Hire Enterprise LLM Implementation Consultants

Senior consultants who design, implement, and operationalize LLM solutions in enterprise environments – from RAG through production deployment. This is an implementation-focused engagement – not AI strategy decks, vendor demos, or experimental pilots.

Your organization is exploring large language models. Maybe you’ve run pilots that showed promise. Maybe shadow AI is already creating the exposure you need to address. Maybe leadership is asking when AI will deliver measurable value instead of interesting demos.

You need to hire LLM consultants who understand enterprise constraints, security requirements, compliance obligations, integration complexity, and governance expectations. You need practitioners who can design RAG architectures that actually work, implement controls that satisfy your compliance stakeholders, and deploy solutions users will adopt.

i3solutions provides senior LLM consultants with direct experience deploying AI in regulated enterprise environments. We work across platforms, Microsoft, Azure, OpenAI, other commercial options, and open-source models, and focus on production readiness, not just proof-of-concept excitement.

When to Hire LLM Consultants

Signals that indicate you need external expertise:

• Your pilots haven’t translated to production: You’ve demonstrated LLM capabilities in isolated environments. Leadership is impressed. But moving to production, with proper security, governance, integration, and support, requires expertise your team doesn’t have. The pilot is stuck.
• Shadow AI is creating unquantified risk: Employees are using ChatGPT, Claude, and other consumer tools with company data. You don’t know what data is being exposed, what retention policies apply, or what compliance implications exist. You need to get ahead of this with governed alternatives.
• Your IT team lacks specialized AI/ML expertise: Your team builds and maintains enterprise systems; they’re not machine learning engineers or AI architects. The learning curve for production LLM deployment is steep, and you can’t afford months of experimentation.
• RAG implementation isn’t delivering: You’ve built retrieval-augmented generation, but results are inconsistent. Users complain about irrelevant responses, hallucinations, or missing information. Your data architecture, chunking strategy, or access controls need expert attention from an LLM consulting team for RAG implementation.
• Governance requirements are blocking deployment: Your compliance, legal, or security stakeholders have concerns. You need governance frameworks, audit capabilities, and documented controls before they’ll approve production deployment.
• Vendor promises haven’t materialized: You purchased an enterprise AI add-on license or another platform, expecting capabilities that require significant implementation to deliver. You need help closing the gap between marketing materials and production reality.
• You need to show ROI, not just potential: Leadership wants evidence that AI investments deliver measurable value. You need evaluation frameworks, metrics, and production results, not more demos.
• Multiple initiatives are fragmenting effort: Different teams are pursuing different AI experiments with different tools and no coordination. You need a coherent strategy and consolidated approach.

Who This Is For

Engage i3solutions if you are:

• An IT or digital leader responsible for AI adoption who needs enterprise-grade implementation expertise
• In a regulated industry (defense, financial services, healthcare, government-adjacent) where security and governance are requirements, not suggestions
• Running pilots that need to become production systems with proper controls
• Facing shadow AI exposure that needs governed alternatives
• Looking to implement RAG or other LLM patterns with production-quality architecture
• Seeking vendor-agnostic guidance, evaluation, and recommendations based on your requirements, not vendor relationships
• Ready to invest in doing AI properly rather than accumulating technical debt and risk
• This engagement is not the right fit if:
• You want a quick demo without production requirements. Our focus is enterprise deployment with governance, security, and operational sustainability.
• You’re not willing to implement governance controls. We build responsible AI deployments; if oversight is unwelcome, we’re not aligned.
• You’ve already committed to a vendor and just need basic configuration. We can help, but our value is in architecture, governance, and integration, not license activation.
• You’re primarily seeking the lowest-cost option. Our enterprise LLM consulting team is senior and US-based. We compete on expertise and production results, not rate minimization.

The Problem: Why Enterprise LLM Projects Stall

Most organizations have AI initiatives underway. Few have production deployments delivering measurable value. The pattern is consistent: experiments proliferate, but production deployment remains elusive.

Where we see organizations get stuck

Technology selection paralysis. The vendor landscape is overwhelming: Microsoft 365, Azure OpenAI, Anthropic, OpenAI direct, and open-source options. Each has different capabilities, pricing models, data handling practices, and integration patterns. Teams evaluate endlessly without deciding, or commit prematurely without understanding implications.

