Turn Financial Data Into Clear Predictions
Most companies drown in spreadsheets. We've spent eight years building machine learning systems that actually read your financial patterns and show you what's coming next. No guesswork. Just numbers that make sense.
Explore Our Programs
The Problems We Actually Solve
Real challenges businesses face when dealing with financial data at scale
Data Sits in Silos
Your accounting system doesn't talk to your sales platform. Your bank feeds arrive in three different formats. Everything's disconnected.
We build unified data pipelines that pull everything into one place automatically.Pattern Recognition Takes Forever
By the time your team spots a trend in quarterly reports, you've already missed the opportunity or the warning sign.
Our algorithms flag anomalies and patterns within hours, not weeks.Forecasts Are Guesswork
Traditional projections use last year's numbers plus a percentage. That doesn't work when markets shift fast.
Machine learning models adapt to current conditions and update predictions daily.
How We Process Your Financial Data
We don't just crunch numbers. Our systems learn from your specific business patterns and market conditions to give you insights that actually apply to your situation.
- Automated data cleaning removes errors before analysis begins
- Pattern detection algorithms run continuously on incoming data
- Risk assessment models flag unusual transactions in real-time
- Forecasting engines update predictions as new data arrives
- Custom dashboards show only metrics that matter to your decisions
What Happens When You Work With Us
A straightforward process from data chaos to clear insights
Data Assessment Week
We connect to your systems and spend the first week mapping where your financial data lives. You'll get a detailed report showing data quality issues and integration points. This part's free because we need to see what we're working with anyway.
Pipeline Construction (2-3 Weeks)
Our engineers build the data pipelines that automatically pull information from your various sources. You'll see progress daily in a staging environment. We fix integration bugs as they come up, not after everything's built.
Model Training Phase
This is where machine learning starts recognizing your business patterns. The algorithms need at least 90 days of clean data to learn properly. During this time, you'll see accuracy improve week by week in test runs.
Go Live and Refinement
We switch on the live system and monitor performance closely for the first month. You'll have direct access to our technical team because the early days always reveal edge cases we need to handle. Adjustments happen fast.
Investment Levels
Choose what fits your data volume and complexity
Starter
- Up to 3 data source connections
- Basic pattern detection
- Monthly forecast updates
- Email support response within 24 hours
- Standard dashboard access
Professional
- Up to 8 data source connections
- Advanced anomaly detection
- Weekly forecast updates
- Priority support with 4-hour response
- Custom dashboard configuration
- API access for your systems
Enterprise
- Unlimited data source connections
- Real-time pattern recognition
- Daily forecast updates
- Dedicated technical account manager
- Fully customized ML models
- White-label options available
- On-premise deployment option
What Clients Say About Results
We were spending days reconciling transactions across four different banking systems. flickercharge's pipeline automated 90% of that work in the first month. Our finance team now focuses on analysis instead of data entry.
Attakorn Vichaidit
CFO, Regional Import Company
The anomaly detection caught a billing error pattern that would have cost us about 2.3 million baht annually. The system paid for itself in three months just from that one catch.