AI Backend & Integration Engineering
Inovativi builds AI backends, RAG systems, MCP/OpenAPI tool gateways, agent workflows, and integrations that connect enterprise data, legacy systems, and human operations.
The controlled execution layer that lets AI work safely with real business systems, documents, APIs, databases, and approvals.
Senior technical ownership and direct engineering accountability for enterprise teams across Europe and selectively in the US.
Python · FastAPI · MCP / OpenAPI Gateways · PostgreSQL · pgvector · Qdrant · Tool-level RBAC · Audit Trails · Evaluation Pipelines
The production gap
Models can reason. Demos can impress. What they cannot do on their own is act safely inside a real business — with permissions, validation, audit trails, and operational reliability. That is where most AI initiatives stall, and that is where Inovativi works.
How AI reaches production safely
The same five steps run behind every Inovativi system: a real business system is exposed through a secure gateway, AI works only inside that controlled surface, sensitive actions pass a human approval gate, and every step is recorded.
ERP · CRM · database · portal · documents
Secure tools · permissions · validation
RAG · agents · structured actions
Review · approve · escalate
Traceability · monitoring · compliance
What we build
Enterprise RAG, document intelligence, deterministic workflows, AI backend engineering, evaluation, computer vision, AI-assisted commerce, technical lab signals, and legacy modernization — delivered as one connected execution layer, not separate service lines.
Retrieval over messy enterprise data — PDFs, contracts, tenders, policies, emails, reports, tables, and internal knowledge bases. Hybrid search (BM25 + vectors), reranking, metadata filters, source citations, and permission-aware results.
Pipelines that convert PDFs, scans, tables, legal judgments, procurement notices, invoices, and forms into reliable structured data — with validation rules, JSON outputs, and human review loops.
Controlled AI workflows with defined states, business rules, approval steps, and audit logs — built with LangGraph-style state machines, conditional routing, tool execution, and human-in-the-loop approvals.
Production APIs, databases, queues, storage, authentication, monitoring, cost controls, and integrations — FastAPI, PostgreSQL, Qdrant/pgvector, Redis, Docker, background jobs, observability, and secure deployment.
Evaluation pipelines, regression tests, guardrails, audit logs, and quality dashboards. Faithfulness, context recall, answer relevance, hallucination checks, prompt injection controls, and sensitive data protection.
Add AI capabilities around existing systems without a risky big-bang replacement — API layers, shadow systems, comparison engines, read-only adapters, data synchronization, and gradual migration support.
Start with a productized first engagement
2–4 weeks · fixed scope
Our fixed-scope AI integration sprint
Expose one internal system safely to AI agents. We map a selected workflow or legacy system, design secure AI-callable tools, implement an MCP / OpenAPI / FastAPI gateway, and add authentication, permissions, validation, logging, and audit trails so AI can interact with real business systems under control.
Governance, safety & cost control
Production AI systems need more than model access. They need controlled execution. We design AI workflows with permission-aware retrieval, tool-level RBAC, prompt-injection safeguards, PII handling, audit trails, approval gates, and deterministic validation before sensitive actions reach production systems.
We build AI workflows with cost visibility from the start: token budgets, loop limits, model routing, semantic caching, usage monitoring, and circuit breakers that prevent uncontrolled agent execution.
Data, documents & retrieval
We connect AI workflows to enterprise data platforms, warehouses, databases, APIs, document stores, and legacy systems — including PostgreSQL, Oracle, SQL Server, Databricks / Delta Lake, SharePoint, CRMs, ERPs, and internal portals.
For complex document environments, we combine vector search, keyword search, metadata filters, reranking, structured extraction, and — where useful — graph-based relationships. We treat GraphRAG as one advanced pattern in the toolbox, not a default.
Portfolio pattern
Inovativi's work spans enterprise AI backends, document intelligence, computer vision, AI-assisted commerce, procurement workflows, technical lab infrastructure, and legacy modernization. The common pattern is practical: connect AI to real data, real workflows, real users, and real operational constraints.
