions We Hear Every Week

How do you help enterprises cut through the GenAI hype?
We assess every proposed GenAI use case against three criteria: does the business problem genuinely require generative capability, is the required data available and suitable, and is the organisation ready to manage the quality and risk implications? Many use cases that appear impressive in vendor demonstrations fail one or more of these criteria. We are direct about this assessment even when it contradicts what the organisation wants to hear. Our job is to get you to production, not to validate enthusiasm.
How do you select the right LLM for our use case?
LLM selection depends on your specific use case requirements, data sensitivity, performance needs, cost constraints, and regulatory environment. We evaluate foundation model providers, open source models, and fine-tuning options without commercial relationships with any provider. Key evaluation dimensions include accuracy on your specific task type, latency characteristics, context window size, data privacy and residency requirements, total cost at production volume, and the provider's enterprise support and SLA standards.
What is RAG and when is it the right architecture?
Retrieval Augmented Generation connects a language model to your proprietary information so it can generate accurate, grounded responses from your own documents, databases, and knowledge bases. RAG is the right architecture when you need a language model to work with proprietary or frequently updated information that cannot be included in model training. It is often more cost effective and maintainable than fine-tuning for knowledge-intensive use cases. We design RAG architectures that are production-grade from the outset, not prototypes that collapse at document scale.
How do you address GenAI security and data privacy risks?
GenAI security and data privacy require specific attention beyond standard AI risk management. Key concerns include prompt injection attacks, sensitive data in model context windows, training data privacy where fine-tuning is used, third-party API data handling, and output filtering for sensitive or regulated content. We design security frameworks addressing these risks before deployment. For organisations with strict data residency requirements, we evaluate and recommend architectures that avoid sending sensitive data to external APIs entirely.
What governance frameworks do you recommend for Generative AI?
GenAI governance needs to address hallucination risk management, output quality monitoring, human review workflows for high-stakes outputs, prompt library management, model version control, user access and permissions, incident response procedures, and regulatory compliance particularly in financial services, healthcare, and legal contexts. We build governance frameworks that enable deployment at pace rather than treating governance as a deployment blocker. Governance should be designed alongside the system, not added afterwards.
When does fine-tuning make sense versus RAG?
Fine-tuning is the right choice when you need a model to behave differently in terms of tone, format, or domain-specific reasoning patterns, and when the required adaptation cannot be achieved through prompt engineering or retrieval. RAG is the right choice when you need a model to have access to specific, frequently updated, or proprietary information. In many cases, a combination of both approaches delivers the best results. We assess this decision based on your specific requirements, data conditions, and total cost of ownership at production volume.
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GenAI in the Context of Your Broader AI Programme

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"Three generative AI pilots in 90 days. One became a production system that cut our contract review time by 60%. The advisory practice knows the difference between demos and deployments."

— General Counsel, Global Professional Services Firm

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  • Production GenAI deployments across 14 industries
  • Independent LLM evaluation, zero vendor relationships
  • RAG architecture at enterprise scale
  • Governance frameworks that enable deployment, not block it
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38 pages covering use case selection, LLM evaluation frameworks, RAG architecture patterns, governance standards, and responsible deployment checklists. Written by practitioners with production GenAI deployments at enterprise scale.

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