AI and data solutions that actually work
We help businesses build practical AI and data infrastructure. From strategy to production — no hype, no science projects, just solutions that drive measurable results.
What we do
AI strategy & roadmap
We assess your business, data, and goals to create a practical AI roadmap. No hype — just a clear plan for where AI can make a real impact.
Data platform engineering
Build reliable data pipelines, warehouses, and analytics infrastructure. Get your data organized so you can actually use it.
Machine learning solutions
From prototyping to production deployment. We build ML models that solve real business problems — forecasting, classification, NLP, and more.
AI integration & automation
Integrate AI capabilities into your existing products and workflows. Custom AI assistants, document processing, and intelligent automation.
MLOps & model monitoring
Keep your models running reliably in production. Automated retraining, performance monitoring, and drift detection.
Analytics & insights
Turn raw data into actionable insights. Custom dashboards, reporting systems, and data-driven decision frameworks.
How it works
Discovery & data audit
We assess your current data, infrastructure, and business goals. This 1-2 week phase identifies where AI can create the most value.
Strategy & architecture
We design the data architecture and AI solution. Clear technical specs, realistic timelines, and a phased approach you can budget for.
Build & validate
We build the solution in iterative sprints — data pipelines first, then models, then integration. Regular demos so you see progress.
Deploy & monitor
We deploy to production with monitoring, alerting, and documentation. Ongoing support to keep everything running smoothly.
AI & Data FAQ
What kind of AI projects do you work on?
We work on a range of projects — from building data pipelines and analytics dashboards to deploying machine learning models in production. Common projects include demand forecasting, recommendation systems, document processing, and custom AI assistants.
Do I need a lot of data to get started with AI?
Not necessarily. We start by auditing what data you already have and identifying quick wins. Some projects work well with small datasets, especially when combined with pre-trained models and transfer learning techniques.
How long does a typical AI consulting engagement take?
A strategy assessment takes 1-2 weeks. A data pipeline project takes 3-6 weeks. A full ML model deployment takes 6-12 weeks depending on complexity. We break every engagement into phases with clear deliverables.
Do you build custom models or use existing ones?
Both. We evaluate whether existing models (like GPT, Claude, or open-source alternatives) solve your problem before building custom ones. Custom models make sense when you need domain-specific accuracy or data privacy.
Can you work with our existing tech stack?
Yes. We integrate with your existing infrastructure — whether that is AWS, GCP, Azure, or on-premise systems. We also work with common data tools like Snowflake, dbt, Airflow, and various databases.