Embedded LLM Specialists
Hire US-Based Senior LLM Implementation Consultants and AI Specialists
Enterprise organizations are moving quickly to explore large language models, but most initiatives stall between experimentation and enterprise reality. Security exposure, unreliable data pipelines, unclear governance, and brittle architectures prevent Large Language Model (LLM) pilots from becoming dependable operational capabilities. Without experienced practitioners, AI efforts often create risk faster than they create value.
i3solutions provides US-based senior LLM specialists who embed directly into enterprise teams to design, implement, and operationalize production-grade AI solutions across Microsoft environments, OpenAI, other commercial options, and open-source models. Our developers work across retrieval-augmented generation (RAG), enterprise data integration, secure AI architectures, and platform modernization to ensure LLM initiatives are built for scale, control, and long-term ownership.
Whether supporting copilots, modernizing knowledge platforms, or establishing an enterprise AI foundation, our specialists bring hands-on implementation depth and architectural discipline to help organizations convert AI ambition into governed, defensible systems.
Access Enterprise LLM Specialists Built for Production Environments
Enterprise AI requires speed without sacrificing security, governance, or architectural control. Access US-based LLM specialists who embed into active initiatives, resolve technical risk, and help establish AI capabilities that operate reliably inside enterprise platforms.
When to Hire LLM Consultants
Enterprise AI initiatives rarely fail from a lack of ideas. They stall because the path from pilot to production exposes gaps in architecture, governance, security, and operational ownership. When that happens, internal teams need specialized LLM adoption and strategy services to move from experimentation to dependable, defensible enterprise systems.
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
This offering is designed for enterprise IT and digital leaders who are accountable for turning AI interest into governed, production-grade capability. If you’re operating in a regulated environment, facing growing shadow-AI risk, or under pressure to convert pilots into secure, measurable business systems, the following criteria will help you quickly assess fit.
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.
What You Get When You Engage i3solutions
Engaging i3solutions gives you access to senior, enterprise-experienced LLM consultants who focus on production systems, not experiments. Our work spans architecture, governance, security, evaluation, and integration, everything required to deploy LLM solutions that are defensible, scalable, and operationally reliable.
Senior LLM and AI Consultants
We are 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 means data preparation, chunking strategy, embedding selection, retrieval optimization, and access control alignment.
Governance and Security Frameworks
Every production deployment needs governance that satisfies compliance stakeholders: data classification policies, use case approval workflows, prompt and response logging, guardrails against prompt injection and data exfiltration, human-in-the-loop controls, and operating models with clear ownership and escalation paths. We design and implement these controls so your teams can move to deployment with full stakeholder approval.
Production Deployment Capability
Production LLM systems require environment architecture, identity integration, secrets management, monitoring and observability, incident response, and operational runbooks. We deploy solutions that operate reliably under real users and real data, not demos that break outside a controlled environment.
Evaluation and Measurement
Proving an LLM system works requires more than anecdotal feedback. We build evaluation frameworks around golden datasets, accuracy measurement, retrieval quality scoring, hallucination detection, and regression testing so you can quantify performance and catch quality degradation early.
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
Enterprise AI adoption is not one-size-fits-all. Some organizations need an initial readiness and risk assessment, others are ready for targeted RAG implementation, governance design, or full production deployment. Our engagement models are structured, time-bound ways to engage our LLM consulting team based on where you are today and how quickly you need to move from exploration to production.
LLM Readiness Assessment
Timeframe: 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
Timeframe: 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
Timeframe: 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
Timeframe: 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
Timeframe: 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
Timeframe: 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.
Start the Right LLM Conversation
Discuss your LLM engagement with experts who understand both the technology and the business realities. Learn how to define your scope, risks, and outcomes before you invest time and budget.
Skills and Roles We Bring
We 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.
LLM architecture and implementation
Our team designs and builds RAG systems, embedding strategies, chunking approaches, retrieval optimization, prompt engineering, and response quality tuning that work reliably with your data and use cases.
AI/ML engineering
Production LLM systems require model selection and evaluation, inference optimization, cost management, and performance tuning with technical depth beyond configuration.
Enterprise security and governance
Every deployment includes data classification, access control design, audit logging, compliance documentation, and risk assessments that satisfy security teams and compliance stakeholders.
Microsoft and cloud platforms
Our consultants bring deep experience with Azure OpenAI, Microsoft 365, SharePoint, Power Platform, and Azure infrastructure, along with familiarity with AWS, GCP, and platform-agnostic deployment patterns.
Integration and data architecture
LLM capabilities connect to existing systems, workflows, and data sources through APIs, connectors, data pipelines, and identity integration.
Evaluation and observability
Measurement frameworks built around golden datasets, quality metrics, monitoring dashboards, and alerting prove systems work and detect when they don’t
Project delivery:
Structured implementations with clear milestones, stakeholder communication, risk management, and quality gates ensure AI projects are delivered predictably, not chaotically.
Typical engagement team composition:
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
Every engagement follows a structured process designed to surface problems early, keep stakeholders aligned, and deliver production-ready results on a predictable timeline. The specifics vary by engagement type, but the discipline doesn’t.
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 minimize 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.
- 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: The code, documentation, policies, and other artifacts we create are yours. Full ownership transfers to you.
Protect Your Data, Compliance, and IP from Day One
Engage with an LLM partner that treats security, governance, and ownership as foundational, not optional. We design and deliver AI solutions that respect your data boundaries, meet regulatory expectations, and ensure everything we build belongs to you.
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.
- 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.
- US-based, senior expertise. Enterprise-experienced, US-based consultants who make decisions and solve problems. No offshore handoffs, no junior staff learning on your project.
- 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.
Frequently Asked Questions
Most engagements can begin within 1-2 weeks of agreement. If you have urgent timeline requirements, let us know, and we’ll tell you honestly what’s achievable.
It depends on scope, complexity, and duration. Assessments are shorter and less expensive than production deployments. We provide specific estimates after understanding your situation. We don’t publish generic pricing because engagements vary significantly.
No. While we have deep Microsoft expertise (Azure OpenAI, M365, SharePoint), we work across platforms. We help you evaluate options, Microsoft, Anthropic, OpenAI, direct, open-source models, based on your requirements, not our partnerships.
Yes. Packaged vendor tools are right for some use cases, particularly those deeply integrated with Microsoft 365 workflows. Custom RAG or other approaches may be better for specialized knowledge retrieval, specific accuracy requirements, or use cases outside their scope. We help you evaluate objectively.
Common situation. We help you assess the portfolio, identify what to consolidate, what to advance, and what to stop. A fragmented approach wastes resources and multiplies risk; we help you build a coherent strategy.
Yes, through retainer engagements for ongoing advisory, monitoring, and optimization. We also structure handoffs to enable your internal team to operate independently, with documentation and training.
Yes. We coordinate with incumbent vendors, MSPs, and internal teams. We define roles clearly to avoid overlap and ensure coverage.
We build governance, evaluation, and human oversight into deployments. We implement guardrails against prompt injection and data exfiltration. We design human-in-the-loop controls for high-stakes decisions. Responsible AI isn’t a separate workstream; it’s how we approach every engagement.
We tell you what readiness requires and help you prioritize. Sometimes the right answer is “address data quality first” or “build governance before scaling.” Honest assessment serves you better than premature deployment that fails.
