Build vs buy

Vertical AI for finance: build it, or buy it?

An honest look at what it takes to build a finance-grade AI in-house, versus buying nummera. Including when building is the right call.

12–18 mo
to reach a verified, integrated, finance-grade AI from scratch.
5–8 people
ML, data and finance engineers to build it, then keep it running.
~every quarter
the frontier models shift, and your build has to keep pace.
The comparison

How the three paths actually score.

Higher is better. Ratings reflect the typical outcome for a mid-market finance team, not the theoretical ceiling.

None Limited Partial Strong Complete
Dimension Build in-houseYour team builds it General-purpose AIChatGPT / Copilot + your data nummeraVertical AI, built for finance
Time to first valueWeeks, or quarters
Finance domain depthClose, variance, reporting
Accuracy & hallucination controlFigures checked against source
Live data integrationsFabric, Dynamics, SharePoint, ledgers
Runs on your own infrastructureAzure, GCP, AWS, on-prem
Auditability & sign-offTrail from raw data to board pack
Keeping pace with AIMaintenance as models change
Cost & effort to ownTotal cost of ownership
Control & customizationShape it to your exact process
Specialist talent requiredLess is better
Best when AI is your core product, and you have the team to own it. Quick, low-stakes drafting where mistakes are cheap. You want finance outcomes now, on your own infrastructure.
The pattern is consistent. Build wins on bespoke control. Buying wins on everything that ships value this quarter. nummera closes the control gap by running entirely on your own infrastructure.

Illustrative ratings for a typical mid-market finance team. Your mileage will vary with scope and in-house capability.

At a glance

The trade-offs, plainly.

Build in-house

Your team builds it

Strengths

  • +Total control over the product and roadmap.
  • +Shaped to your exact process and edge cases.
  • +The IP stays yours.

Trade-offs

  • −12 to 18 months before it earns its keep.
  • −A standing ML, data and finance team to maintain it.
  • −You now run a software company inside finance.

General-purpose AI

ChatGPT / Copilot + your data

Strengths

  • +Cheap and instant to start.
  • +Useful for ad-hoc drafting and summaries.

Trade-offs

  • −No finance depth, no source-checked figures.
  • −Copy-paste in, hope for the best out.
  • −Your numbers leave your tenant.

nummera

Vertical AI, built for finance

Strengths

  • +Live in weeks, on your own infrastructure.
  • +veriveri checks every figure against source.
  • +Built for the close, maintained as models change.

Trade-offs

  • −A product, not a bespoke build.
  • −A subscription, not your own IP.
The honest answer

When to build, and when to buy.

Build if...

AI in finance is genuinely your competitive edge, and you are willing to fund it like a product.

  • You have a standing ML and data team with capacity to spare.
  • Your process is so unusual that no platform could fit it.
  • You can wait 12 to 18 months and maintain it indefinitely.

Buy if...

You want better finance outcomes now, and AI is a capability you use, not a product you sell.

  • You need verified analysis this quarter, not next year.
  • Your data must stay in your own cloud or on-prem.
  • You would rather your team do judgement, not maintenance.
Skip the build

Buy the outcome. Keep the control.

nummera runs on your own infrastructure, verifies every figure, and is live in weeks. See it on your numbers.

Book a demo Talk it through with us