AI Consulting

AI Strategy Before AI Spending

The AI market is moving fast, and every vendor has a pitch. Before you commit to a platform, a model, or a six-figure implementation, you need answers to the questions that actually determine whether AI will work for your organization.

That’s what our AI consulting practice does. We help leadership teams cut through the noise, evaluate options against their specific requirements, and build a plan that delivers results — not just a proof of concept.

Free AI Resources

Choosing the Right LLM for Your Organization

Not all large language models are the same. They differ in capability, cost, data handling, compliance posture, and integration options. Picking the wrong one creates technical debt and vendor lock-in. Picking the right one accelerates everything that follows.

We evaluate platforms across the dimensions that matter for business use:

Claude (Anthropic)

• Strong reasoning and analysis capabilities for complex document processing and decision support
• Built-in safety and alignment features suited for regulated industries
• Extended context windows for working with long documents, contracts, and reports
• Well-suited for agentic workflows where AI needs to reason through multi-step tasks

OpenAI (GPT)

• Broad ecosystem with extensive third-party integrations and tool support
• Strong general-purpose capabilities across text, code, image, and audio
• Enterprise tier with data privacy commitments and SOC 2 compliance
• Deep integration with Microsoft 365 and Azure for organizations already on that stack

Google Gemini

• Native integration with Google Workspace and Google Cloud Platform
• Multimodal capabilities across text, image, video, and code
• Competitive pricing for high-volume processing tasks
• Strong performance on structured data analysis and search-adjacent workflows

Open-Source and Self-Hosted Models

• Full control over data — nothing leaves your environment
• No per-token costs after infrastructure investment
• Customizable for domain-specific tasks through fine-tuning
• Suitable for organizations with strict data residency or air-gapped requirements

Our recommendation depends on your situation, not on a vendor relationship. We work across all major platforms and recommend what fits your data, your compliance requirements, your existing infrastructure, and your budget.

Internal vs. Cloud-Hosted AI

One of the most consequential decisions is where your AI runs. Each approach has real trade-offs:

Cloud-Hosted AI

• Advantages: lower upfront cost, automatic updates, elastic scaling, fastest time-to-value
• Considerations: data leaves your environment, ongoing per-use costs, dependency on vendor availability and pricing changes

Internal / On-Premises AI

• Advantages: complete data control, no per-token costs at scale, regulatory compliance for data residency requirements, customization through fine-tuning
• Considerations: significant infrastructure investment, requires internal expertise to maintain, model updates are manual

Hybrid Approaches

Most organizations land somewhere in between — using cloud AI for general tasks where data sensitivity is low, and internal models for proprietary data processing. We help you draw that line based on your actual risk profile, not theoretical concerns.

AI Security and Compliance

AI introduces new attack surfaces and compliance questions that traditional IT security doesn’t cover. Our consulting engagements address these head-on:

• Data exposure risk — understanding exactly what data flows to AI providers, how it’s stored, and what contractual protections exist
• Prompt injection and adversarial attacks — designing input validation and output filtering to prevent manipulation
• Model output governance — establishing review processes for AI-generated content that represents your organization
• Regulatory alignment — mapping AI usage against HIPAA, CMMC, SOC 2, PCI DSS, and Texas privacy law (HB4) requirements
• Employee AI policies — creating clear guidelines for how staff can and cannot use AI tools, with technical controls to enforce them
• Shadow AI detection — identifying unauthorized AI tool usage across your organization before it becomes a data leak

What an AI Consulting Engagement Looks Like

1. Assessment — we interview stakeholders, audit current technology usage, and inventory the data that would flow through AI systems
2. Opportunity mapping — identify the 3-5 highest-impact use cases where AI can deliver measurable ROI within 90 days
3. Platform evaluation — test candidate models against your actual data and workflows, not generic benchmarks
4. Risk and compliance review — assess security implications and regulatory requirements for each recommended approach
5. Roadmap delivery — a prioritized implementation plan with timelines, costs, and success metrics your leadership team can act on