- FP&A & Fintech AI Engineering
Your CFO Wants AI In the Product.
Your Engineers Have Never Trained A Model On Financial Data.
We know exactly where that leaves you. Embedded AI engineering teams who have already built financial AI that accountants trust, on a timeline your board will believe.
We know exactly where that leaves you. Embedded AI engineering teams who have already built financial AI that accountants trust, on a timeline your board will believe.
The Problem
Financial AI Has a Failure Mode That General AI Does Not
Your VP of Engineering is looking at the team. Good engineers. They built the financial modeling engine. They understand the data structures. But none of them have fine-tuned a language model on financial data, built a retrieval system on a customer's chart of accounts, or shipped an AI feature where being wrong on a number costs a customer their board presentation.
A hallucinated revenue figure in a CFO dashboard is not a minor bug. It is a trust-destroying event that ends the product relationship.
The cost of getting this wrong
69%
of FP&A time still spent on manual data gathering. Every CFO wants AI to fix this. (FP&A Trends 2025)
70%
of CFOs say finance digitization has been slower or less impactful than expected
$750K+
annual cost to build a 3-5 person AI team in-house, before infrastructure
8-12 wk
time to first production AI feature with an embedded Logiciel team
Your team has shipped AI features before. A chatbot. An autocomplete. Smart search. The tolerance for edge case errors was reasonable. FP&A AI is different. The use cases that drive actual adoption, AI-assisted planning, natural language queries on financial data, automated variance explanations, require the model to be correct in ways that are verifiable, auditable, and that your customers' accountants will check.
DORA 2025 data confirms this pattern: AI amplifies what is already there. Teams with deep AI experience ship dramatically faster. Teams learning AI tooling while shipping financially-critical software show individual throughput up and organizational velocity flat, with edge cases found by customers rather than QA.
You need an AI engineering team that already understands why a wrong number is a product-killing event, and has built the validation architecture to prevent it before they write a line of code in your codebase.
Building that combination in-house costs $750K to $1.5M annually. Twelve months to staff. Another six before the team is productive on your specific architecture. Most FP&A software companies do not have that runway.
— WHAT WE BUILD
The Full Engineering Layer, Not Just The Model
We build the validation layer, the retrieval architecture, the data grounding, and the audit trail. Financial AI without that surrounding engineering is a demo, not a product.
Natural Language Interfaces on Financial Data
P&L queries, forecast explanations, variance commentary, with structured output validation so numbers are traceable, not guessed.
AI Scenario Modeling & Driver-Based Planning
Planning engines built to your specific financial model structure, not generic templates applied to your data.
LLM Integration with Full Audit Trails
Every AI-generated number is traceable to a source. Regulators and accountants can follow the chain.
ERP Integration and Data Pipelines
Clear milestones and acceptance criteria, not open-ended retainers. You know what you're getting and when.
Financial RAG Systems
Retrieval architectures grounded in customer-specific chart-of-accounts data, not generic financial knowledge.
Anomaly Detection on Financial Time-Series
Automated management reporting from ERP systems, plus intelligent detection of patterns that need human review.
— HOW IT WORKS
Embedded, Not Outsourced
Logiciel engineers embed directly into your workflow. Sprint planning, architecture reviews, code reviews, standups. We work inside your product architecture, not alongside it.
Discovery Sprint
One sprint to understand your financial data model, architecture, and target use case. Fixed-scope estimate delivered at the end.
Architecture Review
We design the validation layer, retrieval architecture, and data grounding before writing a line of production code.
Embedded Build
Our team joins your sprints. Code reviews, standup, paired architecture. First production feature in 8 to 12 weeks.
Clean Handoff
Full documentation, tested infrastructure, and your team fully equipped to maintain and extend what we built together.
— THE MARKET RIGHT NOW
The FP&A Software Market Is Bifurcating
The difference between the products winning and the products churning is not which LLM they chose. It is the engineering surrounding it.
