Backend Services for AI Platforms
Reliable Python services that give AI platforms a stable, well-documented interface to build on.
Nearshore AI Backend & Integration Engineering
We build the backend execution layer for enterprise AI agents
We help AI teams connect agents, data platforms, and enterprise systems through reliable Python backend services, Azure/Databricks integrations, MCP/tool interfaces, and secure execution layers.
Kosovo-based engineering teams for DACH, Swiss, EU, and UK clients.
Python / FastAPI backend services & APIs
MCP and tool integrations for AI agents
Azure & Databricks integration engineering
Event-driven, observable execution layers
Kosovo-based nearshore engineering for DACH, Swiss, EU & UK clients
Python, FastAPI, Azure, Databricks, MCP & enterprise integrations
Individual engineers or small dedicated delivery pods
From AI prototypes to production-grade systems
The production gap
Most teams have AI prototypes. Few have them in production.
A working demo is not a working system. To run in production, AI agents need real integrations, APIs, security, monitoring, and execution workflows. That backend layer is exactly what we build.
- AI agents need governed access to real systems, not just prompts and replies.
- Prototypes stall because integrations, APIs, and execution workflows are missing.
- Production agents require permissions, retries, logging, and monitoring to be trusted.
- Databricks and Azure platforms need production-grade services built around them.
What we build
The backend layer that makes AI useful in production
Six connected capabilities, from the APIs agents call to the cloud and data platforms behind them, delivered with engineering discipline.
Agent Tool & MCP Integrations
Interfaces that let AI agents call internal systems safely, with validation and auditability built in.
Azure & Databricks Integration
Production connectivity between backend services and your Azure and Databricks data platforms.
Event-Driven Execution Layers
Asynchronous processing that keeps agent and platform actions scalable and recoverable.
Enterprise System Integration
Connectivity to the systems where business actually happens, from GIS to ERP and CRM.
DevOps, IaC & Observability
Deployment and operational discipline so AI-driven systems stay reliable in production.
How it fits together
A controlled path from AI agents to enterprise systems
AI agents need a controlled path to enterprise systems. Inovativi builds the backend layer that validates requests, exposes reusable tools, triggers workflows, connects data platforms, and records every action for reliability and auditability.
AI Agent / AI Platform
LLM apps, copilots, agent frameworks
MCP / OpenAPI Tool Interface
Reusable tools and typed contracts
Secure Execution Layer
Validation, permissions, audit
Python Backend APIs
FastAPI services and business logic
Queue / Event Bus
Async processing, retries, dead-letter
Databricks / Azure / Enterprise Systems
Data platforms, ERP, CRM, GIS
Monitoring / Audit Logs / Error Handling
Observability and reliability
Engagement models
One engineer, a small pod, or a defined project
Choose the model that fits how you work. Each one is staffed with engineers who own backend and integration outcomes end to end.
01
Individual Engineer Placement
For clients who need one strong backend or integration engineer embedded into an existing team.
02
Dedicated Nearshore Team
For clients who need a small delivery pod combining backend, data, DevOps, and AI integration skills.
03
Project-Based Delivery
For defined integration or AI backend implementation projects with a clear scope and outcome.
Case studies
Reference architectures and platform experience
A transparent view of the systems we design and build. Several are reference architectures and internal prototypes — labelled honestly, not presented as paid client work.
Reference Architecture / Internal Prototype
Enterprise AI Agent Integration Layer
A production-oriented backend execution layer that lets AI agents call tools, trigger workflows, interact with enterprise APIs, and execute actions safely with logging, retries, audit trails, and monitoring.
Read case studyReference ArchitectureDatabricks Workflow API Integration
A backend service that lets business applications or AI agents trigger Databricks jobs, track status, retrieve results, and expose data workflows through clean APIs.
Read case studyConcept Demonstration / ArchitectureLegacy System AI Modernization Layer
A phased modernization approach that wraps legacy systems with modern APIs and AI-ready integration layers, allowing gradual replacement without a risky big-bang migration.
Read case studyWhy Kosovo / Why Inovativi
Nearshore efficiency with senior engineering ownership
A European time zone, English-fluent engineers, flexible team setup, and a backend-and-integration mindset — led by a founder with a software and business background and advanced data science training.
Nearshore cost-quality advantage
Senior engineering capacity at rates that work for European budgets, without compromising on delivery standards.
European time-zone alignment
Kosovo sits in Central European Time, so collaboration, stand-ups, and reviews happen inside your working day.
English-fluent delivery
Engineers communicate clearly in English across calls, documentation, code reviews, and written updates.
Flexible team setup
Start with one engineer and scale into a small pod, or assemble a project team for a defined scope.
Backend & integration mindset
We focus on the execution layer: APIs, data platforms, queues, and reliability, not just model output.
Founder-led engineering
The founder combines a software and business background with advanced data science training, and stays close to delivery.
Technology stack
A modern, production-focused stack
The core technologies our engineers work with across backend, cloud, data, AI integration, and DevOps.
- Python
- FastAPI
- Azure
- Databricks
- PySpark
- Delta Lake
- MLflow
- MCP
- OpenAPI
- Docker
- Terraform
- CI/CD
- PostgreSQL
- Queues
- Monitoring
Next step
Need backend engineers for your AI platform?
Tell us the role or project and we will respond with suitable individual engineers or a small nearshore team for Python, Azure, Databricks, MCP, and enterprise AI integration work.