• Data architecture isn’t ready: RAG promises to ground LLM responses in your content. But your content lives across SharePoint sites, file shares, databases, and applications with inconsistent metadata, outdated material, and access controls that don’t align to retrieval boundaries. The RAG system retrieves irrelevant content or surfaces material users shouldn’t see.
• Governance doesn’t exist: Your compliance team asks: What data flows to external APIs? How are prompts and responses logged? Who approves new use cases? What happens when something goes wrong? The answers are “we’re not sure” or “we haven’t decided.” Deployment stalls.
• Security concerns block progress: Information security raises legitimate questions about data exfiltration risk, prompt injection vulnerabilities, and audit requirements. Without clear architecture and controls, they can’t approve deployment. Projects wait in review indefinitely.
• The pilot gap: Pilots run in sandbox environments with synthetic data and enthusiastic volunteers. Production requires identity integration, secrets management, error handling, monitoring, support processes, and skeptical users. Teams underestimate this gap repeatedly.
• No evaluation framework: Leadership asks, “Is it working?” and teams can’t answer with evidence. There’s no baseline, no accuracy measurement, no quality monitoring. Decisions about scaling or killing projects are made on opinion, not data.
• Fragmented initiatives: Multiple teams experiment with different tools for different purposes. There’s no common architecture, no shared governance, no coordinated strategy. Each experiment creates its own technical debt and risk exposure.

You need AI implementation consultants who’ve navigated these patterns across multiple organizations, who can provide architecture, governance, and implementation expertise while avoiding the pitfalls that stall most enterprise AI initiatives.

What You Get When You Engage i3solutions

Senior LLM and AI Consultants

Not generalists learning AI on your project. Our consultants have direct experience with enterprise LLM deployments, RAG architecture, governance frameworks, security controls, and production operations. They’ve seen what works and what fails across multiple regulated enterprise environments.

Vendor-Agnostic Expertise

We work across platforms: Azure OpenAI, Anthropic, OpenAI direct, open-source models like Llama and Mistral. We recommend, based on your requirements, use case fit, cost structure, data handling, integration needs, not our partnership agreements. If Microsoft is right, we’ll tell you. If it isn’t, we’ll tell you that too.

RAG Architecture That Works

Retrieval-augmented generation done properly: data preparation, chunking strategy, embedding selection, retrieval optimization, and access control alignment. We build RAG systems that return relevant, accurate results from your content while respecting authorization boundaries.

Governance and Security Frameworks

Data classification policies. Use case approval workflows. Prompt and response logging. Guardrails against prompt injection and data exfiltration. Human-in-the-loop controls. Operating models with clear ownership and escalation paths. These are the controls your compliance stakeholders require before they’ll approve deployment, designed and implemented by enterprise AI consultants for governance and security.

Production Deployment Capability

Environment architecture, identity integration, secrets management, monitoring and observability, incident response, operational runbooks. We deploy LLM solutions that operate reliably in production, not demos that break when real users and real data are involved.

Evaluation and Measurement

Golden datasets, accuracy measurement, retrieval quality scoring, hallucination detection, and regression testing. We build evaluation frameworks that prove your LLM system works and alert you when quality degrades.

Enterprise Integration Experience

We’ve delivered hundreds of Microsoft 365, SharePoint, Power Platform, Dynamics 365, and Azure projects. We know how to integrate LLM capabilities into existing enterprise environments, identity, security, data, and workflows, without creating new silos or governance gaps.

Engagement Models

LLM Readiness Assessment

Duration: 2-3 weeks

What we deliver:

• Shadow AI and current state assessment
• Data readiness evaluation for priority use cases
• Governance maturity analysis
• Platform evaluation and recommendations (vendor-agnostic)
• Prioritized roadmap with realistic timeline

What you provide:

• Access to relevant systems and documentation
• Stakeholder availability for interviews
• Clarity on business priorities and constraints

Best for: Organizations exploring LLM adoption who need a structured assessment before committing to implementation.

RAG Implementation Sprint

Duration: 6-10 weeks

What we deliver:

• RAG architecture design and implementation
• Data preparation and indexing
• Access control alignment and validation
• Evaluation framework with accuracy measurement
• Governance controls and documentation
• Pilot deployment ready for production

What you provide:

• Access to content sources and the target environment
• Stakeholder engagement for requirements and testing
• Decision-making authority for architecture choices

Best for: Organizations with defined use cases ready to implement production-quality retrieval-augmented generation.

