2–4 weeks
Enterprise MCP / OpenAPI Gateway Sprint
Safely expose selected business capabilities to AI agents through authenticated, permission-controlled, and auditable tools.
Enterprise AI Execution Layer
Enterprise AI Execution Layer for Regulated Workflows
We build the secure backend, MCP/OpenAPI tool gateways, Databricks integrations, audit trails, and approval flows that let AI agents move from pilot to production.
For CTOs, AI platform teams, German/Swiss recruiters, and boutique AI consultancies that need senior nearshore engineers for production AI infrastructure.
Senior technical ownership and direct engineering accountability for enterprise teams across Europe and selectively in the US.
MCP/OpenAPI Gateways · Databricks Workflows · Azure Integration · Tool-Level RBAC · Audit Trails · Human Approval Gates · CET-Aligned Delivery · NDA/DPA/SCC Ready
Portfolio-backed delivery across institutional modernization, AI-assisted document workflows, computer vision intake, procurement intelligence, and technical lab systems.
Production AI backends for enterprise documents, workflows, and integrations
Enterprise RAG, document intelligence, and evaluation pipelines
Deterministic AI workflows with audit trails and human approval
Secure cloud, hybrid, or on-prem deployment
The production gap
Most AI prototypes never reach production
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.
- Demos do not survive contact with real data, real users, and real systems
- Agents need governed access to tools, not raw access to production
- Backend reliability, retries, and audit trails are not optional in production
- Cloud, hybrid, and on-prem deployment realities shape what is actually buildable
How AI reaches production safely
Business System → Gateway → AI Workflow → Approval → Audit
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. Nervora, our internal MCP gateway reference architecture, demonstrates these patterns with tool-level RBAC, PII redaction, async execution, idempotency, and audit logs.
- Step 01
Business System
ERP · CRM · database · portal · documents
- Step 02
MCP / OpenAPI Gateway
Secure tools · permissions · validation
- Step 03
AI Workflow
RAG · agents · structured actions
- Step 04
Human Approval
Review · approve · escalate
- Step 05
Audit Log
Traceability · monitoring · compliance
Selected Work
Systems That Prove the Pattern
Inovativi's work spans institutional modernization, AI-assisted workflows, document intelligence, computer vision, technical lab systems, and commerce operations. Each project demonstrates the same pattern: connect real data, real users, real rules, and real operational constraints.
Internal R&D Reference Architecture
Nervora — Secure MCP Gateway for Enterprise AI Tool Execution
Nervora demonstrates how enterprise AI agents can safely access business systems through a governed MCP gateway with OIDC authentication, tool-level RBAC, PII redaction, dry-run approvals, async workflow execution, audit logs, idempotency, and OpenTelemetry tracing.
MCPFastAPIRBACOIDCAzure Service BusDatabricksOpenTelemetryPostgreSQLEnterprise AI Governance
View Reference ArchitectureAI Operations & Workflow PlatformOperanto — AI Operating Layer for Conversation-Driven Businesses
A multi-tenant AI operating layer that turns customer conversations into commercial operations: a unified multi-channel inbox and AI assistant over a config-driven workflow engine — leads, quoting, human approvals, scheduling, CRM integration, and document AI.
Next.jsWorkflow EngineMulti-TenantHuman ApprovalPostgreSQLProvider-Agnostic AI
View Case StudyInstitutional Finance ModernizationFiscora — Budget & Financial Workflow Modernization
A modernized institutional finance platform covering hierarchical budget planning, allocations, spending, invoices, payments, formula traces, reporting, and audit-ready financial workflows.
Public FinanceBudget Execution.NETNext.jsPostgreSQLAudit Logs
View Case StudyAI-Ready HR PlatformPersonora — AI-Ready HR Platform, Built End to End
A production-grade HR platform covering employee lifecycle, recruitment, employment history, leave, contracts, timesheets, appraisals, documents and audited sensitive access — with a governed multi-provider AI layer (Claude / OpenAI / DeepSeek).
HR Systems.NET 10Next.jsPostgreSQLMulti-Provider AIAudit Logs
View Case StudyProcurement IntelligenceTenderScope — AI-Assisted Tender Intelligence
A procurement intelligence workflow for tender ingestion, ranking, summaries, readiness checks, alerts, and operator decision support.
