LLM Adoption & Strategy Consulting Services

Move from AI experimentation to production-grade LLM solutions, with the governance, security, and integration rigor your enterprise requires.

Large language models are transforming how enterprises handle knowledge work, automate processes, and serve customers. The potential is real. So is the gap between an impressive demo and a production system your organization can actually rely on.

For regulated enterprises, “move fast and break things” isn’t an option. You need LLM deployments that meet security requirements, produce auditable results, integrate with existing systems, and operate under governance controls that satisfy your compliance stakeholders.

i3solutions delivers LLM consulting services that can help your enterprise IT leaders deploy LLMs with the architecture, governance, and operational rigor production systems demand. We provide vendor-agnostic strategy, hands-on implementation, and a structured path from pilot to scale, whether you’re evaluating Anthropic’s Claude, a single-vendor tool, Azure OpenAI, other commercial platforms, or open-source models.

What Enterprise LLM Adoption Actually Requires

The gap between an impressive LLM demo and a production system your organization can depend on is larger than most vendors acknowledge. Demos show what’s possible; production requires what’s sustainable, secure, and defensible.

Enterprise LLM deployment requires:

Data architecture that supports retrieval

Retrieval-Augmented Generation (RAG) is frequently the right approach for enterprise knowledge applications, but only if your content is properly indexed, access-controlled, and current. Most organizations discover their SharePoint libraries, file shares, and knowledge bases aren’t ready. Content is scattered, metadata is inconsistent, access controls don’t align to retrieval boundaries, and freshness varies wildly. RAG, which is built on poor data architecture, produces poor results.

Governance and guardrails before scale

Who can use which models? What data can be sent to which endpoints? How do you prevent prompt injection, data exfiltration, or unauthorized actions? How do you approve new use cases? Most organizations deploying LLMs have no governance framework; they’re making decisions ad hoc and accumulating risk they don’t fully understand.

Security and compliance alignment

For regulated industries, LLM deployments must meet the same audit, logging, and access control standards as any other system handling sensitive data. That means understanding data flows, implementing appropriate controls, and producing evidence that satisfies compliance stakeholders. Relying solely on “the vendor says it’s secure” isn’t sufficient, and engaging LLM security and governance consulting ensures your deployments meet rigorous compliance and risk requirements.

Evaluation and observability

How do you know if responses are accurate? How do you detect hallucinations? How do you measure quality over time and prove the system delivers value? Without evaluation frameworks and production observability, you’re operating on faith, which doesn’t satisfy leadership, auditors, or users who depend on reliable outputs.

Integration into real workflows

LLMs create value when they’re embedded in business processes, augmenting how people work, automating routine tasks, and improving decision quality. Standalone chatbots that users abandon after a week don’t justify the investment. Integration requires understanding workflows, designing appropriate human-in-the-loop controls, and building adoption into the rollout.

This isn’t primarily a technology selection problem. It’s an architecture, governance, and operating model problem. The platform capabilities exist; the challenge is deploying them responsibly in your specific context.

Who This Is For

This service is designed for:

• IT and digital leaders at mid-to-large enterprises exploring LLM adoption who need structured guidance beyond vendor sales pitches
• Organizations in regulated industries (defense, financial services, healthcare, government-adjacent) where security, governance, and auditability are non-negotiable requirements
• Teams that have run pilots or proofs-of-concept and discovered the gap between demo and production, and need help crossing it
• Enterprises with significant Microsoft investments (Microsoft 365, Azure, Power Platform, Dynamics 365) are looking to integrate LLM capabilities responsibly into their existing environment
• Leaders who want vendor-agnostic guidance to evaluate options (a single vendor tool, Azure OpenAI, other commercial platforms, open-source models) based on their requirements, not a vendor’s quota
• Organizations are concerned about shadow AI, where employees are already using consumer AI tools with company data, creating security and compliance exposure

This is not a fit if:

• You’re looking for a quick chatbot demo without production requirements. We focus on deployments that need to operate reliably under enterprise constraints.
• You want to deploy LLMs without governance controls or security review. We build responsible deployments; if governance is unwelcome, we’re not the right partner.
• You’re committed to a single vendor’s solution and don’t want an evaluation. We provide objective guidance. If you’ve already decided and just need implementation, be clear about that.
• You need consumer-grade AI without enterprise integration. Our expertise is enterprise deployment with governance, security, and integration requirements.

