AI in Financial Solutions Without Compliance Headaches

We prepare sensitive financial data for AI training while maintaining regulatory compliance.

We handle:

• Cleaning and structuring KYC, transaction, credit, and risk datasets
• Enabling secure access to PCI, SOC 2, and PII data with anonymization
• Feature engineering for models such as loan default prediction or AML scoring

Deliverables:

• Financial Data Dictionary
• Data Quality & Compliance Report
• Secure Feature Engineering Pipeline

Tools: Jupyter Notebooks, Databricks, Apache Spark, AWS Glue, Azure Data Factory 

We architect a secure, scalable tech stack to support financial AI use cases.

We handle:

• Selecting frameworks for fraud detection, credit scoring, or NLP for contracts
• Designing vector DB and RAG systems for document-heavy workflows (e.g., onboarding, underwriting)
• Choosing cloud and ML tools aligned with internal IT and governance requirements

Deliverables:

• Financial AI Stack Blueprint
• RAG Architecture for Document AI
  Integration & Infrastructure Strategy

Tools: Pinecone, ChromaDB, Keras, PyTorch, Amazon Bedrock

We train and fine-tune AI models for sector-specific tasks.

We handle:

• Training models for fraud detection, loan approvals, or investment signals
• Fine-tuning LLMs for document summarization (e.g., underwriting files, policy documents)
• Prompt and context engineering for financial chat agents

Deliverables:

• Custom ML or LLM Models
• Fine-Tuning & Evaluation Logs
• Use-Case-Specific Model Outputs

Tools: n8n, Make, CrewAI, OpenAI Agents,  Microsoft AutoGen, 

We ensure secure deployment, traceability, and regulatory visibility for financial AI.

We handle:

• Versioning models with audit logs for regulators and model risk teams
• Automating CI/CD pipelines for retraining fraud, scoring, or forecasting models
• Managing rollback, approval workflows, and access control

Deliverables:

• MLOps Governance Plan
• CI/CD Pipeline for Financial Models
• Model Audit & Registry Setup

Tools: MLflow, CircleCI, AWS SageMaker, Hugging Face, Kubeflow

We deploy models into production environments and integrate with banking software stacks.

We handle:

• Serving AI models via APIs into CRMs, loan origination systems, or payment rails
• Containerizing microservices for deployment on AWS, Azure, or internal infrastructure
• Optimizing inference latency for real-time decisions (e.g., credit card fraud blocking)

Deliverables:

• Financial API & Endpoint Specs
• Deployment & SLA Report
• Integrated Environment Plan

Tools: Neptune, XGBoost, Gradio, TensorFlow

We monitor financial models in production and ensure compliance, drift detection, and performance.

We handle:

• Real-time monitoring of fraud, credit, and advisory models
• Detecting concept or data drift in transaction-heavy environments
• Triggering retraining based on performance thresholds or regulatory audits

Deliverables:

• Monitoring Dashboards for Key Financial KPIs
• Alert System with Audit Trails
• Retraining Strategy with Regulator-Ready Logs

Tools: DataDog, New Relic, Dynatrace, Grafana, Evidently.AI