How to Hire an AI Automation Partner for Financial Services
Hiring an AI development company sounds straightforward until you're three months and $200K into a project and the vendor asks what a covenant is.
AI in finance is not a horizontal problem. The firms that treat it like one deliver horizontal results — which is to say, they deliver very little. If you're evaluating an AI automation partner for finance, here's exactly what to look for, what to avoid, and how to structure the engagement so you get production value, not production promises.
Why Finance Needs a Specialized AI Partner
A general-purpose AI development firm can build you a chatbot. They can stand up a document classifier. What they can't do — without burning weeks of your time educating them — is understand why a bank statement from Chase looks different than one from a regional credit union, why DSCR calculations vary by lender, or why covenant compliance isn't a simple threshold check.
Domain specialization produces results 3–5x faster than horizontal AI firms. Not because the technology is different, but because the ramp-up time disappears. A specialized AI automation partner for finance walks in understanding your data, your workflows, and your edge cases on day one.
This is the difference between hiring an AI development firm and hiring the right AI development firm.
The Five Non-Negotiables
When you hire an AI development firm for financial services, these five criteria are non-negotiable:
1. Domain Expertise They should speak your language without a glossary. Ask them to walk through a specific finance workflow — rent roll extraction, loan tape reconciliation, covenant monitoring — and watch whether they lead or follow.
2. Time-to-Value Under 8 Weeks If the first deliverable is more than 8 weeks out, the engagement is structured wrong. You should see a working model on real data within 2–3 weeks.
3. IP Ownership You own the models, the code, and the data pipelines. Full stop. Any partner that retains IP ownership is building their product on your dime.
4. Transparent Pricing Fixed-scope phases with clear deliverables. No open-ended retainers. No "discovery phase" that costs $80K before a single line of code is written.
5. Measurable Outcomes Accuracy rates, processing times, error reduction — defined before work begins, measured throughout. Finance AI consulting services should be held to the same rigor as any other financial engagement.
Red Flags in the Evaluation Process
End the conversation if you see any of these, as outlined in our vendor evaluation guide:
- No domain-specific demo. If they can't show you finance workflows they've built, they haven't built any.
- 3+ month "Phase 1 discovery." This means they don't know what they're doing and need you to teach them — on your budget.
- No engineer in the first meeting. If it's all sales, the engineering team is either stretched thin or nonexistent.
- No finance references. Adjacent industries don't count. Ask for firms with your deal volume and complexity.
- Vague pricing. "It depends" is not a pricing model. Experienced firms can estimate within 20% after a single scoping call.
Two Models Worth Considering
The market has consolidated around two viable engagement structures:
The Product/SaaS Model You subscribe to a platform. It handles specific tasks — document extraction, compliance monitoring, reporting. Best for: standardized, high-volume workflows where your process matches the product's assumptions.
The Embedded Engineering Model Engineers deploy directly into your environment, building custom systems on your infrastructure. This is the Forward Deployed AI approach — no generic platform, no waiting on a vendor's product roadmap. Best for: firm-specific workflows, complex integrations, and teams that need production AI in weeks.
Most finance teams benefit from a hybrid: embedded engineering for the 2–3 workflows that drive the most value, and SaaS products for everything else.
Making the Final Decision
Don't decide based on a pitch deck. Run a 2–4 week paid pilot on a single, well-scoped workflow. Measure four things:
- Accuracy: Does the model meet or exceed your threshold on real data?
- Time savings: How many hours per week does it actually save?
- Integration quality: Does it connect cleanly to your existing systems?
- Communication: Is the team responsive, transparent, and easy to work with?
A paid pilot costs $15–30K. A failed 12-month engagement costs $300K+ and a year of lost productivity. The math is straightforward.
Ready to evaluate your options? Skip the research phase and book a 30-minute demo with our team. We'll walk through a live finance workflow, show you exactly what we'd build first, and give you a scoping estimate before the call ends. No slide decks. No "Phase 1 discovery." Just answers.