A project that needs a skill nobody has
Your team is good at what it does: a web app, an internal system, a data warehouse. Now there is a project that needs something new. Maybe an assistant that answers customer questions from your documentation, a classifier that routes incoming emails, or a model that predicts which orders will be late.
Nobody on the team has built one of these for real. Someone has tried a few prompts in ChatGPT and it looked promising. The question is who takes it from a promising demo to something your customers or staff can rely on.
Why hiring for it feels wrong
Your instinct that a permanent AI hire might not fit is often correct.
- The work has a shape: a burst of specialist design and build, then a much lighter phase of running and tuning it.
- Experienced AI engineers are in demand, so the search is slow and the hire is expensive.
- A single specialist on a team that does not understand their work becomes another person who knows everything, which is a different risk.
- The skills needed are narrower than the job title suggests. Wiring a language model into your app well is mostly software engineering plus careful testing, not research.
What is usually missing is not a data scientist. It is someone who has shipped this kind of feature before and knows where it fails.
What happens if you push on without it
| Common shortcut | What tends to go wrong |
|---|---|
| Demo moved straight to production | Answers are fine on test questions and wrong on real ones |
| No evaluation set | Nobody can tell whether a prompt change made things better or worse |
| API keys in the front end | Keys get copied and usage bills climb |
| No cost tracking per feature | The monthly AI bill surprises the finance team |
| No fallback to a person | Wrong answers reach customers with no way out |
None of these are exotic. They are the usual gaps between a demo and a feature, and someone who has closed them before saves your team from learning each one the hard way.
How we embed AI skills for the project
- Clarify the feature. We work with you on exactly what it should do, what data it can use, what a wrong answer costs and where a person should step in.
- Add an engineer from SpiderHunts with the matching skill, for example LLM integration with OpenAI or Anthropic Claude, retrieval over your documents, classic machine learning, or model deployment on AWS or Azure.
- Build inside your codebase. The AI parts live in your repository, follow your conventions and deploy through your pipeline, not as a separate black box.
- Set up evaluation from the start: a set of real examples with expected outcomes, so every change can be checked against it.
- Add the operational parts: logging of prompts and outputs, cost tracking, rate limiting, secrets kept out of code, and a route to a person when confidence is low.
- Pair with your developers throughout, so by the end they understand how it works and how to change it, and the documentation covers the rest.
When the build phase is done, the engineer can step back entirely, or stay on for a few hours to help with tuning. That is your choice, and it depends on how confident your team feels.
What you are left with
An AI feature that behaves predictably enough for real use, with tests that show when it drifts, costs you can see, and code your own team can maintain. Your developers have picked up practical AI skills along the way, which makes the next project easier to start.
Signs this fits
- You have a specific AI feature in mind, not a general wish to use AI.
- Your developers are capable but have not shipped an AI feature before.
- A permanent AI hire feels like too big a commitment for one project.
- You have a demo that works in testing but not yet in the real world.
- You want your own team to own the result afterwards.