LLM Governance Framework

Duration: 3-4 weeks

What we deliver:

• Data classification and boundary policies
• Use case approval workflow design
• Logging, monitoring, and audit architecture
• Security controls and guardrail specifications
• Operating model with roles and responsibilities
• Documentation for compliance stakeholders

What you provide:

• Engagement from compliance, legal, and security stakeholders
• Clarity on regulatory and policy requirements
• Decision authority for governance policies

Best for: Organizations deploying LLMs (or addressing shadow AI) who need a governance structure before or alongside implementation.

Production LLM Deployment

Duration: 10-16 weeks

What we deliver:

• End-to-end deployment: strategy through production
• RAG or other architecture implementation
• Governance framework operational
• Production infrastructure and observability
• User adoption program execution
• Operational handoff with documentation

What you provide:

• Executive sponsorship and decision authority
• Cross-functional stakeholder engagement
• Production environment access
• User population for rollout

Best for: Organizations ready to move from experimentation to production with comprehensive implementation support.

Embedded LLM Team / Staff Augmentation

Duration: Multi-month engagement

What we deliver:

• Dedicated LLM consultant(s) integrated with your team
• Flexible allocation across assessment, implementation, and governance
• Knowledge transfer to internal staff
• Ongoing advisory and problem-solving

What you provide:

• Team integration and communication access
• Clear objectives and workstream ownership
• Ongoing stakeholder engagement

Best for: Organizations with extended AI programs needing sustained expertise, or those building internal AI capability alongside external support.

Ongoing LLM Advisory

Duration: Monthly retainer

What we deliver:

• Continuous governance oversight and refinement
• Use case expansion guidance
• Evaluation, monitoring, and quality assurance
• Vendor and technology advisory as the landscape evolves
• Incident support and troubleshooting

What you provide: – Regular touchpoints and stakeholder access – Visibility into new initiatives and challenges – Engagement on strategic decisions

Best for: Organizations with production LLM deployments that need ongoing expertise for optimization, expansion, and governance maintenance.

Skills and Roles We Bring

We can deliver LLM initiatives that move beyond experimentation and into dependable production when we combine our deep technical capability with real-world delivery experience. Our team covers the full spectrum required to design, build, secure, and operate enterprise-grade AI systems. This will bring the following skills and roles to every engagement:

• LLM architecture and implementation: RAG design, embedding strategies, chunking approaches, retrieval optimization, prompt engineering, response quality tuning. We build LLM solutions that work reliably with your data and use cases.
• AI/ML engineering: Model selection and evaluation, inference optimization, cost management, performance tuning. Technical depth beyond configuration, actual engineering for production systems.
• Enterprise security and governance: Data classification, access control design, audit logging, compliance documentation, risk assessment. Controls that satisfy security teams and compliance stakeholders.
• Microsoft and cloud platforms: Deep experience with Azure OpenAI, Microsoft 365, SharePoint, Power Platform, and Azure infrastructure. Plus familiarity with AWS, GCP, and platform-agnostic deployment patterns.
• Integration and data architecture: Connecting LLM capabilities to existing systems, workflows, and data sources. APIs, connectors, data pipelines, and identity integration.
• Evaluation and observability: Building measurement frameworks, golden datasets, quality metrics, monitoring dashboards, and alerting. Proving that systems work and detecting when they don’t.
• Project delivery: Structured implementation with clear milestones, stakeholder communication, risk management, and quality gates. AI projects delivered predictably, not chaotically.

Typical engagement team composition:

• Lead AI/LLM consultant: architecture, strategy, governance design
• LLM implementation specialist: RAG development, integration, evaluation
• Security and governance specialist: controls, policies, compliance documentation
• Project coordination: timeline management, stakeholder communication

Team composition scales based on engagement scope, timeline, and complexity.

How We Work

Initial Consultation

We discuss your AI initiatives, challenges, and objectives. What have you tried? What’s working? What’s stuck? What does success look like? No commitment required, a focused conversation to understand your situation and determine fit.

Scoping and Proposal

Based on your needs, we propose a specific engagement: assessment, implementation sprint, governance framework, production deployment, or embedded team. Clear deliverables, timeline, and investment. No ambiguity about what you’re getting.

Kickoff and Discovery

We establish access, align with stakeholders, and confirm scope. For technical work, we review existing architecture, data sources, and constraints. Discovery is structured, with specific questions, specific reviews, and documented findings.

Execution

• For assessment engagements: Systematic evaluation, stakeholder interviews, technical review, documented findings, and recommendations.
• For implementation engagements: Architecture design, development, testing, and iteration. Regular demos and progress updates. Adjustments based on what we learn.
• For governance engagements: Policy development, workflow design, control specification, and documentation. Stakeholder review cycles.

