Hire generative AI engineers who plan for the failure
A demo answers one clean question. Production has to handle the messy one, and know when to say it does not know. Our engineers build for that from day one, and you interview them before anyone joins.
What you are getting
4 things that decide this
- 01Senior engineers who have already shipped generative features to real users, not just to a demo audience of one.
- 02They build evals before they ship a prompt change, because that is the only way to know a change made things better.
- 03You interview every engineer before they join your team. Nobody is assigned from a profile you never saw.
- 04Every model call, prompt and guardrail they write is yours from the first commit, and you can swap models later without a rebuild.
The job here, past the demo
A generative feature is easy to demo and hard to trust. The engineer's job starts where the demo ends. What happens when the output is wrong, when a user asks something the system was never shown, or when the model behind it changes?
On Greenlight they built a three-stage research pipeline on GPT-4 and Perplexity Sonar-Pro that scores a company's sustainability. Every finding links to a live source. AI judgment counts for 66% of the score against 33% for data averages, so a claim without a citation cannot win. On Broollie they built agenda drafting and minute-writing with GPT-4o-mini. A finished call becomes tracked action items in about five minutes, sent to the right person by voice, WhatsApp or SMS. On Go4Gr8 they replaced a no-code chatbot with dedicated AI sparring partners per user, wired through MCP for commitment tracking. Onboarding for a new executive dropped to under 15 minutes.
Three products, one shared discipline: the generation is the easy 20%. The scoring, the routing and the guardrails are the rest.
What they take off your roadmap
Evals before the first prompt ships
A scored question set that runs on every change, so 'this feels better' becomes a number you can defend to your team.
Guardrails for the wrong answer
What the system says when it does not know, when a user pushes past its scope, or when the output would embarrass you if it shipped raw.
Model swap-out built in
The provider behind a feature changes over its life. They design the call layer so swapping models is a config change, not a rewrite.
Cost that does not surprise you
Token usage tracked per feature from day one, so a generative feature does not become the line item nobody can explain.
- Scoping callFree. What you need built
- ShortlistEngineers matched to the work
- You interviewYour process, your bar
- EmbedYour repo, standups, tools
- ReviewSwap if the fit is wrong
The interview is yours. A marketplace that assigns a vetted profile is skipping the only step that predicts fit.
How hiring works
- 01
Tell us what you are trying to ship
A free call about the feature, the users and the data behind it. If a generative approach is the wrong answer, you will hear that on the call.
- 02
Meet the engineers
We shortlist people who have taken a generative feature from demo to production, and you interview them. Say no and we go back to the shortlist.
- 03
They embed
Your repo, your standups, your ticket system. One engineer owns the feature and is named as the person accountable for it.
- 04
They hand over
The eval set, the prompts and the guardrail logic, documented, plus someone on your team trained to run it. Support and maintenance are free for the first 2 months, with every build.
Stack
Models and orchestration
Data and infrastructure
Practices
Tell us what the demo does not cover
Bring the feature and the edge cases that worry you. The scoping call is free, and you will leave it knowing what production actually requires.
01We already have a working demo. What is left to build?
Usually most of the work. A demo runs against clean inputs you chose yourself; production runs against every input a real user tries, including ones you did not anticipate. Our engineers add evals, guardrails for the wrong answer, cost tracking and a way to catch quality drift after launch. That gap is most of a generative AI build, not a finishing touch.
02How is this different from a freelancer who knows prompt engineering?
Prompting is a small part of the job. Our engineers design the evals, the fallback behaviour and the data pipeline feeding the model, and a company stands behind the result. A freelancer hands you a prompt and the risk that it stops working when your data changes.
03Can we swap the engineer if the project changes shape?
Say the word and we swap them out. You interviewed them, so this is rare, and it stays our problem rather than becoming a hiring cycle you run again.
04Who owns the prompts and the eval set afterward?
All of it is yours from the first commit, including the prompts, the guardrail logic and the scored question set. That eval set is what tells you months later whether output quality has drifted.
05What actually drives the cost of a generative AI build?
How well-defined your data and edge cases are, more than the model you pick. A narrow, well-scoped feature is quick; one with open-ended input and high stakes for a wrong answer takes longer to guard properly. Scoping calls are free. Where we have to get into an existing codebase before answering, a paid two-week diagnostic produces a fixed price rather than a guess.
