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We provided enterprise software development services to build an AI finance automation system that drives treasury management and reconciles cash flows, leaving the existing tools untouched.
This client is a B2B fintech that operates in the UK, Germany, and the Netherlands. After several rounds of funding, it ended up with an inefficient financial stack, comprising NetSuite as its ERP system, a separate payments provider, various bank APIs in different countries, and old FP&A systems with no financial process automation capabilities.
The financial process automation challenges presented to Jelvix had a multi-layered nature.Operational PainFinancial data was manually extracted from NetSuite, banking websites, and payment processors daily. Monthly closing required four to five days. Forecasts were based on 30 days, 60-70% accurate, revised weekly. Anomalies in transactions appeared only two to three days after.Compliance RiskNew markets bring complex local regulations. Manual data handling causes errors, delaying audits and risking penalties. Regional format mismatches create constant friction.
Strategic RiskEntering each new market introduced more sources of information and manual operations. More staff are needed for the finance team to scale, whereas the reporting needs of the board keep increasing. The CFO gets an overview of the financial situation with a lag of three to five days.Technical BottleneckConventional financial systems can be used for budgeting; however, they cannot automatically reconcile accounts or detect any anomalies. BI systems create dashboards but cannot fix problems in the upstream process. For accurate cash flow forecasting, an AI development is required.
The AI finance automation solution involved building a centralized finance data layer for AI/ML applications, using all existing data points as the source of truth.Module 1: AI Cash Flow ForecastingImplementing an LSTM model with data from 18+ months helped extend the AI cash flow forecasting from 30 to 90 days. AI financial forecasting is produced across best-case, base-case, and worst-case scenarios. The accuracy of AI financial forecasting increases by 25% each month.Module 2: Automated Reconciliation EngineAn automated reconciliation software uses a gradient boosting model for reconciling transactions between NetSuite, banks, and payment gateways. More than 90% of transactions undergo straight-through processing, where the month-end close is shortened from 4-5 days to 8 hours.Module 3: AI Anomaly Detection and Smart AlertsAI anomaly detection algorithms compare transaction data with a standard benchmark for spotting duplicate transactions, suspicious activity from suppliers, or abnormal spikes. The alerts are classified according to their level of severity.Module 4: CFO Intelligence Dashboard and Cash Management AutomationP&L, cash position, and runway metrics are available automatically with no need for user intervention. The CFO can question data in conversational form and get an automated reply back. All queries are processed via an enterprise-grade Azure OpenAI layer with Zero Data Retention (ZDR) to guarantee strict GDPR compliance.Security and ComplianceEvery model is based on the data provided by the client. There is an audit trail for each automatic decision that meets European traceability guidelines and includes CloudTrail logging.
Integrated AI Modules
Cash Flow Forecasting
The architecture covers five distinct domains that integrate AI automation and Big Data in finance.Data Ingestion Pipeline: Collects the raw unstructured data feeds from NetSuite ERP, three banking APIs, and payments API through Apache Airflow, which performs incremental synchronization, dbt-based schema normalization, and data quality validation with the help of Great Expectations.AI/ML Layer: Includes an ensemble of LSTM models (PyTorch) to perform cash flow analysis for the next 90 days, an XGBoost machine learning model for transaction reconciliation, and an Isolation Forest (scikit-learn) for real-time anomaly detection.Analytics Layer: Supports live P&L, cash, and runway calculations by combining a FastAPI backend and React/TypeScript frontend with WebSocket capabilities; natural language queries and reporting are done using the Azure OpenAI service (GDPR compliant without data storage).Security and Compliance Layer: Uses role-based access controls with AWS IAM, AWS KMS to encrypt data at rest, and AWS CloudTrail to log all machine-learning automated decisions, complying with SOC 2, UK FCA, and Dutch/German regulations.Infrastructure Layer: Developed purely using AWS technology stack – SageMaker for ML training and inference, Lambda for event-driven processing, RDS (Postgres) as a data store for transactions, S3 for storing our data lake, and CloudWatch & EventBridge for monitoring.
