Husnain Bukhari vs Hiring an in-house AI team
Hire one of us for the project that proves the business case before you hire three of them on payroll.
When Hiring an in-house AI team is the right choice
- AI is a 12-month-plus core competency, not a single feature
- You have $400K+/yr to spend on salary, benefits, and management overhead
- You can wait 3–6 months for hiring + onboarding + first production deploy
- Your engineering manager can run AI-engineer interviews competently
When you should pick us instead
- You need a feature live this quarter, not next year
- You want to validate the business case before committing to permanent headcount
- Your existing team can maintain it after the build but can't lead the build
- You're a single PM/founder with no AI hiring network to pull from
Side-by-side comparison
| Us | Hiring an in-house AI team | |
|---|---|---|
| Cost | $8K–$45K USD fixed-scope build, paid against milestones, billed once | $300K–$600K USD fully-loaded annual cost per AI engineer (US benchmark) before you ship anything |
| Speed to production | First production deploy in 4–10 weeks from kickoff | 3–6 months to hire + 2–3 months to onboard + first production deploy |
| IP & ownership | All code, eval set, and runbook in your repo. Your team can extend or hand off to the in-house team you eventually hire. | Same — but you're paying salary the entire time it takes to find and hire them. |
Common questions about this choice
- Will an in-house team eventually be cheaper?
- Per-feature, no. A senior US AI engineer at fully-loaded $400K/yr writes ~6–10 production features per year. The math only beats us once you're shipping AI on dozens of products simultaneously.
- Can you help me hire the in-house team after?
- Yes — we can write the JD, run technical screens, and pair-code with your top 2 finalists. Optional add-on at the end of an engagement.
- What about institutional knowledge?
- Documented in your repo, in the runbook, and in a recorded handoff session. Most clients say documentation quality is higher than what their full-time engineers leave behind.