.avif)
AI-Powered Financial Analytics Platform Built with Multi-Agent Architecture
USA
Microservices architecture
Platform integrations
Multi-agent AI
MVP to production
7 AI agents
Orchestrated into a single
system
system
5× lower token cost
Optimised context and routing
efficiency
efficiency
One flow
Financial data, from ingestion
to insights
to insights
Business context
We built a financial analytics platform that connects to accounting systems, consolidates revenue and expense data, and provides AI-driven insights. The goal was to replace manual analysis in accounting tools with a system that explains financial performance in plain language and forecasts future spend based on real data.
Delivering reliable financial insights requires more than raw LLM output, it demands structured context, controlled inputs, and validation at every step
.avif)
Reliable AI on real financial data
To make AI useful for finance, we had to ensure reliable outputs, control token cost and latency, and keep financial data consistent across sources. There was no predefined solution, core parts of the system were designed and validated through iteration
Precision
Answer quality
We had to turn long LLM outputs into concise, source-backed insights about the company's data.
Efficiency
Token cost
We needed to control context size across agents to make analysis fast and affordable.
Consistency
Data integration
We had to sync QuickBooks and Stripe data and keep ledgers consistent.
Radency pulls off a SaaS MVP powered by 7 AI agents
We started an R&D project to see how agentic AI performs on real financial data. In one month, we shipped the MVP, and a couple of weeks later, we polished it into a fintech SaaS with data-backed analysis
01
Financial data integration
QuickBooks and Stripe were integrated as sources of truth, ensuring consistent revenue and expense data.
02
AI analysis pipeline
An agent-based workflow was introduced to prepare data, build context, and generate structured financial insights.
03
Forecasting layer
A prediction model was added to project future spend, with outputs grounded in historical data.
04
Multi-agent architecture
The system was split into specialized agents, each handling a specific task to reduce context size and improve performance.
05
Working AI financial system
The result is a platform that analyzes financial data, surfaces insights, and provides explainable forecasts in real time.
Results achieved with the Radency team
7-agent architecture in production
Data ingestion, analysis, forecasting, and validation handled in a single pipeline.
~5× reduction in token usage
Lower cost and faster response times through optimized context handling.
Explainable financial insights
Outputs grounded in source data, not black-box responses.
“Now our team knows how AI agents behave on real-world data, so we can deliver agentic AI faster, more affordably, and with full confidence.”
.avif)
Max Honcharuk
Partner & Solution Architect at Radency
Finance analysis MVP, shipped in 1.5 months
We built a multi-agent finance app that ingests accounting and payment data, surfaces AI insights in a dashboard and reports, and lets users chat with their ledgers. It connects to QuickBooks and Stripe via secure APIs and runs on a cloud-ready microservices stack
.avif)
01
Agentic AI core
7 agents collaborate to prepare data, build context, call the LLM, and refine outputs into finance insights.
02
Dashboard with AI insights
The dashboard shows spend spikes, trends, and variances at a glance, with quick drill-downs to the underlying transactions.
03
Detailed reports & charts
Reports cover monthly, quarterly, and yearly views, and users can export them for reviews or sharing.
04
AI chatbot
Users can ask plain-language questions about P&L or expenses, and get answers grounded in the source data.
05
Explainable forecasting
The app projects future expenses from historical data, and each forecast shows where the numbers came from.
06
Role-based access & invites
Access is scoped by role, and team members can be added with simple invites.
From idea to agentic AI in finance in ~45 days
The platform is now powered by 7 AI agents that analyze, explain, and forecast a company’s revenues and expenses
Before
No AI agents for finance analytics
High model cost from oversized context
Unclear projections and “black box” results
Intuition-based budget planning
After
7-agent MVP delivered in ~1.5 months
~5× lower token usage via smart routing
Explainable insights with cited sources
Data-driven forecasts of future spend
Increase your team velocity
Book a free consultation to see how our certified engineers can be embedded into your product development process
If you have more engineers like this, I'll make space for them.
.avif)
I'm delighted to say that deciding to go with Radency has taken away a key area of stress in my life!
.avif){kind=icon}
They have exceeded expectations in terms of value for money, professionalism, and quality.
.avif)
They really took the time to understand our project and asked the right questions.
Tell us about
your ideas
Oops! Something went wrong while submitting the form.
.avif)
.svg){kind=icon}
.avif)