01
ML readiness assessment
A structured evaluation of data quality, infrastructure, and AI maturity, with a prioritized implementation roadmap.
Impact: Identifies and resolves readiness gaps early
Data & AI services
Data science and ML services that predict, automate, and optimize
Apply advanced analytics and AI to predict outcomes, automate insights, and optimize enterprise performance across your data-driven operations.
What we do
Data science and machine learning services
Harness the latest in machine learning for your business, from computer vision to predictive analytics.
Our approach
Our approach to machine learning and data science projects
Six connected stages that take a project from business problem to a supported production solution.
- 01
Stage 01 / 06
Studying the problem
We start by studying your product needs and business challenges, documenting requirements and your vision to connect data and value.
- 02
Stage 02 / 06
Exploratory data analysis
We review your data infrastructure and explore datasets to find anomalies, missing values, dependencies, and patterns.
- 03
Stage 03 / 06
Data preparation
Before modeling, we prepare data by cleansing it and transforming it into a unified format.
- 04
Stage 04 / 06
Data modeling and evaluation
Our data scientists train multiple models, then choose the best on accuracy, simplicity, and performance.
- 05
Stage 05 / 06
Designing the solution
Whether a BI product, an ML algorithm, or a data management solution, we engineer, integrate, and test it as you adopt the new capabilities.
- 06
Stage 06 / 06
Support and maintenance
We help you release new features, add tools and data sources, and integrate the product further into your workflow over time.
Accelerators
AI/ML accelerators for faster delivery and production readiness
Proprietary frameworks that compress AI development timelines and speed the path from experiment to production-grade business solutions.
02
Feature store accelerator
A pre-built feature-engineering framework that standardizes ML input pipelines across model types and business domains.
Impact: Reduces feature development time
03
GenAI solution blueprint
A structured GenAI architecture framework covering LLM selection, RAG design, guardrails, and enterprise integration patterns.
Impact: Reduces GenAI pilot delivery time
04
Model validation and governance framework
Pre-built validation, bias testing, and governance documentation for enterprise-ready ML deployment.
Impact: Compresses model approval timelines
Tech & tools
Supported AI and analytics ecosystem
Our teams bring hands-on production experience across the full AI/ML development, orchestration, and deployment ecosystem.
AI and ML platforms
Azure OpenAI
AWS SageMaker
Google Vertex AI
Databricks
SAS Viya
LLM and GenAI frameworks
OpenAI GPT
Anthropic Claude
LangChain
LlamaIndex
Hugging Face
ML development
Python
R
scikit-learn
XGBoost
PyTorch
TensorFlow
MLflow
Data platforms and feature stores
Snowflake
BigQuery
Feast- TeTecton
Apache Spark
Deployment and MLOps
Kubernetes- Azure ML
- SeSeldon
BentoML
Visualization and BI
Power BI
Tableau
Good to know
Common questions about AI/ML services
It depends on the problem. LLMs are strong for language, generation, and reasoning over text, while a custom model often wins on narrow, high-stakes prediction where you control the data. Sometimes the right answer is both. We help you choose based on your use case, not the hype.
Ready to bring your idea into reality?
Tell us what you want your data to do. We'll come back with a clear first step.