Applied Artificial Intelligence

AI & machine learning development for practical business decisions

Turn operational data into forecasts, alerts and automated actions. We design AI and machine learning features that connect to the systems teams already use instead of becoming a disconnected experiment.

Based in Kaduwela, Sri Lanka · Delivery for local and international projects
Enterprise AI platform visual with predictive analytics and connected machine learning models
Connected solution architectureDesigned around real users, systems and operating conditions
CustomModels aligned to your data and workflow
API-firstPredictions that connect to existing systems
Human-ledReview points for responsible decisions
Solution overview

Useful AI starts with a decision, not a model

We begin with the business decision that needs to improve, assess whether the available data can support it, and select the simplest reliable approach. The result can be a prediction API, an intelligent dashboard, a workflow assistant or an automated action inside an existing platform.

  • Architecture aligned to your current systems and future roadmap
  • Responsive experiences for desktop, tablet and mobile workflows
  • Clear delivery stages, integrations and acceptance criteria
What we deliver

Core ai & ml development capabilities

A focused delivery scope can combine the capabilities below or start with the highest-priority operational need.

01

Forecasting and prediction

Demand, yield, stock, risk or operational forecasts built from relevant historical signals and evaluated against clear business outcomes.

  • Time-series forecasting
  • Classification and scoring
  • Model evaluation and monitoring
02

Intelligent dashboards

Decision views that explain patterns, surface anomalies and help teams act without manually combining reports.

  • Trend and anomaly detection
  • Prioritised alerts
  • Role-specific decision views
03

AI-enabled workflow automation

Automation layers that translate model output into review queues, notifications or controlled actions inside existing workflows.

  • Approval-aware automation
  • API and ERP integration
  • Audit-friendly decision records
Where it fits

Practical use cases for ai & ml development

Examples of the operational problems this solution can be designed to support.

Demand and inventory planning

Estimate future product demand, highlight likely stock pressure and support purchasing teams with better planning signals.

Operational risk alerts

Detect unusual patterns across transactions, sensor readings or business processes and route them to the right team.

Field and agriculture intelligence

Combine historical records, field observations and sensor data to support forecasting and timely interventions.

AI features inside existing products

Add prediction, summarisation or decision-support functions to a current web platform, mobile app or ERP workflow.

Technology reveal

A modern stack selected around the solution

Technology choices are made around integration needs, data boundaries, operating conditions and the team that will maintain the system—not a one-size-fits-all checklist.

Machine learning
PythonLightGBMFeature EngineeringModel Evaluation
Application layer
TypeScriptNode.jsREST APIsNext.js
Data layer
PostgreSQLMongoDBOperational Data Pipelines
Delivery
Containerised ServicesMonitoringVersioned Models
Delivery approach

From business need to production operation

A staged path keeps the important decisions visible and gives teams review points throughout delivery.

  1. 01

    Discovery and technical review

    We map the business goal, current systems, available data, users, risks and practical constraints before recommending an approach.

  2. 02

    Architecture and delivery plan

    The solution is broken into clear modules, integrations, milestones and acceptance criteria so the delivery path stays visible.

  3. 03

    Build, integrate and validate

    We develop in reviewable stages, connect the required systems and validate the important workflows, security controls and edge cases.

  4. 04

    Launch and continuous improvement

    After release, we support production use, monitor the system and improve it using real operational feedback.

Delivered work

Related projects from the Keen Systems portfolio

Published work that demonstrates relevant technology or workflow experience.

Smart Agri-Suite Mobile App for Smart Agri Research Team
Smart Agri Research TeamSmart Agri-Suite Mobile App

A farmer-support application for spice farming with forecasting, stock alerts, machine learning predictions and notifications.

FlutterFirebasePython MLLightGBM
Answer-ready guidance

Frequently asked questions about ai & ml development

Clear answers to the questions teams commonly ask before scoping a project.

Do you train custom models or use existing AI services?

Both approaches are possible. When historical business data supports forecasting or classification, we can train a focused model around that data. When a proven external AI service is more reliable and economical, we can integrate it with suitable controls.

What data is needed for a machine learning project?

The requirement depends on the decision being improved. Useful sources often include historical transactions, operational records, sensor readings or labelled outcomes. We assess completeness, relevance and data quality before proposing a model.

Can AI features connect to an existing ERP, website or mobile app?

Yes. A model can be exposed through a secure API or embedded into a dashboard and workflow, including Keen Systems ERP / ePOS where the use case and integration scope are suitable.

How do you keep automated decisions under control?

We define confidence thresholds, human review points, activity records and fallbacks based on the risk of the decision. Higher-impact actions should remain reviewable rather than operating as an unexplained black box.

Start with a clear next step

Planning a ai & ml development project?

Tell Keen Systems what you need to improve, what systems are already in place and who will use the solution.