Technology experience
See how UTIS uses data, cloud and business-application tools. Each example links the technology to a specific contribution and makes the delivery stage clear.
01 / Technology experience
Collect and process data, check its quality and connect it to reporting.
Data platforms
Modernize machine-log processing, load changed records and maintain reporting during restrictions on source-system changes.
Project-reported cost comparison · delivered reporting models
The pipeline engagement reports about 57% lower average daily compute cost across 11 pipelines in its initial comparison. Selected reporting models were also made available with daily refresh.
Data, applications & engineering automation
Build data-processing and validation workflows, analytics applications and engineering automation in Python. Documented work includes automated data checks, Streamlit applications, analysis jobs on AKS and conversion of selected legacy workloads to PySpark.
Project-specific validation, application and data-engineering work
The Denodo upgrade case documents automated validation across more than 2,000 data views. Separate cases describe a delivered Snowflake application, validated analysis outputs on AKS, and Python-to-PySpark pipeline modernization.
Data platforms & analytics applications
Build a conversational analytics application using Cortex Analyst and Streamlit, including semantic models, saved questions, session behavior and charts.
Client-confirmed application delivery
The application delivery is confirmed by the client; its case explains the features and handover.
Reporting & analytics
Create business reporting, data models and dashboards with client business intelligence teams. Validate selected measures against agreed definitions and source queries.
Delivered reporting work
Direct client feedback confirms Power BI analytics and dashboards; delivery records document report validation and integration work.
Databases & data processing
Build and support warehouses and data-loading processes, including incremental processing, recovery, data quality and monitoring.
Measured incident-detection result
A new alert detected a failed overnight refresh the next day, after blind spots that had lasted months.
Enterprise reporting data
Make selected SAP HANA data models available in Databricks with daily refresh, so reporting can evolve during a freeze on changes to an enterprise resource planning system.
Measured selected-model result
Selected business-critical data models were made available in Databricks with automated daily refresh.
Connected data & automated checks
Use Python to compare data outputs before a Denodo upgrade, checking individual records and totals across saved queries used by reports and applications.
Measured validation scope
More than 2,000 data views—saved queries used by applications and reports—were automatically validated in the documented upgrade.
Cloud data migration
Move legacy datasets into Azure storage, improve data-loading speed and support retirement of an aging Hadoop platform.
Measured migration result
The migration reported a 35% ingestion-speed improvement and was completed through legacy-platform retirement.
02 / Technology experience
Deploy applications, automate changes and monitor how systems are running.
Running containerized applications
Build the cloud infrastructure and browser-based engineering-analysis application using Azure Kubernetes Service (AKS), Python and Databricks Apps.
Validated outputs · cutover in progress at time of writing
Analysis outputs matched the existing production system; daily cutover in progress at time of writing.
Repeatable infrastructure deployment
Define cloud infrastructure in code so deployment can be repeated and reviewed. Documented work includes an engineering application and machine-data processing.
Deployed interface · production rollout in progress at time of writing
The engineering interface was deployed and demonstrated; production rollout in progress at time of writing. The machine-log pipeline case separately documents deployment improvements.
Automated software releases
Contribute to application integration and automated build, test and release processes—known as CI/CD—using Azure DevOps in a manufacturing analytics project.
Validated outputs · cutover in progress at time of writing
Production analysis code runs on the new platform with outputs matching the existing system. Daily-workflow cutover in progress at time of writing.
System-health monitoring
Rebuild monitoring for an existing machine-data platform using Prometheus. Extend Grafana data access and connect additional machines so teams can inspect the data and system health.
Delivered monitoring & user enablement
All scoped pipeline components were brought into one monitoring dashboard, and users were enabled to access the data.
Live machine-data processing
Operate and extend the path that collects machine events, stores readings over time and supplies monitoring dashboards.
Delivered operations & extension
The machine-data case describes the existing platform, its monitoring and access provided to users.
Cloud computing environments
Prepare Azure virtual machines and application dependencies for engineering analysis in a development environment.
Engineering-analysis platform · cutover in progress at time of writing
Daily-workflow cutover in progress at time of writing.
03 / Technology experience
Build AI tools and business applications, with each example’s delivery stage stated.
Scientific AI
Build and benchmark an AI simulation on an existing GPU platform, compare outputs with reference physics and prepare instructions for other teams.
Validated proof of concept
Approximately ten-times faster simulation in the documented proof of concept. This is a workload benchmark, not a general platform guarantee.
AI document processing
Work with the client team to build an LLM/NLP pipeline that reads legacy XML and generates decision logic and configuration rules, with a custom migration interface.
Production rollout in progress at time of writing
80–90% pipeline accuracy with ~70% less manual workload (joint client–UTIS team result).
Machine-learning deployment
Build and demonstrate a Databricks-based framework with examples for deploying and managing machine-learning workflows.
Built and demonstrated
Built and demonstrated a Databricks-centric MLOps framework with example workflows.
AI use-case discovery
Build an AI assistant using Mistral and Databricks Mosaic AI to help engineering teams identify and describe potential AI use cases.
Agent built · adoption outcome not established
The build is documented in the delivery record. Production adoption and measured business outcomes were not established.
Business applications
Connect reporting to a business application that replaces manual delivery lookups and separately sent notifications.
Delivered workflow example
This workflow accompanies the SAP HANA-to-Databricks reporting work. No separate percentage time saving is claimed for the application.
Workflow automation
Build approval workflows in an enterprise business platform, connecting business requirements to a usable process.
Delivered workflow example
The delivery record includes approval automation. No separate percentage saving is claimed for these workflows.
Customer portals
Provide technical architecture, access controls and the process for deploying and updating a customer portal.
Implementation · supplied engagement summary
The engagement progressed from proof of concept into implementation with a part-time UTIS technical architect.
The firm behind the skills
UTIS engineers are assessed by practitioners in the relevant technology. We agree the responsibilities, expected results and handover with your team.
Compare ways to work with UTISTechnology names describe tools used in the documented work. They do not imply vendor partnership or certification. Results retain their engagement-specific scope; client references require permission and appropriate confidentiality arrangements.
Meet with UTIS
Tell us which system or project needs support. We’ll discuss relevant experience and a practical way to work together.
Choose a preferred time
Book a meetingUTIS will review your preferred time and confirm the meeting by email.