The Challenge: From Demo to Production

Every organization can access ChatGPT or spin up an enterprise AI add-on demo. The barrier to experimentation is gone. The barrier to production deployment remains significant, and most organizations underestimate it, which is why enterprise AI implementation services are often the difference between a compelling pilot and a system that can operate reliably at scale.

Where we see organizations get stuck:

Shadow AI is already happening

Your employees are using ChatGPT, Claude, and other consumer AI tools with company data right now. They’re pasting sensitive information into systems you don’t control, with data retention policies you haven’t reviewed, creating security and compliance exposure you can’t quantify. You’re not deciding whether to adopt AI, you’re deciding whether to govern it.

RAG implementations fail on data quality

Organizations invest in retrieval architecture only to discover their content isn’t ready. SharePoint sites have inconsistent metadata. Access controls don’t match retrieval boundaries, users retrieve content they shouldn’t see, or can’t retrieve content they should. Content is stale, duplicated, or poorly organized. The RAG system works technically, but produces unhelpful or inappropriate results.

Governance frameworks don’t exist

Who approves new use cases? What data classifications can interact with which models? How do you audit what happened, what queries were sent, what responses were returned, and what actions were taken? Most organizations are making governance decisions ad hoc, with no framework, no documentation, and no consistency.

Evaluation is an afterthought

Leadership asks, “Is it working?” and no one can answer with evidence. There’s no baseline, no accuracy measurement, no way to detect quality degradation, no framework for comparing approaches. Decisions about scaling or expanding are made on anecdote and enthusiasm rather than data.

Pilots don’t translate to production

A successful proof-of-concept doesn’t mean production readiness. The pilot ran in an isolated environment with synthetic data and enthusiastic early adopters. Production requires identity integration, secrets management, error handling, monitoring, support processes, user training, and adoption management. The gap is consistently underestimated.

Vendor promises exceed reality

Marketing materials describe capabilities that require significant configuration, systems integration, and governance to actually deliver. Organizations purchase licenses expecting turnkey solutions and discover they’ve bought platforms that require substantial implementation work.

The path forward requires structured strategy and implementation discipline, not more experimentation and not blind faith in vendor roadmaps.

Our LLM Adoption Services

We deliver hands-on LLM adoption consulting and strategy, implementation, and governance, not slide decks summarizing vendor documentation. Our work produces deployments that operate reliably in your environment under your constraints.

LLM Readiness and Strategy Assessment

Evaluate where you stand and build a realistic roadmap:

• Assess organizational AI readiness: data quality, architecture maturity, governance capability, and change readiness
• Identify shadow AI exposure and current risk posture
• Evaluate data sources for RAG suitability: content freshness, access control alignment, metadata quality, retrieval boundaries
• Analyze use case candidates and prioritize by business value, implementation feasibility, and risk profile
• Provide vendor-agnostic platform recommendations based on your requirements, constraints, and existing investments
• Deliver a prioritized roadmap with realistic resource requirements and timeline estimates

RAG Architecture and Implementation

• Design and build retrieval systems that actually work:
• Design retrieval architecture aligned to your content sources: SharePoint, file shares, databases, knowledge bases, and line-of-business systems
• Implement chunking, indexing, and metadata strategies optimized for retrieval quality
• Configure access control alignment so users retrieve only the content they’re authorized to see, critical for sensitive data
• Build evaluation frameworks to measure retrieval quality and response accuracy
• Integrate with your existing environment (Microsoft or otherwise) through appropriate APIs and connectors
• Establish content lifecycle management, so retrieval stays current as source content changes

LLM Governance and Security Framework

Build the controls that make enterprise deployment defensible:

• Define policies for data classification boundaries: what data can interact with which models, under what conditions
• Establish use case approval workflows: how new applications get reviewed, approved, and monitored
• Implement prompt and response logging with appropriate retention for audit and troubleshooting
• Configure guardrails against prompt injection, data exfiltration, and unauthorized tool or action execution
• Establish human-in-the-loop controls for high-risk actions and decisions
• Create a governance operating model with clear roles, review cadence, escalation paths, and exception handling
• Document policies and procedures that satisfy compliance stakeholders

