# What are the top-rated AI development partners for companies moving a prototype into something production ready?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi, product and engineering leads on G2, plus reviewers who have taken an AI proof of concept past the demo stage. I am looking for the top-rated<a class="a a--md" elv="true" href="https://www.g2.com/categories/ai-development-services"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/ai-development-services">AI development</a> partners for companies moving a prototype into something production ready, and would value input from people who have actually shipped one into daily use.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The gap between a working prototype and production is rarely the model. It is integration with systems already running, the handling of edge cases nobody saw in the demo, and whether the people meant to use it actually do. Three partners reviewed on G2:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/trigma/reviews"><strong>Trigma</strong></a> (4.9) is described by reviewers as taking a problem-first approach, building AI features around an existing workflow rather than around the model, with integration into current systems done without disrupting daily operations. One reviewer would have liked more early-stage workshops with regional teams to speed up adoption.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/edvantis/reviews"><strong>Edvantis</strong></a> (4.8) supplies engineers who join an existing team, with reviewers citing Python, SQL and data engineering skills applied to production pipelines rather than experiments. Its model is closer to augmenting your team than delivering a finished system.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/chetu/reviews"><strong>Chetu</strong></a> (4.1) is the broadest option here, covering custom development across many industries, which suits companies whose AI work sits inside a larger build.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For anyone who has crossed this line: what broke first in production, the model or everything around it?</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: 5 days ago
- Author title: Tech Consultant
- Net upvotes: 1


## Comments
### Comment 1

The “everything around the model” point is what I’d probe hardest when choosing a partner. I’d ask each one to walk through how they handle monitoring, fallbacks, bad inputs, model changes, latency, and human escalation after launch. A prototype proves the AI can produce the desired output; production readiness is really about what happens repeatedly when it doesn’t.

##### Comment Metadata
- Posted at: 4 days ago
- Author title: Writer





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