Services · AI Officer
Bring AI into your product without the agency overhead.
A Fractional AI Officer picks the right models, builds the eval pipelines, writes the production prompts, and ships the feature with your existing team. We do this for a small number of product teams at a time.
What an AI Officer does inside a product team
An AI Officer is a senior engineer with a narrow specialty: shipping language model features that hold up in production. They sit between your product team, who knows what the user needs, and your platform team, who knows the codebase. They translate vague ambitions like "we want a copilot" into a spec with a latency budget, an eval set, a model choice, a cost ceiling, and a rollback plan.
Without an AI Officer, the work usually defaults to whichever engineer is most curious about ChatGPT. That engineer writes a clever prompt, ships a demo, gets praise, and then watches the feature degrade for six months as edge cases pile up. The job of an AI Officer is to skip the demo phase and land in production directly.
Common use cases we ship
Retrieval augmented generation over your own corpus, where the answer must cite a source document. Internal copilots for support, sales, or ops teams, where the model takes actions in your existing tools. Eval pipelines that score model output against a golden dataset, so quality regressions show up before they reach a user. Model selection and routing, where a small model handles the easy 80 percent and a large model handles the rest. Cost control work where a feature is shipping but the inference bill is doubling every month.
We do not build chatbots that sit on top of a marketing site. The work has to live inside the product or it is not worth doing.
Our approach: eval first, then ship, then iterate
The first artefact of any AI engagement is an eval set. Twenty to two hundred examples that represent the real distribution of inputs, scored against the right metric. For a support copilot the metric is task completion. For a search feature it is answer relevance plus citation accuracy. For a writing assistant it is a blend of style match and factual grounding.
With an eval set we can compare models. We can change a prompt and see whether it actually helped. We can swap from GPT to Claude to a local Qwen and read the numbers, not vibes. Without an eval set you are guessing, and you will be guessing for the entire life of the feature.
Once eval is in place, we ship the smallest version of the feature that beats the bar. Then we iterate against real traffic, with the eval set as the floor.
Case study teaser
For a recent partner we built a production RAG feature in eight weeks. First pass on OpenAI gpt-4o-mini, escalation to gpt-4o on low confidence chunks, sub one second p95 latency, and a 73 percent reduction in inference cost compared to a single model baseline. Read the full writeup in the AI implementation playbook.
How this pairs with other engagements
Some teams take the AI Officer engagement on its own. Others pair it with a Fractional CTO engagement when AI is one of several open technical questions, or with SaaS product development when the AI feature is the product. We work with a small number of product teams at a time so we can stay close to the code.
FAQ
Common questions
What does a fractional AI Officer actually do?+
Picks the right models for your use case, builds eval pipelines so you can measure quality, writes the production prompts, designs the retrieval layer if RAG is needed, sets a cost ceiling, and ships the feature with your existing engineering team.
Do you only work with OpenAI?+
No. We are model agnostic. We have shipped with OpenAI, Anthropic, Google, Mistral, and on prem open weights. The choice is driven by latency budget, cost ceiling, eval scores, and data residency, in that order.
How do you handle hallucinations and quality?+
Every engagement begins with an eval set. We do not ship an AI feature without a measurable quality bar and a rollback plan. We use a mix of LLM as judge, golden datasets, and human review depending on stakes.
How is this different from hiring an AI agency?+
Agencies optimise for billable hours and demos. We optimise for a feature that survives contact with real users for a year. We embed in your codebase, write production code, and hand off cleanly.
Can you control inference costs?+
Yes. Cost ceilings are part of the eval. Typical patterns include cascading from a small model to a large model on low confidence, caching deterministic responses, batching where latency allows, and choosing the right context window for the task.
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