Production Deployment and Integration

Move from pilot to production with operational rigor:

• Architect production environments with appropriate isolation, secrets management, and identity integration
• Implement observability: latency monitoring, cost tracking, quality metrics, error alerting, and usage analytics
• Integrate LLM capabilities into existing workflows and applications, not standalone chatbots that require training to leverage well
• Build adoption programs: user training, communication, feedback channels, and support processes
• Establish incident response procedures for hallucinations, failures, quality degradation, and security events
• Create operational runbooks for ongoing management and troubleshooting

Use Case Development and Evaluation

• Identify value and prove it delivers:
• Facilitate use case discovery and prioritization based on business impact and implementation feasibility
• Build evaluation harnesses with golden datasets: known-good question-answer pairs for accuracy measurement
• Define success metrics and acceptance criteria before deployment, not after
• Implement feedback loops and quality monitoring for continuous improvement

How We Work: From Strategy to Production

Phase 1: Discovery and Scoping (Weeks 1-2)

Understand your current state and define engagement scope:

• Review existing AI initiatives, pilots, and shadow AI exposure
• Assess data architecture, content sources, and retrieval readiness
• Understand security requirements, compliance constraints, and stakeholder concerns
• Identify high-priority use cases and business drivers
• Define engagement scope, success criteria, and timeline

Deliverable: Scoping document with current state summary, priority use cases, and engagement plan

Phase 2: Readiness Assessment (Weeks 2-4)

Systematic evaluation of organizational and technical readiness:

• Data quality and architecture assessment for priority use cases
• Governance maturity evaluation: policies, controls, operating procedures
• Platform evaluation against requirements (if platform selection is in scope)
• Risk assessment: security, compliance, operational, and adoption risks
• Use case prioritization with implementation feasibility analysis

Deliverable: Readiness assessment report with findings, recommendations, and prioritized use case roadmap

Phase 3: Strategy and Architecture (Weeks 4-6)

Design the approach before building:

• Architecture design: RAG implementation, integration patterns, security controls
• Governance framework design: policies, approval workflows, monitoring approach
• Platform and tooling recommendations (vendor-agnostic, based on your requirements)
• Implementation roadmap with phased approach, resource requirements, and milestones
• Risk mitigation planning and contingency approaches

Deliverable: Architecture documentation, governance framework, and implementation roadmap

Phase 4: Pilot Implementation (Weeks 6-12)

Build a production-quality pilot for your highest-priority use case:

• RAG implementation consulting services (if applicable) with proper data preparation and access controls
• Governance controls operational from the start
• An evaluation framework is in place to measure quality
• Integration with target workflows and user experience design
• User acceptance testing with representative users

Deliverable: Operational pilot with governance controls, evaluation metrics, and user feedback

Phase 5: Production Deployment (Weeks 12+)

Scale from pilot to production:

• Production environment deployment with full operational infrastructure
• User training and adoption program execution
• Monitoring and incident response operational
• Documentation and runbook handoff to internal teams
• Post-deployment review and optimization recommendations

Deliverable: Production deployment with operational handoff and adoption metrics

Phase 6: Ongoing Governance and Optimization

Continuous improvement after initial deployment:

• Model and content updates as capabilities and sources evolve
• Evaluation refinement based on production data
• Use case expansion based on proven patterns
• Governance review and policy updates
• Performance and cost optimization

Why i3solutions for LLM Adoption

Vendor-agnostic approach. We recommend solutions based on your requirements, not our vendor relationships or partnership incentives. a single vendor tool, Azure OpenAI, Anthropic, OpenAI direct, open-source models: we help you evaluate options objectively and avoid lock-in where it matters. If Microsoft is the right answer, we’ll tell you. If it isn’t, we’ll tell you that too.

