How we compare
The Elnova Audit vs. Building AI In-House
Most companies weighing this aren't choosing between good and bad options. Both can work. The real differences are timeline, who's spending the time, and what happens if the first guess about the problem is wrong.
| Dimension | Elnova | Building in-house |
|---|---|---|
| Timeline to first result | A 2-3 week audit, then a scoped build | Often 3-6 months before anyone agrees on what to build |
| What you're paying for | A fixed-fee audit that finds the highest-leverage problem before any code is written | Engineering time spent discovering the problem and building the fix, usually without a dedicated AI hire |
| Risk of building the wrong thing | The audit stops before the build if there's nothing worth fixing | Sunk cost bias means most teams ship what they started, not what turned out to matter |
| Who owns it after launch | Documentation, handover, and a retainer option | Whoever built it, until they leave |
| Best for | Teams that want one specific, prioritized problem fixed | Teams that already have a dedicated AI engineering function with time to spend |
Choose Elnova when
You know something is slow but not exactly what, or why, or what it's costing you. The audit exists to answer that before anyone commits to a build.
Choose Building in-house when
You already have a dedicated AI engineering team with time to spend discovering the problem before building anything. Most mid-market operations teams don't have that team yet. That's the gap the audit fills.
Still not sure which fits?
That is usually where the audit starts. Two to three weeks, fixed fee, no pitch deck.