VARTEQ

Create with Various Technologies

AI development services

VARTEQ builds custom AI solutions and integrates AI into existing products and workflows, connecting models with business data and evaluating them against real requirements.

AI coding tools we use

  • Claude
  • GitHub Copilot
  • Cursor
  • OpenAI
  • Gemini
  • Windsurf

See our AI work

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ISO/IEC 27001 certified

AI development for business workflows

From assessing where AI fits to running it inside your systems.

  • Generative AI development

    Assistants, summaries, and generated material built on models such as OpenAI, Claude, and Gemini, connected to your own records and documents, with review steps where a person needs to decide.

  • Custom AI development

    Models trained on your data for one job, such as recognizing products in images and video or scoring deals from years of history, delivered behind an API your systems can call.

  • AI software development

    The software around the model: data collection and labeling, training and evaluation pipelines, human review for edge cases, and dashboards for progress and cost.

  • AI document processing

    Reading, classifying, and extracting data from business documents, such as mortgage files, and passing it to the systems that need it.

Our work

AI and machine learning work

All case studies

Results from these projects

What the work changed

Ways to work together

AI development, at the level of involvement you choose

Hand over a few seats, a whole team, or the entire project. The bar shows how much of the work we take on.

  1. We add engineers

    Staff augmentation

    Our engineers join your in-house team and work inside your process.

    You lead
    Roadmap, process, and day-to-day management
    We take on
    Engineers with the skills your team is missing
    How it works
  2. We run the team

    Dedicated team

    A stable VARTEQ team that works on your roadmap long term and grows or shrinks as plans change.

    You lead
    Product priorities and the roadmap
    We take on
    Running the team and its delivery
    How it works
  3. We own the delivery

    End-to-end project

    We take the project from scope to release and support, with milestones agreed up front.

    You lead
    Goals, scope, and sign-off at each milestone
    We take on
    Architecture, build, testing, release, and support
    How it works

Why VARTEQ

Built for the long run

Companies keep building with VARTEQ year after year. Some of our client relationships have lasted more than a decade.

Clients served
100+
Founded in Chicago
2014
ISO/IEC 27001 certified information security management system
Certified information security management.

What our AI projects run on

We pick models and tools for the task, not the trend, and our engineers stay accountable for every release.

Models and APIs · Chosen per task

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • DeepSeek

Machine learning · Selected for your use case

  • TensorFlow
  • PyTorch
  • Convolutional neural networks
  • Reinforcement learning
  • Human review loops

Integration · Into the systems you run

  • Model Context Protocol (MCP)
  • REST and web APIs
  • Microsoft Azure
  • AWS

AI in our delivery · Engineers stay accountable

  • Claude Code
  • Cursor
  • GitHub Copilot

FAQ

FAQs

Short answers to what clients usually ask before a first call.

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Can you add AI to our existing software and workflows?

Yes. We start with the workflow, the data available and the systems AI has to connect to. Often that means adding one capability, not rebuilding the platform.

How do you decide between generative AI and machine learning?

We choose the approach around the task, available data and required accuracy. Generative models can support tasks such as drafting and summarization; predictive models and computer vision can support scoring, forecasting and recognition. A solution may combine these approaches.

Can you build a custom model on our data?

Yes, when the data supports it. We check what you have, agree how results will be measured, then train and deliver the model behind an API your systems can call.

How do you evaluate AI output and protect our data?

We agree acceptance criteria and review points for each use case, and set data access to what the project needs. Where a decision matters, a person reviews it.

What does it take to move an AI prototype into daily use?

Integration with your systems, testing against real cases, access controls, monitoring and a clear owner after launch. Scope and timing depend on the starting point.

How do you keep people in control of AI decisions?

Edge cases and low-confidence outputs go to human reviewers, and we use their feedback to improve the model. In our own delivery, AI tools help write, test, and review code, while our engineers make the decisions and remain accountable.

Services

Engineering around the AI system

Services

  • Application Modernization

    Update older systems to support AI features.

    Learn more

  • API Integration

    Connect AI to the systems and partners you already run.

    Learn more

  • Data Engineering

    Pipelines that get your data ready for models and analytics.

    Learn more

  • Software Testing & QA Automation

    Testing for the software around your models.

    Learn more

Insights

Recent thinking from our engineers

All insights

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Discuss your AI initiative

Tell us about the workflow, the data and the systems involved. A VARTEQ partner will reply to arrange a first call.

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