• Enterprise integration expertise: We’ve delivered hundreds of Microsoft 365, SharePoint, Power Platform, Dynamics, and Azure projects for enterprise clients. We know how to integrate LLM capabilities into existing enterprise environments, identity, security, data, workflows, not bolt on disconnected tools that create new silos.
• Governance-first mindset: For regulated enterprises, governance isn’t optional and isn’t an afterthought. We build controls, audit trails, approval workflows, and operating models from the start. Your compliance stakeholders should be comfortable with the deployment, not surprised by it.
• Production focus: We optimize for working systems that users adopt and depend on, not impressive demos that get forgotten after the executive presentation. Evaluation, observability, adoption, and operational sustainability are built into every engagement.
• Senior-led delivery: Our consultants have direct experience with enterprise AI deployments in regulated environments. You work with practitioners who make decisions and solve problems, not junior staff learning on your project.
• US-based team: All work is performed by US-based personnel. For organizations with data sensitivity requirements or personnel security considerations, this matters.
• No AI hype: We won’t tell you AI will transform everything overnight or that you need to adopt immediately or be left behind. We provide a realistic assessment of what LLMs can and can’t do for your specific situation, and honest guidance about readiness, timeline, and investment required.

Security, Compliance, and Governance Considerations

Security, compliance, and governance are foundational requirements for any LLM deployed in an enterprise environment. These considerations must be embedded into the architecture and operating model from the outset to ensure systems are secure, auditable, and aligned with regulatory and organizational expectations:

• Data classification and boundaries: We help you define what data can interact with which LLM endpoints. Not all data should flow to all models. Classification boundaries, approved use cases, and technical controls should align.
• Access control and authorization: RAG implementations must respect access controls, and users should retrieve only content they’re authorized to see. We design and validate access control alignment as a core requirement, not an afterthought.
• Audit and logging: Enterprise deployments need audit trails: what queries were submitted, what responses were returned, what actions were taken. We implement appropriate logging with retention policies that satisfy compliance requirements.
• Prompt injection and security controls: LLM applications face specific security risks, including prompt injection, data exfiltration through crafted queries, and unauthorized action execution. We implement guardrails appropriate to your risk profile.
• Vendor and data residency: Different platforms have different data handling practices, residency options, and compliance certifications. We help you evaluate options against your requirements, FedRAMP, HIPAA, SOC 2, data residency, or other constraints.
• Human oversight: Not all LLM outputs should proceed without review. We help you design appropriate human-in-the-loop controls for high-risk decisions, sensitive outputs, or actions with significant consequences.
• Ongoing governance: AI governance isn’t a one-time configuration. We help you establish operating models with clear ownership, review cadences, policy update processes, and escalation paths for issues that arise.

Engagement Options

LLM Readiness Assessment

Timeframe: 2-3 weeks

What you get:

• Current-state assessment: shadow AI exposure, data readiness, governance maturity
• Use case analysis and prioritization
• Platform evaluation against your requirements (vendor-agnostic)
• Readiness roadmap with recommendations and a realistic timeline
• Executive summary for stakeholder communication

Best for: Organizations exploring LLM adoption who need clarity on where they stand, what’s feasible, and how to proceed responsibly.

Schedule an LLM Readiness Assessment

RAG Implementation Sprint

Timeframe: 6-10 weeks

What you get:

• RAG architecture design for your priority use case
• Data preparation and indexing implementation
• Access control alignment and validation
• Evaluation framework with accuracy measurement
• Governance controls and documentation
• Pilot deployment ready for user testing

Best for: Organizations with a defined use case and data sources ready to implement retrieval-augmented generation with production-quality architecture.

Plan a RAG Implementation Sprint

LLM Governance Framework

Timeframe: 3-4 weeks

What you get:

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

Best for: Organizations deploying LLMs (or discovering shadow AI) who need a governance structure before scaling.

Establish LLM Governance

Production LLM Deployment

Timeframe: 10-16 weeks

What you get:

• End-to-end deployment from strategy through production
• RAG implementation (if applicable)
• Governance framework operational
• Production infrastructure and observability
• User adoption program
• Operational handoff with documentation and training

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

Plan a Production LLM Deployment

Ongoing LLM Advisory

Timeframe: Monthly retainer

What you get:

• Continuous governance oversight and policy refinement
• Use case expansion guidance and prioritization
• Evaluation, monitoring, and quality assurance
• Vendor and technology guidance as capabilities evolve
• Incident support and troubleshooting

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

Discuss Ongoing LLM Advisory