Why most AI consulting fails

Most AI consulting produces a slide deck, a workshop, and a prototype that dies on staging. The firm optimizes for billable hours, not production outcomes. Champlin Enterprises optimizes for a single metric: does the AI system work in production, measured on the cases that matter most to your business. That means evaluation harnesses before production, RAG with source attribution, and a ship-or-it-does-not-count bias.

The AI consulting engagement model

Three quarterly engagement slots. Sprint engagements start at $10,000 and typically last two to three weeks. Build engagements scale with the scope of the production system. Fractional AI engineering lead retainers are available for clients on the active roster. Every engagement is led and executed by a single senior engineer — no team, no juniors, no outsourcing.

The AI stack we use in production

Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-weight models (Llama, Qwen, DeepSeek). Vector stores on Pinecone, Weaviate, or Postgres with pgvector. Orchestration in TypeScript, Python, or Laravel depending on your existing infrastructure. Evaluation in Promptfoo, Langsmith, or custom harnesses. We are not locked into a single vendor or framework.

What this looks like in practice

Production LLM integrations

Grounded retrieval, structured output, tool use, and streaming. Deployed, monitored, and continuously evaluated.

RAG architecture

Your data, your answers. Zero hallucinations on source-bound queries. Built for regulated industries where provenance matters.

Evaluation harnesses

Before you ship, you know whether the AI is right on the 500 cases that matter most. Every deploy runs the eval in CI.

AI strategy and roadmapping

Where to invest, where to wait, which vendors are production-ready, and what the rollout looks like. Written analysis, not generic frameworks.

FAQ

Common questions

What AI consulting services does Champlin Enterprises offer?

Production LLM integrations, retrieval-augmented generation (RAG) architectures, fine-tuning, evaluation harnesses, intelligent workflow automation, AI strategy and roadmapping, and code audits of existing AI systems.

Which LLM providers do you work with?

Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-weight models (Llama, Qwen, DeepSeek). Provider selection is part of the consulting engagement — not an up-front assumption.

Can AI consulting produce measurable ROI?

Yes — but only when it is scoped as a production system, not a prototype. Typical measurable outcomes include decision-loop compression (days to minutes), knowledge-worker hour recovery, and triage throughput increases on tickets, contracts, or underwriting queues.

Do you help with AI strategy, or only implementation?

Both. AI strategy engagements produce a written analysis of where to invest, where to wait, and which initiatives will actually ship. They can be paired with a build-sprint option so strategy and implementation are not separated by a six-month RFP cycle.

How do you handle AI safety, accuracy, and hallucinations?

Production-grade RAG with source attribution. Evaluation harnesses that run on every deploy to catch regressions. Prompt injection defenses on user-facing interfaces. Red-teaming against known jailbreak patterns. This is the work, not an afterthought.

Is AI consulting available as a retainer?

Yes. Fractional AI-engineering-lead retainers are available for clients already on the active roster — typically after one successful sprint or build engagement.

What is the minimum AI consulting engagement?

Sprint engagements start at $10,000. AI strategy sprints and code audits are commonly two to three weeks. Build engagements scale with the scope of the production system being delivered.

Three AI engagements every quarter.

A real AI problem. A real budget. A production outcome. The application takes ten minutes.

Apply for an Engagement