Production AI Backends for Enterprise Documents, Workflows, and Integrations
We build the backend layer that connects retrieval, deterministic workflows, evaluation, and integrations — turning AI prototypes into systems your operators rely on every day.
Senior-led, engineering-driven delivery for enterprise teams across Europe and selectively in the US.
Beyond demos and chatbots
AI agents are only useful when they can act on real systems
AI agents are only useful when they can interact with real systems. That requires more than prompts. It requires APIs, event-driven workflows, secure execution layers, observability, and integration with cloud and data platforms.
Where AI initiatives stall
AI agents need governed access to real business systems, not just model responses.
Integrations are fragile when they lack contracts, validation, and error handling.
Backend actions require logging, permissions, retries, and observability to be trusted.
Cloud and data platforms need production-grade services built around them — on AWS, Azure, GCP, Databricks, or on-prem alike.
Enterprises need a secure execution layer, not only an LLM endpoint.
Service areas
The backend layer that makes AI useful in production
Seven connected capabilities, from the APIs agents call to the cloud and data platforms behind them, delivered with engineering discipline rather than hype.
Enterprise RAG & Hybrid Search
Retrieval systems that work on messy enterprise data: PDFs, contracts, tenders, policies, emails, reports, tables, and internal knowledge bases. Vector search, keyword search, reranking, metadata filtering, and source citations combine to produce grounded answers.
Multi-source indexing and permission-aware retrieval
Document Intelligence & Structured Extraction
Pipelines that parse complex documents and convert them into reliable structured data: PDF tables, scanned documents, legal judgments, procurement notices, invoices, forms, and operational reports.
PDF parsing, OCR workflows, and table extraction
Markdown conversion and JSON extraction with schemas
Validation rules and confidence scoring
Human review loops for high-value or uncertain cases
Deterministic AI Workflow Orchestration
Controlled AI workflows instead of uncontrolled autonomous agents. Each workflow follows defined states, business rules, approval steps, and audit logs.
LangGraph-style state machines and conditional routing
Tool execution with structured contracts
Workflow state persistence and webhook-based approvals
Human-in-the-loop approvals and escalation paths
Production Voice AI & Call Automation
Sub-300ms latency conversational voice systems wired securely into enterprise backend systems with strict human-in-the-loop escalation.
Low-Latency Native Audio Streams: Direct integration with native speech-to-speech models and optimized WebSockets/streaming infrastructure (Vapi, LiveKit, ElevenLabs) to match natural human conversation pacing.
Context-Aware Voice RAG: Specialized, ultra-fast vector search caching and semantic routing to fetch enterprise knowledge, compliance boundaries, and user records in milliseconds mid-call.
Deterministic Voice Guardrails & Escapes: Real-time validation layers that monitor audio outputs, prevent hallucinated commitments, and execute seamless state handoffs (transferring live audio + full transcripts) to human operators when edge cases or anomalies are detected.
Full System Tool Execution: Safe function-calling architectures enabling voice agents to reliably trigger webhooks, query databases, and update CRMs/ERPs mid-conversation without breaking system state.
AI Backend Engineering
The backend infrastructure required to run AI systems in production: APIs, databases, queues, storage, authentication, monitoring, cost controls, and integrations.
Observability, cost controls, and API integrations
Evaluation, Guardrails & Auditability
Measure and control AI behavior before deploying it into real business operations. Evaluation pipelines, regression tests, guardrails, audit logs, and quality dashboards.
Hallucination checks and prompt injection controls
Sensitive data protection and PII handling
Audit trails and operator dashboards
Legacy System AI Modernization
Add AI capabilities around existing systems without risky big-bang replacement. API layers, shadow systems, comparison engines, and workflow overlays for gradual modernization.
Strangler pattern with modern API layers
Shadow architecture and comparison engines
Read-only adapters and data synchronization
Phased migration support with audit logs
Computer Vision Workflow Systems
We build practical computer vision workflows where images become structured operational inputs — for example, guided measurement, visual recommendation, review queues, and quotation workflows.
Photo intake with calibration references (e.g. A4)
Homography, guided corner selection, and admin review
Visual previews and image-based recommendations
Review queues that combine CV with operator confirmation
AI-Assisted Commerce & Personalization
We help commerce workflows use AI for personalization, guided product selection, customer intake, content generation, and operational automation.
