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Codelucent vs. hiring an in-house AI team.
We're not trying to replace your engineering org. We're the right call for a specific window — before the first 2-3 AI hires, during risky rollouts, and for specialist work your team shouldn't have to hire for.
| Codelucent | In-house AI team | Do nothing | |
|---|---|---|---|
| Time to production | 3-6 weeks | 4-9 months | Never |
| Up-front investment | $25-100k engagement | $600k+ first year | $0 |
| Specialist depth (RAG, MCP, evals, infra) | |||
| Cloud AI infrastructure | |||
| Eval harness + CI gates from day one | |||
| HIPAA / SOC 2 scaffolding | |||
| Institutional context over time | |||
| Owns 24/7 on-call long term | |||
| Ships your roadmap forever | |||
| Risk if roadmap changes | Low | High | N/A |
When Codelucent is the better call.
- — You need production AI in weeks, not quarters.
- — You haven't hired your first AI engineer yet and aren't ready to commit.
- — A regulated rollout needs specialist scaffolding you'd rather not build twice.
- — You have one or two AI features to ship, not a roadmap for a new team.
- — You want evaluation and audit work done by people who don't ship the thing being audited.
When in-house is the better call.
- — AI is core to your product roadmap for years, not quarters.
- — You need 24/7 on-call ownership of running systems.
- — Your domain is unusual enough that institutional context matters more than speed.
- — You already have 2-3 strong AI engineers and need to scale the team, not bootstrap it.
Most of our clients do both.
We ship the first production AI system and the infrastructure underneath it. Your in-house team takes over on day one — we hand off runbooks, evals, and context so you're not dependent on us.
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