Validation and Handoff

Deliverables are reviewed and validated. For technical implementations, we test in production conditions. For governance frameworks, we confirm stakeholder acceptance. Documentation is complete. Your team is trained and ready to operate.

Quality Gates

• Scoping sign-off before work begins
• Architecture review before implementation
• Testing and validation before deployment
• Stakeholder acceptance before handoff
• Post-deployment review for production engagements

We don’t skip gates. Problems surface early, when they’re correctable.

How We Reduce Delivery Risk

Successful LLM initiatives fail less from a lack of ambition and more from unmanaged risk. To keep delivery predictable and outcomes aligned with business expectations, we embed risk reduction into how we work from day one through to deployment. This is how we consistently minimise delivery risk across our engagements:

• Structured methodology: LLM projects can sprawl without discipline. We follow consistent patterns for assessment, architecture, implementation, and governance, refined across multiple engagements. Work stays focused on defined outcomes.
• Senior practitioners: Experienced consultants make decisions and solve problems directly. You’re not waiting for escalations or managing junior staff. The people designing solutions are the people implementing them.
• Evaluation from the start: We establish success criteria and measurement frameworks early, not after deployment when it’s too late to adjust. You’ll know whether the solution works because we’ll measure it.
• Governance built in: Controls, logging, and documentation aren’t afterthoughts. We build governance alongside implementation, so deployment isn’t blocked by security review.
• Incremental delivery: We deliver in phases with validation points. You see progress, provide feedback, and course-correct before significant investment is at risk.
• Vendor neutrality: We recommend platforms based on your requirements, not our incentives. You get objective guidance, not a sales pitch for whatever we’re partnered with.
• Transparency: Regular status updates. Clear milestone tracking. Risks identified when they emerge, not hidden until they’re crises. You know where the engagement stands.

Security, Compliance, and IP Considerations

LLM initiatives often involve sensitive data, regulated environments, and valuable intellectual property. We take an enterprise-first approach to security, compliance, and trust, ensuring that your data protection, confidentiality, and ownership are addressed from the outset and remain integral throughout the engagement:

• Data handling: We work with your data in your environment according to your policies. We don’t extract your content, prompts, or responses to our systems. Implementation artifacts are your property.
• Personnel: Our LLM consultants are US-based. We can accommodate specific personnel security requirements where contractually necessary.
• Confidentiality: Your AI strategy, use cases, architecture, and implementation details are confidential. We don’t share customer-specific information across engagements. Standard confidentiality terms apply; we accommodate customer-specific requirements.
• Access controls: We work with appropriate access for the engagement scope. Assessment may require read-only access; implementation requires administrative access to target systems. Access is scoped and revoked at engagement end.
• Work product ownership: Code, documentation, policies, and other artifacts we create are yours. Full ownership transfers to you.
• Vendor relationships: We have partnerships with Microsoft and other vendors. Those relationships don’t influence our recommendations. We recommend what’s right for your situation, even when that’s not a partner product.

Why Choose i3solutions as Your LLM Partner

Selecting the right LLM partner determines whether your initiative becomes a durable capability or another stalled experiment. i3solutions is built for enterprises that need practical outcomes, clear accountability, and solutions that stand up to real-world constraints. Here are some of the best reasons why your organization should choose us:

• We’ve done this before. Our team has deployed LLM solutions in regulated enterprise environments, RAG implementations, governance frameworks, and production systems. We bring pattern recognition from multiple engagements, not first-time experimentation.
• Vendor-agnostic by design. We evaluate LLM platforms, hosting models, and architectures against your data, security, and governance requirements — not partner incentives or preferred tools. Microsoft, Azure OpenAI, Salesforce, Okta, other commercial options, and open-source models, we evaluate objectively and recommend honestly.
• We focus on production, not demos. Impressive demos are easy. Production systems that work reliably under enterprise constraints are hard. We optimize for the hard part while integrating with your existing platforms like Dataverse, Microsoft Teams, and Power Apps.
• We build governance in. For regulated enterprises, governance isn’t optional. We design controls, documentation, and operating models from the start; your compliance stakeholders should be comfortable with the deployment.
• We’re senior and US-based. Experienced consultants who make decisions and solve problems. All work is performed by US-based personnel.
• We don’t overpromise. We won’t tell you AI will transform everything or that you’re falling behind competitors. We provide a realistic assessment of what’s achievable in your specific situation with honest guidance about investment, timeline, and integration with enterprise systems.