Your fintech or SaaS platform needs better risk, compliance, or analytics workflows
AI can easily detect anomalies, classify documents, speed up Know Your Customer (KYC) checks, analyze transaction patterns, and support reporting, saving hundreds of hours of manual work over time. However, for regulated products, AI integration requires proper engineering and safeguards to prevent hallucinations or compliance violations: secure architecture, role-based access controls, audit trails, data protection, and QA processes.
With 180+ delivered projects, AWS and Databricks partnerships, ISO certifications, and deep QA expertise, our AI integration company helps companies develop their systems from vague automation ideas to business-ready agentic AI systems that are secure, scalable, and architectured to withstand real operating conditions.
Stronger customer-facing product experience
AI can make portals, SaaS platforms, fintech apps, and internal dashboards more useful through natural-language search, smart replies, report builders, recommendation logic, and task automation. Instead of adding disconnected generative AI tools on top of existing workflows, Geniusee’s specialists help integrate them into the product use cases that your clients already follow, with proper security controls for business and customer data.
From fragmented travel requests to an AI-powered concierge platform
Geniusee partnered with a small travel company to develop an AI-powered concierge platform that integrates travel assistance and service management into a single digital environment. The MVP combines an AI-powered chatbot for travelers with an administrative platform for managing its content, users, data, and operations.
Case details
Materials scienceHow Geniusee helped Planck Technologies process 10,000 scientific papers with an AI data pipeline
Planck Technologies develops AI-powered technology for materials discovery, helping researchers evaluate porous materials for industrial applications. They addressed Geniusee to build a scientific data pipeline that collects research publications, extracts and normalizes materials data, preserves source provenance, and supports AI-assisted scientific research.
Case details
K’CIDADE Academy: How we built an AI-powered learning platform for AKF Portugal
K'CIDADE Academy is AKF Portugal's professional learning platform for civil society organizations, educators, and community leaders. Built on adaptive learning pathways, an AI virtual tutor, and a gamified progress system, it delivers personalized training to professionals working across social inclusion, education, and community development.
Case details
Scaling Edge AI & computer vision: QuantumEye real-time surveillance platform
Geniusee helped the QuantumEye AI platform that upgrades legacy CCTV with real-time detection, instant incident notifications, and decision-ready analytics.
Case details
Anatomy Financial: Automated financial management software for healthcare
Anatomy introduces an innovative solution designed to automate document processing, compare various documents and banking transactions, and handle the intricacies of physical check processing. By conducting a thorough analysis, the system identifies discrepancies between the declared prices of medical procedures and the actual amounts received by the clinic.
Case details
MartechCompose AI: Advanced content writing solution
We partnered with Compose AI to enhance their AI-powered writing platform through cloud migration, UX improvements, seamless integrations, and scalable feature development, upporting their growth from YC-backed startup to a widely adopted productivity tool.
Case detailsWhat makes Geniusee’s AI integration specialists efficient partners
“AI works best when it fits the business systems people already use. Since 2017, Geniusee has built software products, cloud infrastructure, and DevOps environments for companies pursuing business growth.Backed by our AWS Advanced Tier Service Partner status, we design enterprise-grade AI for real business workflows. This means making AI integration across products, operations, and infrastructure practical, secure, and production-ready.”
Taras Tymoshchuk
CEO, Founder
Check if your AI idea is worth building
Download the whitepaper to evaluate AI use cases, set value gates, validate PoCs, and scale only the workflows that prove measurable value. Includes readiness score, use case framework, PoC validation steps, governance, and scaling roadmap, based on Geniusee’s internal AI transformation, where one workflow saved up to 120 hr/month.
- Digital banking platforms for personal finance, account management, and customer self-service
- Lending and credit platforms for application intake, borrower profiles, and risk review workflows
- Payment and eWallet apps for transactions, user accounts, merchant tools, and operational dashboards
- WealthTech and investment software for portfolio views, client reporting, and advisor productivity
- Learning management platforms for schools, universities, corporate training, and online course providers
- Student portals for enrollment, learning records, schedules, communication, and self-service access
- Assessment and certification platforms for exams, testing flows, grading operations, and progress tracking
- Corporate training systems for employee onboarding, compliance learning, internal academies, and skills development
- eCommerce platforms for product discovery, checkout journeys, customer accounts, and order management
- Marketplace software for vendor management, listings, buyer journeys, moderation, and seller operations
- Retail management systems for store operations, stock visibility, pricing workflows, and sales performance
- Customer loyalty platforms for rewards, segmentation, personalized offers, and omnichannel engagement
- Property listing platforms for search, inquiries, broker workflows, and listing management
- Real estate customer portals for buyers, tenants, investors, and property owners
- Property management software for maintenance requests, tenant communication, payments, and documents
- Real estate analytics platforms for market insights, portfolio visibility, pricing context, and investment review
We use CRISP-DM logic as a base for data and AI work, then combine it with Geniusee’s software delivery process: discovery, architecture, engineering, QA, DevOps, and post-release support. This helps us bring AI into your existing product, workflow, or infrastructure without treating it as a disconnected experiment.