The project needed fintech software development, ML engineering, and enterprise data architecture expertise:Project Manager: Coordinating AI/ML and Data Engineering development across multiple work streams;Solution Architect: Financial data modeling, AI/ML architecture, and security architecture for regulated fintech;ML Engineers (2): Cash flow forecasting models (LSTM, ensemble), reconciliation matching engine, anomaly detection, and model validation;Data Engineers (2): ERP and banking API pipelines, data warehouse construction, and data quality framework;Backend Engineer: FastAPI services, real-time alert engine, and AI model serving layer;Frontend Engineer: CFO dashboard, scenario modeling UI, and real-time data visualizations;DevOps Engineer: AWS infrastructure setup, SageMaker MLOps pipeline, and CI/CD;QA Engineer: ML model accuracy validation, financial data integrity testing, and security testing.
The platform evolved from basic automation to advanced AI in stages: starting with reconciliation, moving to forecasting, and ending with real-time cash visibility.1. Data Foundation We started with a comprehensive audit of all the financial data sources used by the client, including NetSuite, banking APIs in three different regions, and payment processors. A central repository of all this data was created by creating an efficient data pipeline to feed data into it. The data quality checks were performed during this phase itself.2. Reconciliation Automation To help the client achieve an automated financial close, we created an AI-powered matching system based on the company’s historical transaction records, considering common and more complex scenarios, such as transactions that involve split payments, multi-currency, and partial matches. 3. Forecasting Engine The LSTM engine was trained and back-tested for 18+ months of historical transaction data. Multiple windowed periods were considered for testing seasonal and volatile behavior patterns. Confidence interval analysis using probability-based forecast scenarios was also carried out before making the initial 90-day forecast available to the finance team.4. Anomaly Detection Baselines of behaviors were modeled for each type of transaction and counterparty. The real-time alerting engine was put into action for two weeks while running parallel to human review, enabling the setting of severity thresholds and eliminating false positives.5. CFO Dashboard and Intelligence Once the reconciliation was done and forecasts added to the data, we designed the intelligence layer. The dashboard, NLQ interface, narrative generation for board packs, and scenario modeling were built and approved by the heads of the finance department at UAT.
The chosen stack supported real-time data processing, ML model serving, and regulatory-grade auditability:Data Pipelines: Python, Apache Airflow, dbt, Great ExpectationsML/AI: scikit-learn, XGBoost, PyTorch (LSTM), Azure OpenAI Service (GDPR-compliant), MLflow (model versioning)Backend: Python, FastAPI, PostgreSQL, RedisFrontend: React.js, TypeScript, Recharts / D3.js, WebSocketsCloud: AWS — SageMaker, Lambda, RDS, S3, CloudWatch, EventBridgeSecurity: AWS KMS, IAM, CloudTrail, SOC 2-ready configuration
Jelvix created a solution that has changed the way the 8-member finance team works within three European jurisdictions.
The team no longer spends 80% of its time collecting data. That capacity has been redirected to analysis, scenario planning, and strategic support for the CFO.
Financial decisions and automated financial close processes now rely on continuously updated real-time data instead of reports compiled over 3–5 days.
Double payments, strange vendor behavior, and transaction order anomalies are detected right away, rather than after several days of manual reconciliation.
For each market or product line, there is a data connection. As the number of products grows, it does not lead to an increase in finance employees’ workloads.
Complete transparency was provided by the AI finance automation solution within the client’s multi-jurisdiction financial system.
Transactions matched automatically across NetSuite, bank statements, and the payment processor with no intervention required.
Close compressed from four to five days to eight hours through automated reconciliation software.
With the use of the LSTM ensemble model, the forecast accuracy increased from 60-70% to 85-90%, with daily updates.
AI cash flow forecasting extended forward visibility from 30 to 90 days.
Irregularities in transactions are immediately identified upon occurrence, without a two-to-three-day detection lag.
Platform deployed on top of existing infrastructure with no operational disruption and no downtime.
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