A successful demonstration still needs a delivery plan.
Data access, infrastructure, deployment and support may need work before an AI application can be used in day-to-day operations.
Engineering expertise / AI
AI engineering, from validation to deployment.
UTIS helps enterprise teams build useful AI applications and the data and cloud systems around them. Our work includes analytics applications, scientific AI workloads and document-processing workflows. We define the question to answer, agree reference results, test the implementation and explain what has been demonstrated and what remains before production.
AI engineering
Put validation inside the workflow.
- Define the task
Inputs, reference outputs and boundaries.
- Build and evaluate
Develop the workload and test its output.
- Agree the next stage
Separate validation from rollout.
Your priorities
Problems we help solve
These are common reasons to bring UTIS into the work.
Important knowledge is trapped in old documents.
Turning specialist documents into structured, usable information can require substantial manual effort.
A promising result is difficult to judge.
A speed or accuracy claim needs a clear test, comparison and delivery stage before it can guide an investment decision.
Our contribution
Where can UTIS help with AI engineering?
Build conversational analytics applications
Connect business questions to modeled data using Snowflake Cortex Analyst and Streamlit, with saved questions, charts, session behavior and a documented handover.
Prepare machine-learning delivery
Build frameworks for deploying and managing machine-learning workflows, often called MLOps. A Databricks-centric framework was built and demonstrated with example workflows.
Validate scientific AI workloads
Benchmark simulations on an existing GPU platform, compare the results with the reference physics and document how other teams can run the workload.
Turn documents into structured information
Build AI document-processing applications with client teams. The migration interface was deployed and demonstrated; production rollout in progress at time of writing.
Assist engineering with AI
Use AI-assisted tooling to draft configuration, generate pipeline code, review it and orchestrate changes, while engineers approve every promotion.
Azure ML · Databricks · MLflow · PyTorch · NVIDIA GPUs · LLM applications
Project experience
Examples of relevant results
Each result describes a specific engagement and retains its original source and scope.
Demonstrated
Databricks-centric MLOps framework
UTIS built and demonstrated a Databricks-centric MLOps framework with example workflows for a global semiconductor company.
Built and demonstrated
Read the case~10×
Faster scientific simulation
A scientific workload ran faster than the standard environment on the same physics.
(measured) · validated proof of concept
Read the case80–90%
Pipeline accuracy
The engineering-document pipeline achieved ~70% less manual workload. Production rollout in progress at time of writing.
(joint client–UTIS team result)
Read the caseWorking together
How do we validate an AI workload?
Define the task and comparison
Agree representative inputs, acceptable output and the existing result against which the AI work will be assessed.
Test in the relevant environment
Validate the model or application and record what the test does and does not establish.
Build the application around the model
Address deployment, permissions, monitoring and human review for the intended workflow.
Plan the remaining rollout
Document how to run the system and agree what must happen before production adoption.
Before we start
Frequently asked questions
What to expect when you work with UTIS.
Which AI projects have reached production?
Delivery stages differ. The Snowflake analytics application has client-confirmed delivery. The scientific GPU workload is a validated proof of concept, and the Databricks-centric MLOps framework was built and demonstrated. Each case states its scope and remaining rollout work.
Can you work with an existing GPU platform?
Yes. The scientific simulation case used an existing GPU platform. UTIS validated and benchmarked the workload, then prepared guidance for other teams using that platform.
What do you validate before expanding an AI workload?
We agree the task, reference result, execution environment and remaining dependencies. In the scientific simulation case, the workload reproduced the reference physics and was benchmarked against the standard environment. Your project would have its own acceptance criteria.
Meet with UTIS
Book a meeting.
Choose a preferred time for a 30-minute conversation about your project or hiring needs.
Choose a preferred time
Book a meetingUTIS will review your preferred time and confirm the meeting by email.