AI/ML & GenAISolutions
We build machine learning models and generative AI features into products that run in production, not demos that stay in a notebook. From predictive models to AI assistants, every solution is scoped around a business decision it needs to improve.
Applied AI, engineered for production.
Most organizations don't need a research lab — they need machine learning and generative AI capabilities embedded directly into the systems their teams already use. Fountain designs and builds AI features around a specific business problem: forecasting demand, automating a manual review process, summarizing large volumes of text, or surfacing insight from data that currently sits unused.
We handle the full lifecycle — data preparation, model selection or fine-tuning, integration into your application, and monitoring once it's live. Where a packaged capability already exists, such as the predictive modules and generative AI features built into Fountain's own hospitality and retail platforms, we apply that experience directly rather than starting from zero.
What we deliver.
Generative AI Applications
Custom GenAI features — summarization, drafting, search and conversational interfaces — built into your existing products.
AI Assistants & Copilots
Purpose-built assistants that support a specific team or workflow, not a general-purpose chatbot bolted onto a website.
Machine Learning Solutions
Models trained and evaluated against your own data and the business metric you are trying to move.
Predictive Analytics
Forecasting and risk-scoring models — from occupancy and cancellation prediction to demand and churn.
Intelligent Automation
Combining ML with workflow logic to automate decisions that currently rely on manual review.
Enterprise AI Integration
Connecting AI capability into the systems you already run, rather than isolating it in a separate tool.
From challenge to production.
Understand
Business requirements, systems and constraints.
Architect
Design the right technical and operational solution.
Build
Engineer and integrate the platform.
Deploy
Securely move the solution into production.
Operate
Monitor, support and continuously improve.
Built beyond the prototype.
Enterprise-ready architecture
AI features built as part of the application architecture, not a disconnected script or plugin.
Responsible data handling
Data pipelines and access controls considered from the first design conversation.
Scalable model serving
Inference paths designed to hold up under real production traffic, not just a demo.
Integration with existing systems
Models and AI features connect to the data and applications you already operate.
Human-in-the-loop where it matters
Automation designed with review and override points for decisions that need them.
Maintainable, documented pipelines
Data and model pipelines built so your team — or ours — can maintain them after launch.
Deployment and monitoring support
We track model and feature performance after go-live, not just at handover.
Local understanding, global standards
AI delivered by a team that understands both your market and current ML/GenAI practice.
How it fits together.
One partner across the lifecycle.
Strategy → Build → Operate
Architecture-first approach
Software + Cloud + Infrastructure
Post-deployment support
Questions we hear about AI/ML & GenAI Solutions.
From predictive models — occupancy forecasting, cancellation risk, demand prediction — to generative AI features like summarization and AI assistants, and computer vision for recognition and detection tasks. We scope each project around the business decision it needs to support.
No. We handle data preparation, model development and integration. Where you have an internal data or analytics team, we work alongside them; where you don’t, we cover that ground ourselves.
Yes — most of our AI work integrates into existing applications and data sources rather than replacing them, connecting through APIs or direct data access.
Data handling, storage and access are scoped with you at the start of the project, and pipelines are built around the access rules and privacy requirements you define.
We monitor performance against the metric the model was built for and support retraining or refinement as real-world data comes in.