Retail
National retailer cut inventory carrying costs by 25%
Replaced a legacy rules-based demand forecast with a store-SKU-level ML model. Reduced stockouts on fast movers by 18% and markdowns on slow movers by 22%.
AI / ML
AI that ships. And keeps shipping.
From use-case discovery to production MLOps, we help enterprises turn AI ambition into measurable business outcomes.
What it is
Turn data into decisions that move the business.
We deliver end-to-end AI and machine learning programs — from strategy and data readiness through model development, deployment, and ongoing optimization. Our engagements cover predictive analytics, generative AI, natural language processing, computer vision, and recommendation systems.
Why it matters
The hard part isn't the model — it's the workflow.
Most AI investments stall between pilot and production. Models get built, dashboards get demoed, but very few make it into the workflows where they create value. We focus on the hard middle: the data pipelines, evaluation frameworks, and MLOps practices that turn a promising model into a durable capability.
What's included
AI strategy and use-case prioritization
Predictive analytics and forecasting models
Generative AI and LLM-powered applications
Natural language processing and document intelligence
Computer vision for inspection and automation
Recommendation and personalization engines
MLOps: CI/CD for models, monitoring, and retraining
Responsible AI: fairness, explainability, and governance
Real-world scenarios
How enterprises deploy this service to solve specific, high-stakes problems.
Financial services
Fintech lifted fraud detection precision by 40%
Built a real-time transaction scoring model with a feature store and drift monitoring. Cut false positives in half while catching a new family of account-takeover attacks within 48 hours.
Healthcare
Payer cut claims adjudication time from days to minutes
Deployed an NLP pipeline that extracts structured data from unstructured provider submissions. Straight-through processing rose from 34% to 71%.
Manufacturing
Industrial OEM reduced unplanned downtime by 30%
Delivered a predictive maintenance model on top of existing sensor telemetry. ROI recovered in under six months from avoided line stoppages alone.
How it works
1
Discovery
We map your business problems to AI patterns, assess data readiness, and identify the 2–3 use cases with the best risk/reward ratio for the first six months.
2
Design
We define success metrics, model architecture, evaluation framework, and the production path — before any model is trained.
3
Build & Deploy
We engineer the data pipelines, feature stores, training workflows, and deployment surfaces. Everything is built to be versioned, tested, and monitored.
4
Operate & Optimize
We stand up MLOps practices — drift detection, retraining, A/B testing, governance — so your models stay accurate as the world changes.
Typical outcomes
Faster decisions
Analytics cycle times cut from weeks to hours with automated, ML-driven forecasts.
Lower operating costs
15–30% reductions in inventory, fraud losses, manual review, or downtime across engagements.
Durable capability
Your team owns the resulting systems — we transfer both code and operational playbooks.
Works with
Python, PyTorch, TensorFlow, scikit-learnAWS SageMaker, Azure ML, Google Vertex AIDatabricks, Snowflake, dbtMLflow, Kubeflow, Weights & BiasesLangChain, LlamaIndex, OpenAI, Anthropic, BedrockFeature stores: Feast, Tecton
Why VS Tech
Outcome-first engagement
We scope to a measurable business metric, not a deliverable count. If the number does not move, the work is not done.
Production-grade from day one
Our engineers treat models like any other critical system — versioned, observable, and governed.
Team enablement baked in
We pair with your engineers and data scientists so capability stays after we leave.
Ready to see AI / ML in your environment?
Book a 30-minute working session with our team. We'll walk through your stack, your pain points, and what a pilot looks like.