--- title: "How to choose an AI implementation partner" description: "A practical checklist for choosing an AI implementation partner: check the workflow plan, the team, the controls, code ownership, and the exit path." canonical_url: "https://www.galactus.dev/insights/how-to-choose-an-ai-implementation-partner" markdown_url: "https://www.galactus.dev/insights/how-to-choose-an-ai-implementation-partner.md" --- [All insights](/insights) Buying guide # How to choose an AI implementation partner A practical checklist for choosing an AI implementation partner: check the workflow plan, the team, the controls, code ownership, and the exit path. Updated 2026-09-28 / 3 min read Short answer Judge an AI implementation partner by what reaches production, not by the demo. Check the workflow plan, the team, the testing method, security, running costs, code ownership, and handover before you compare prices. ## 1\. Ask which job changes after launch A good proposal says which job will change, who owns it, which systems are involved, and how you'll judge the result. It also says what's left out of the first release. Be wary of a proposal that leads with a model, an agent framework, or a vendor platform before it explains the work. The tools should follow the workflow and its risks. ## 2\. Meet the people who'll do the work Ask who handles discovery, architecture, engineering, testing, and rollout. Meet the people who'll work with your team, not only the salesperson. Check how much of their time you actually get. The same people should be able to talk about user needs and production detail in one conversation. Fewer handoffs keep the business goal tied to the technical choices. ## 3\. Ask for proof beyond a polished demo Ask for examples that show the starting workflow, the systems, the controls, the rollout, and the measured result. A polished demo proves the team can present. It doesn't prove they can handle data access, edge cases, monitoring, and adoption. If client confidentiality limits what they can share, they can still walk you through their method, the ways these projects usually fail, and what they hand over at the end. ## 4\. Make them explain failure first Ask how they test quality before and after launch. You want real example cases, a written definition of unacceptable behavior, and a review step for model or prompt changes. High-risk actions need human approval or a hard-coded check. Check access control, data retention, secrets, audit logs, incident handling, and third-party terms too. How deep you go should match the risk of the workflow. ## 5\. Put the exit path in the proposal Write down where the code, docs, credentials, and infrastructure will live. Ask which parts depend on the partner or a proprietary platform. Know what happens if the engagement ends after the first release. A proper handover includes architecture notes, deployment steps, evaluation cases, monitoring, known limits, and a runbook your team can use. Your internal owner should join well before the final week. Key points - Insist on one clear production boundary and written exclusions. - Meet the people who'll do the work. - Ask for evidence of controls, rollout, and measured results. - Own the code, and make sure you can walk away. ## Common questions ### What should I ask an AI implementation partner? Ask for a plan at the workflow level, the names of the people doing the work, production examples, how they test quality, their security process, who owns the code, an operating cost estimate, and a handover plan. ### Should the partner use our existing stack? Usually, yes. If they want to add a new platform, they should explain why. Replacing systems that work adds cost and delays the first result. ### Who should own the source code? For custom work, the contract should give you clear rights to the code, and the code should live in a repository you control. Have counsel review the exact terms. ### How do I compare two proposals? Compare the production boundary, exclusions, deliverables, team, decision points, ongoing costs, and ownership. A low price can hide a prototype-only scope or lock you into a closed platform. Read next AI implementation 3 min read ## [What does AI implementation involve?](/insights/what-does-ai-implementation-involve) Once the demo works, the real job starts: the workflow, software, data, integrations, testing, controls, launch, and who owns it. Read guide AI implementation 3 min read ## [How much does AI implementation cost?](/insights/how-much-does-ai-implementation-cost) What moves the budget, and how we price a $5,000 sprint, a $20,000+ MVP, or a larger scale-up. Read guide Hiring 3 min read ## [AI automation engineer vs AI agency: which should you hire?](/insights/ai-automation-engineer-vs-ai-agency) When to hire in-house and when to bring in an outside team, judged on workload, speed, context, cost, and ownership. Read guide galactus / contact accepting projects ## Start with one workflow. We work in your stack, the same people run discovery and delivery, and you keep the code and the runbook. [See how we work](/services/ai-implementation) ## Structured data ```json {"@context":"https://schema.org","@graph":[{"@id":"https://www.galactus.dev/#organization","@type":"Organization","name":"Galactus","url":"https://www.galactus.dev","logo":"https://www.galactus.dev/galactus-brandmark.svg","foundingDate":"2018","founder":{"@type":"Person","name":"Colin Burke"}},{"@id":"https://www.galactus.dev/#service","@type":"ProfessionalService","name":"Galactus","url":"https://www.galactus.dev","description":"Forward-deployed AI engineers who build agents, automation, and integrations in your stack. Most first releases ship in four to eight weeks. 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