Discovery and AI consulting
We identify your business goals, current systems, data sources, and operational bottlenecks. This AI consulting stage helps define where AI can automate certain tasks, improve search, support decision-making, or personalize the user experience.
This step usually includes:
– Business process analysis
– Use case prioritization
– Data and system review
– Initial risk and cost assessment
– Integration feasibility check
Data and system readiness
We check whether your data, APIs, cloud environment, and architecture can safely support AI and ML features. Then, we prepare the technical foundation for secure, reliable integration.
This step can include:
– Data collection, cleaning, and structuring
– API and database readiness checks
– Cloud and infrastructure assessment
– Security, compliance, and access control review
– Architecture planning for AI integration
Model and solution design
We choose the right technical approach: prebuilt AI services, custom models, RAG systems, AI agents, or ML workflows. When needed, our data scientists build AI models for forecasting, classification, computer vision, NLP, or anomaly detection.
This step usually covers:
– Model and platform selection
– RAG, AI agent, or ML workflow design
– Prompt and guardrail planning
– Prototype scope definition
– Success metrics and evaluation criteria
Development, integration, and QA
Our engineers build AI components and connect them with your product, CRM, ERP, LMS, data platform, cloud services, or internal tools. Geniusee’s AI integration expertise covers engineering, data pipelines, MLOps, DevOps, and QA.
This step can include:
– AI feature development
– API and third-party service integration
– RAG pipeline or AI agent setup
– Automated and manual QA testing
– Security, performance, and regression testing
Deployment, monitoring, and improvement
We release the solution, monitor performance, and improve it as your workflows change. After deployment, we can tune prompts, retrain models, expand integrations, or add new AI features.
This step usually includes:
– Production deployment
– Model and system monitoring
– MLOps and retraining support
– Prompt, workflow, and feature improvements
– Ongoing support and troubleshooting
Certified AWS Partner delivering secure, scalable cloud-native solutions.
ISO-compliant processes ensuring quality, security, and reliability.
Trusted integration partner for financial data connectivity and open banking.
Team of ISTQB-certified QA engineers for world-class software testing.
Consistently rated ★5.0 by clients for reliability and delivery excellence.
Accredited partnership supporting advanced testing and continuous QA automation.
Genuisee’s versatile experience, gained over more than 8 years, has enabled us to form a team with a proven track record.
How can AI help my business?
It depends on where your team loses the most time or where decisions rely on data that’s hard to access quickly. Conversational AI handles repetitive customer interactions. Predictive models surface patterns in transactions, documents, or user behavior. The right starting point is usually a specific workflow, not an advanced AI strategy.
How does the AI integration process work?
We start by reviewing your existing software, data sources, and workflows to understand what’s actually needed. From there, we define the architecture, handle data integration, select the right models, and connect everything through APIs and workflow logic without rebuilding what already works.
How much does AI integration cost?
Scope drives cost. A focused AI integration service, such as a document automation flow or a support chatbot, is a different investment than a multi-system platform connected to CRM, ERP, cloud infrastructure, and analytics. We scope based on your data readiness, system complexity, and security requirements before providing the estimate. Contact us to discuss your project and get a rough estimate of costs, or use our Estimator.
How long does the AI integration process take?
Simpler automations and chatbots can be delivered in a few weeks. More advanced generative AI services, including RAG systems, AI agents, and multi-platform integrations, typically take three to six months. Data quality, third-party systems, and testing requirements are usually what affect the timeline most.
Do I need technical knowledge to get started?
No. You describe the process, product feature, or business problem you want to improve. Our team handles the technical side, recommends the right approach, and helps you deploy AI without requiring your team to manage the engineering details.
Do you offer ongoing support after AI integration?
Yes. Our AI integration services that connect implementation to long-term support include monitoring, prompt tuning, model updates, workflow adjustments, and performance checks. AI solutions drift without maintenance, and we make sure yours stays useful as your data, users, and business processes evolve.
