Natural Language Interfaces on Financial Data
P&L queries, forecast explanations, variance commentary, with structured output validation so numbers are traceable, not guessed.
FP&A & Fintech AI Engineering
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.
What We Build
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.
P&L queries, forecast explanations, variance commentary, with structured output validation so numbers are traceable, not guessed.
Planning engines built to your specific financial model structure, not generic templates applied to your data.
Every AI-generated number is traceable to a source. Regulators and accountants can follow the chain.
Clear milestones and acceptance criteria, not open-ended retainers. You know what you're getting and when.
Retrieval architectures grounded in customer-specific chart-of-accounts data, not generic financial knowledge.
Automated management reporting from ERP systems, plus intelligent detection of patterns that need human review.
Logiciel engineers embed directly into your workflow. Sprint planning, architecture reviews, code reviews, standups. We work inside your product architecture, not alongside it.
One sprint to understand your financial data model, architecture, and target use case. Fixed-scope estimate delivered at the end.
We design the validation layer, retrieval architecture, and data grounding before writing a line of production code.
Our team joins your sprints. Code reviews, standup, paired architecture. First production feature in 8 to 12 weeks.
Full documentation, tested infrastructure, and your team fully equipped to maintain and extend what we built together.
The difference between the products winning and the products churning is not which LLM they chose. It is the engineering surrounding it.
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
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
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 |
Your first production AI feature is live. Your CFO's Q3 deadline is met. Your engineers understand the architecture they are inheriting.
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.
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.