Search the data you already have.
Connect retrieval and language models to relevant business information, with access rules and source context built into the workflow.
AI INSIDE THE WORK YOU ALREADY DOMake AI work inside
Make AI work inside
your existing product.
Connect AI models, your data, and the software your business already uses. Start with one workflow where a useful result can be defined, tested, and put into practice.
START WITH THE WORKFLOW
Give AI a specific job.
Choose the user and the step that needs to improve. Then decide how AI should fit into the application and how its output will be used.
Turn information into usable output.
Apply AI to a defined document task, from extracting structured information to preparing a draft that a person can review.
Help people finish the next step.
Put AI assistance into the application where the work happens. Define what it can do, what needs review, and how errors are handled.
MODELS, DATA, AND APPLICATION LOGIC
Meet the engineering specialties
Make the whole integration work.
Calling a model is one part of the job. The surrounding engineering connects the result to a real business process.
AI IN YOUR EXISTING PRODUCT
Existing app
& data
& data
AIFeature
Business
workflow
workflow
Check the starting point.
Review the current application, APIs, data quality, access requirements, and constraints. Identify where the feature can connect and what needs preparation.
Choose and evaluate the approach.
Compare the model and service options against the task. Where relevant, use retrieval-augmented generation (RAG) to give a model context from your own data, and test the answers against representative questions.
Plan operation and ownership.
Design authentication, error handling, review steps, monitoring, and cost controls around the feature. SVITS engineers can work with your technical lead or alongside Alex as advisor or CTO.
HOW WE START
One workflow. A measurable next step.
Agree what useful means before deciding how much to automate.
Map the task.
Identify the user, existing steps, and result that would make the feature useful.
Review the inputs.
Check data, permissions, APIs, and the application’s integration points.
Connect and test.
Build the integration and evaluate quality, latency, cost, and failure cases.
Release into the workflow.
Connect the feature to daily use, monitoring, and agreed engineering support.
SELECTED WORK
Engineering experience in AI products.
Our product work brings together application architecture, data, AI capabilities, and ongoing engineering.
AI & BEHAVIORAL HEALTH
Benehab
Voice AI and behavioral-health analysis in a care platform built and run by SVITS engineers under Alex’s architecture and product direction.
Explore the project CLINICAL & GENETIC DATAPGxCare / pgx.ai
A pharmacogenomics platform built by SVITS, with product and compliance engineering for clinical-data workflows.
Explore the projectWORKING WITH SVITS
Adding AI to an existing system.
Questions about data, model choices, and operating the feature.
Do we need to rewrite our product to add AI?
That depends on the current architecture and feature. Many integrations can connect through existing APIs or a focused application change. We review the system and workflow before deciding what needs to change.
What data is needed for an AI integration?
Start with the inputs the task depends on and representative cases for checking the result. We review relevance, quality, permissions, and how data is accessed before choosing an integration approach.
Can we use an existing LLM or AI service?
Yes. Existing models and services can be integrated where they fit the task, privacy requirements, quality targets, and operating cost. The product still needs application logic, data access, evaluation, and error handling around the model.
When is RAG useful?
Retrieval-augmented generation supplies a model with relevant information from your data when it produces an answer. It can be useful when a feature needs current or business-specific context. Whether it fits depends on the data, task, and quality of the resulting answers.
How do we judge quality and operating cost?
Agree representative inputs and useful outputs for the workflow. Evaluate correctness, failure cases, response time, and model usage, then review the complete experience with users before expanding the feature.
Who supports the integration after launch?
We agree ownership of application changes, model and data updates, evaluation, and operational support. SVITS can continue engineering alongside your team or under Alex’s technical leadership.
START WITH YOUR BUSINESS GOAL
Which workflow could AI improve?
Tell us about your software, the people using it, and the result you want to make possible.
Discuss an AI integration