Photo and preference-based customer intake
Personalized product guidance and shortlists
Checkout, logistics, and IOSS/VAT handling
Customer support and operational automation workflows
Technical Lab & AI-Ready Signal Workflows
We support technical lab concepts where RF, SDR, signal data, dashboards, and AI-ready analysis workflows can be combined for training, research, and operator support.
RF/SDR training infrastructure and hands-on workflows
Spectrum capture, labelling, and event datasets
AI-assisted classification and anomaly detection
Operator-assistance dashboards over lab signals
Custom Integrations
We connect AI systems and backends to the tools real teams use — CRMs, ERPs, email, WhatsApp, partner portals, internal databases — through clean, documented contracts.
CRM and ERP integrations with typed contracts
Email, WhatsApp, and partner portal connectors
Database adapters and event-driven workflows
Authentication, retries, audit logs, and observability
From AI demos to production systems
The reliable layer beneath every useful AI agent
Many AI initiatives stop at prototypes. Inovativi focuses on the backend layer that makes AI useful in production: integrations, APIs, execution workflows, data-platform connectivity, deployment automation, and operational reliability.
Enterprise AI Integration Layer
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
Cloud / Data Platforms / Enterprise Systems
AWS, Azure, GCP, Databricks, ERP, CRM, GIS
Monitoring / Audit Logs / Error Handling
Observability and reliability
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.
Why Inovativi
Backend and AI workflow design in one accountable team
We combine backend engineering, retrieval design, evaluation, and deterministic workflow orchestration. The team that designs the architecture is the team that ships the production system.
Nearshore from Kosovo
A Kosovo-based engineering company in a European time zone, with a strong cost-to-quality ratio for DACH, Swiss, and EU clients.
Integration-first mindset
We treat backend, integration, and execution reliability as the core deliverable, not an afterthought to a model.
Engineers or small teams
Embed a single strong engineer into your team, or stand up a small delivery pod with backend, data, DevOps, and AI integration skills.
DACH-ready delivery
English-fluent delivery, remote-first collaboration, and on-site workshops when an engagement calls for them.
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.
Typical roles
Backend AI EngineerPython Backend EngineerAzure Integration EngineerDatabricks EngineerAI Platform EngineerDevOps / Terraform EngineerData / AI Integration Engineer
Representative engagements, from exposing tools to agents through to wrapping legacy systems with APIs and observability.
Connect an AI agent to internal enterprise APIs.
Build an MCP server exposing business tools to agents.
Create a Python backend service that triggers Databricks jobs.
Build an event-driven execution layer for agent actions.
Integrate Azure Service Bus with AI workflow services.
Build an API layer around GIS or valuation systems.
Add logging, retries, audit trails, and monitoring to AI-driven actions.
Modernize legacy workflows by wrapping them with APIs and AI-assisted interfaces.
FAQ
Questions clients ask first
Straight answers on scope, engagement models, geography, and the technologies behind the work.
What is AI Backend & Integration Engineering?
It is the backend engineering work required to connect AI platforms and agents to real business systems through APIs, execution workflows, cloud services, data platforms, and secure integrations.
Is this the same as chatbot development?
No. Chatbots mainly answer questions. AI backend integration focuses on production systems where agents can call tools, trigger workflows, retrieve data, and execute controlled actions.
What is MCP?
MCP is a protocol for exposing tools, data sources, and workflows to AI applications and agents. We use MCP-style integrations to help AI systems interact safely with enterprise systems.
Can Inovativi provide individual engineers?
Yes. We can provide individual backend, AI integration, Azure, Databricks, DevOps, and data engineers.
Can Inovativi provide a small delivery team?
Yes. We can provide a nearshore pod combining backend, data, DevOps, and AI integration skills.
Which markets do you serve?
We primarily support DACH, Swiss, EU, and UK clients.
Which technologies do you work with?
Python, FastAPI, REST, OpenAPI, Azure, Databricks, PySpark, Delta Lake, MLflow, MCP, Docker, Terraform, CI/CD, PostgreSQL, queues, monitoring, and observability.
Do you work remotely?
Yes. We work remote-first and can support on-site workshops when needed.
Next step
Bring AI into your documents, data, and workflows
Tell us about your documents, data sources, or operational workflow. We will respond with an architecture path and a realistic plan from proof of confidence to production. Reach us at info@inovativi.com.