Products that are winning
- Shipped AI features that are reliably correct before scaling them
- Built the validation layer, not just the model
- Grounded retrieval in real customer financial data structures
- Expert users find the AI useful, not alarming
- Winning competitive deals on AI reliability
Products that are churning
- Shipped impressive demos that failed when a real accountant tested edge cases
- Repairing customer trust after a bad AI-generated number
- Rebuilding the AI layer under pressure with the wrong architecture
- Losing deals to competitors with more reliable AI outputs
- 12-month in-house hiring cycles while the market moves
“98% of CFOs have invested in digitization. Only 41% report that even 25% of their processes are actually automated.” (FP&A Trends 2025 Benchmarks)
75+
North American clients
3,000+
Product releases shipped
120+
Engineers on team
Days
Time to sprint-ready
— WHY NOT THE OTHER OPTIONS
What The Other Options Actually Look Like
Every option has tradeoffs. Here is an honest view.
| What you need | Toptal / Contractors | In-house ML Team | Logiciel |
|---|---|---|---|
| Financial AI domain knowledge | You source and vet this yourself | 12-18 month hiring cycle | Production experience in FP&A and fintech AI |
| Validation and audit architecture | Your responsibility to design | Your team learns while building | Pre-built patterns, first sprint |
| Time to first production feature | Varies. You own integration. | 12-18 months to productivity | 8-12 weeks |
| Annual cost | $150-250K per senior contractor | $750K-$1.5M per year | Fixed-scope. Starts at $55K. |
| Product delivery accountability | You manage them | Full ownership over time | Architecture, implementation, QA, and handoff |
— QUESTIONS CTOS ASK US
Direct Answers, No Pitch
Financial accuracy requires more engineering around the model than the model itself. We build retrieval-augmented generation grounded in the customer's actual financial data, implement structured output validation against known financial relationships, add confidence scoring and fallback handling for low-confidence outputs, and build audit trails so every AI-generated number is traceable to a source.
Multi-tenant financial AI has specific architecture requirements: customer data isolation in the retrieval layer, tenant-specific financial schema configuration, and security boundaries that prevent cross-tenant data exposure in LLM context windows. We architect for these from the first sprint. Retrofitting tenant isolation into an AI system is expensive and risky.
NetSuite, Sage Intacct, QuickBooks Online and Desktop, SAP S/4HANA and ECC, Microsoft Dynamics 365, Oracle EBS, Xero, and most mid-market accounting systems. We also build custom connectors for proprietary systems and data warehouses. ERP integration is usually the first build for FP&A AI because without clean source data, the model is working on noise.
Almost always a distribution mismatch. The training or test data does not match real customer financial data structure, accounting conventions, or edge cases. A manufacturing company's chart of accounts looks nothing like a SaaS company's. We run a data distribution analysis, identify the failure modes, and rebuild the affected pipeline with proper data grounding rather than tuning the model on top of a structural problem.
A focused AI feature, one NL interface, one automated reporting pipeline, or one anomaly detection system, typically starts at $55K. A full AI layer including NL queries, scenario modeling, automated reporting, and production inference infrastructure runs $140K to $380K. Fixed-scope estimates after one discovery sprint. No billing surprises.
We have built NL-to-query systems on financial schemas, natural language P&L explanation and variance commentary generators, and conversational planning interfaces. Financial NLI requires schema-aware prompting, structured output validation, handling of ambiguous financial terminology across different accounting standards, and careful scope limiting so the system does not answer questions it cannot reliably answer. We have production patterns for all of these.
— WHAT COMES NEXT
Your Product Wins The Deals Where Financial AI Reliability Is The Differentiator
If you are trying to ship financial AI your accountants will trust, booking this call is the right decision. We will come with a direct point of view on the architecture, not a generic AI capabilities pitch.
Week 8-12
Your first production AI feature is live. Your CFO's Q3 deadline is met. Your engineers understand the architecture they are inheriting.
Month 3-6
Customers are using the AI features in real planning cycles. Accountants are checking the outputs & trusting what they find. Churn conversations stop starting with AI reliability.
Competitive position
You are in the group of FP&A products that shipped AI that actually works in production. That is the group that wins the next wave of deals.
Book Your Free 30-Minute FP&A Engineering Call
Tell us what financial AI feature you are trying to ship and what the engineering challenge is.