Featured portfolio
A broader cross-section of our work — internal products, reference architectures, prototypes, and technical lab initiatives across AI backend, document intelligence, computer vision, AI-assisted commerce, workflow automation, and legacy modernization.
A procurement intelligence workflow platform for Kosovo public procurement opportunities. TenderScope ingests tender notices, applies deterministic ranking, and uses AI for summaries, explanations, readiness checks, alerts, and operator decision support.
Read case studyInternal Product / Legal AI PlatformA legal document intelligence platform focused on retrieval, structured extraction, and analysis of legal judgments and documents. Juristi demonstrates AI-assisted legal search, document parsing, section extraction, party identification, and standardized JSON output workflows.
Read case studyComputer Vision / Quotation Workflow PrototypeA window and door quotation workflow that helps customers submit photos with a known-size reference object, such as an A4 sheet, and uses computer vision, homography, guided corner selection, and admin review to support preliminary dimension estimation and quote preparation.
Read case studyAI-Assisted Commerce / Visual Recommendation PrototypeAn eyewear recommendation and visual preview platform that helps users discover frame styles based on a submitted face photo. Framelo demonstrates image-based intake, AI-assisted style recommendations, visual previews, product linking, and guided shopping flows.
Read case studyInternal Platform / Reference ArchitectureAn AI operations and workflow platform concept for managing SOPs, tasks, quality checks, customer communications, and human-in-the-loop execution. Operanto demonstrates how AI agents, structured workflows, and operator dashboards can support real business operations.
Read case studyTechnical Lab / Training InfrastructureA technical training and lab infrastructure initiative focused on RF measurement, software-defined radio, spectrum awareness, and applied signal analysis. The center supports hands-on RF/SDR training and provides a foundation for AI-assisted workflows such as signal classification, anomaly detection, spectrum-event labeling, and operator-assistance dashboards.
Read case studyProduct / E-commerce AI ExperienceAn AI-assisted personalized e-commerce workflow for custom press-on nails. Nagelista combines customer photo intake, personalization logic, product guidance, checkout, logistics, IOSS/VAT handling, and customer support workflows.
Read case studyConcept Demonstration / Legacy Modernization ArchitectureA phased modernization approach for legacy government and enterprise systems. The architecture wraps legacy databases and applications with modern APIs, shadow services, comparison layers, audit logs, and AI-ready integration points, allowing gradual replacement without risky big-bang migration.
Read case studyPortfolio items include internal products, reference architectures, technical lab initiatives, and concept demonstrations. They are labelled transparently to show the type and maturity of each project — not implying confidential client work.
How we work
A bounded, evaluation-driven path from a first conversation to a system your operators rely on every day.
We map data sources, workflows, users, risks, and integration points.
We design a bounded architecture and validate it with real documents, real queries, and measurable evaluation criteria.
We implement APIs, retrieval pipelines, workflow logic, databases, and integrations with existing systems.
We test retrieval quality, hallucination risk, permissions, latency, cost, and operational reliability.
We support deployment, monitoring, iteration, and controlled expansion into additional workflows.
Technology
We choose tools based on reliability, integration needs, security, and maintainability — not hype. Our preferred stack supports enterprise RAG, document intelligence, workflow orchestration, and backend integrations.
Why Inovativi
What buyers consistently get from working with a small, senior-led team that ships production AI systems.
We build AI that runs every day inside real operations — with monitoring, retries, audit trails, and the boring engineering work that demos skip.
We understand enterprise documents and legacy systems as they actually exist — PDFs without structure, tables in scans, applications without APIs.
We combine backend engineering with AI workflow design, so the same team owns retrieval, orchestration, integrations, and the APIs underneath.
We design for evaluation, approval flows, and clear audit logs. AI proposes, humans confirm where it matters, and every step is recorded.
A bounded first engagement validates the architecture on real data and real queries, with measurable evaluation criteria before scope expands.
Hands-on technical leadership stays close to architecture and implementation throughout the engagement — no hand-offs to a sales layer.
Next step
Whether you need enterprise RAG, document extraction, workflow automation, computer vision intake, or legacy system AI integration, Inovativi can help design and build a production-ready backend.
