AI Consulting
Most businesses are not short on AI enthusiasm. They are short on clarity about where AI actually helps and where it introduces more complexity than it resolves. We help organizations move from 'we should be doing something with AI' to a specific, grounded plan — with honest assessments of capability, realistic implementation requirements, and clear guidance on when not to use AI at all.
Capabilities
What AI consulting covers
Where to start
AI Opportunity Assessment
A structured assessment of where AI can genuinely improve a specific business process — and where it cannot. We evaluate candidate use cases against the data requirements, accuracy requirements, and maintenance cost of an AI solution versus a conventional software approach. Not every automation problem is an AI problem.
Adding AI to existing systems
LLM Integration Planning
Planning the integration of large language model capabilities (OpenAI, Anthropic, Google, or open-source models) into an existing product or workflow. This includes prompt architecture, context management, latency and cost modeling, output validation, and the fallback behavior when the model produces incorrect output.
Knowledge-grounded AI
RAG Architecture Design
Designing retrieval-augmented generation (RAG) systems that ground LLM output in your organization's specific data — documentation, support knowledge, product information, or domain expertise. RAG is the approach that makes an LLM genuinely useful for domain-specific tasks where general training data is not sufficient.
Which model for which task
AI Vendor and Model Selection
Evaluating competing AI providers and models against specific task requirements: output quality, cost per query, latency, context window, fine-tuning support, data retention policies, and compliance requirements. Model selection decisions have significant cost and quality implications that merit a structured evaluation.
Advisory during build
AI Implementation Oversight
Providing technical oversight for an engineering team building AI features — prompt review, architecture guidance, evaluation methodology, and risk assessment — without taking over the implementation. This is useful for teams that have the engineering capacity to build but want expert guidance on the AI-specific decisions.
Managing AI responsibly
AI Governance and Risk Framework
Defining the policies, review processes, and monitoring requirements for AI use within an organization: acceptable use policies, output review workflows, bias monitoring, accuracy tracking, and incident response procedures for when AI-generated output causes a problem.
Our approach
Our recommendation is to earn independence from AI tooling first
Our approach to AI adoption is grounded in a principle we hold about software in general: reach a position where you do not depend on any AI tooling, then layer AI in where it clearly helps. Teams that build their workflows around AI outputs before they understand the system's limitations create fragile processes that break unpredictably. Build the non-AI version first; add AI where it genuinely improves it.
Not every business problem is an AI problem
A business process that is slow because it is poorly designed will not be improved by adding AI to it. A document review that takes too long because of an approval bottleneck needs a process change, not a language model. We spend time on the problem definition before recommending an AI solution, because the right answer is sometimes a conventional software approach.
AI output requires human review in high-stakes contexts
Large language models produce plausible-sounding output that is sometimes incorrect. For internal tools where errors are low-stakes, a fully automated AI pipeline may be appropriate. For customer-facing content, legal or compliance work, or medical contexts, the architecture must include human review of AI output at appropriate checkpoints. We design review workflows as part of any AI system.
Cost scales with usage in ways that surprise teams
AI API costs are not flat fees. They scale with usage — token count, query volume, model tier. A prototype that costs $20 per month may cost $2,000 per month at production scale. We model AI costs at realistic usage volumes before a system is built, so that cost surprises do not appear after deployment.
All engineering work is done by US-based engineers. We do not offshore any development or architecture work.
Part of our Consulting practice
FAQ
Common questions
Virginia · United States
Trying to figure out where AI actually fits?
If your organization needs honest guidance on AI capability and a specific plan for where to apply it, reach out and we will assess your situation and recommend where to start.