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Custom AI Software

We build end-to-end software applications with integrated artificial intelligence: from the backend to ML models, from APIs to responsive frontends. Cloud-native, secure, and scalable architectures designed around your real processes — not the other way around.

Custom applications with integrated artificial intelligence models, designed for your workflow.

Use cases

  • Internal document analysis platforms
  • Dynamic pricing tools with ML
  • Candidate/position matching systems
  • Predictive dashboards for management
  • Marketplaces with AI recommendations

Measurable benefits

  • Software perfectly aligned with your processes
  • Ownership of code and models
  • Vertical and horizontal scalability
  • Faster time-to-market than building an internal team

Technical details

Backend stack

  • Python (FastAPI, Django), Node.js, Go
  • PostgreSQL, MongoDB, Redis
  • Microservices on Kubernetes
  • Event-driven with Kafka/RabbitMQ

AI/ML stack

  • PyTorch, TensorFlow, Hugging Face
  • OpenAI, Anthropic, Mistral integration
  • MLOps with MLflow and Weights & Biases
  • Model serving with Triton/BentoML

Frontend stack

  • React, Next.js, TypeScript
  • Tailwind CSS + custom design system
  • PWA and mobile-first
  • WCAG 2.1 AA accessibility

Cloud and DevOps

  • AWS, Google Cloud, Azure
  • CI/CD GitHub Actions / GitLab
  • Infrastructure as Code (Terraform)
  • Monitoring Grafana, Datadog, Sentry

From requirement to software in production

  1. Discovery — We map the process the software must support: actors, steps, exceptions, data in and out, regulatory or contractual constraints.
  2. Architecture — We decide where data and inference live, how users authenticate and which components stay replaceable — the model in particular must not become structural.
  3. Requirements and roles — We turn the process into verifiable requirements and a permission model: who sees what, who approves what, what is logged.
  4. Prototype — A clickable prototype on real data shows whether the flow works for daily users before we write production code.
  5. Backend and frontend — We build APIs, persistence, interfaces and integrations; model-based features sit behind a service layer with explicit fallbacks.
  6. Evaluation — Before release we measure output quality on real cases, behaviour on anomalous input and steady-state inference cost.
  7. Deployment and observability — Separate environments, application logs, error tracking and usage metrics, so post-launch support is driven by data.

Delivery includes source code, technical documentation, deployment scripts and operating instructions; ownership is defined in the project contract.

Working with us

The team that analyses the process is the team that builds and maintains it: product, engineering, integration with your existing systems, governance of automated decisions and post-release support.

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FAQ

What technologies do you use?

We select the stack based on the project: Python and Node.js on the backend, React/Next.js on the frontend, AWS/GCP for the cloud. The code is always yours.

Can I integrate existing AI models?

Yes, we integrate APIs from OpenAI, Anthropic, Google, or self-hosted open-source models on your cloud.

What happens after the go-live?

We offer evolutionary maintenance contracts with dedicated SLAs, guaranteed hotfixes, and a quarterly roadmap.