Public ProcurementDocument IntelligenceRankingAlerts
View Case StudyComputer Vision / Quotation WorkflowKlarwerk — AI-Assisted Window Measurement & Quotation
A photo-based window and door quotation workflow using A4 reference calibration, guided corner selection, homography, review screens, and structured quote preparation.
Computer VisionHomographyNext.jsPrismaPostgreSQL
View 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.
What we build
Connected capabilities for production AI systems
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.
Enterprise RAG & Hybrid Search
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.
Document Intelligence & Structured Extraction
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.
Deterministic AI Workflow Orchestration
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.
AI Backend Engineering
Production APIs, databases, queues, storage, authentication, monitoring, cost controls, and integrations — FastAPI, PostgreSQL, Qdrant/pgvector, Redis, Docker, background jobs, observability, and secure deployment.
Evaluation, Guardrails & Auditability
Evaluation pipelines, regression tests, guardrails, audit logs, and quality dashboards. Faithfulness, context recall, answer relevance, hallucination checks, prompt injection controls, and sensitive data protection.
Legacy System AI Modernization
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. Includes full domain-driven rebuilds of institutional finance and HR systems from legacy ASP.NET into API-first platforms.
Productized engagements
Start with one controlled production workflow
Choose the smallest engagement that proves value inside your real systems.
4–8 weeks
Controlled AI Workflow Sprint
Implement one complete AI-assisted business process with durable state, deterministic rules, human approval, failure recovery, and an operational interface.
- Step 1
Gateway Sprint
Connect AI safely to business systems
- Step 2
Controlled Workflow Sprint
Execute one real operational process
- Step 3
Production Hardening
Scale, observe, test, and operate it reliably
Governance, safety & cost control
Controlled execution, not raw model access
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.
Safety & governance
- Tool-level RBAC and permission-aware retrieval
- Prompt-injection mitigation and input sanitization
- PII redaction and anonymization at the boundary
- Approval gates before write actions
- Audit logs for every tool call and agent action
- Human-in-the-loop review for sensitive decisions
- Deterministic validation before production writes
- Fallback handling and error recovery
Cost control & token economics
- Token budgets per request, per workflow, per tenant
- Model routing — small models where they fit, large where they earn it
- Semantic caching to avoid repeated work
- Loop limits and circuit breakers on agent execution
- Usage monitoring and cost dashboards or logs
- Retry policies and dead-letter handling for failed jobs
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
Connect AI to the data your business already runs on
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.
Document & data pipelines
- Structured extraction with schemas and validation
- Data normalization across heterogeneous sources
- Document parsing, OCR, and table extraction
- Metadata enrichment and lineage
- Ingestion, indexing, and incremental refresh
- Audit-friendly operational data flows
Retrieval that holds up in production
- Hybrid search — vector + keyword with metadata filters
- Reranking and source-grounded answers with citations
- Permission-aware retrieval and access control
- Structured extraction alongside free-text answers
- Graph-based relationships where they add real value
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.
Institutional modernization
Institutional Modernization, Proven Through Rebuilt Systems
We modernize legacy public-sector and institutional software into secure, API-first, audit-ready platforms. Fiscora and Personora demonstrate how complex finance and HR workflows can be rebuilt from legacy ASP.NET systems into modern platforms with preserved business logic, automated tests, audit logs, and AI-ready extension points.
Modernization Case Study
Fiscora
Institutional Budget & Financial Workflow Modernization
A modernized budget and finance platform covering hierarchical budget planning, execution tracking, allocations, invoices, payments, payroll-related calculations, formula traces, reporting, and audit-ready financial workflows.
Public FinanceBudget Execution.NETNext.jsPostgreSQLAudit Logs
View Fiscora Case StudyModernization Case StudyPersonora
AI-Ready Institutional HR Platform
A production-grade, AI-assisted HR platform covering employee lifecycle, recruitment, employment history, leave, contracts, timesheets, appraisals, documents, reports and audited sensitive access — with a governed multi-provider AI layer (Claude / OpenAI / DeepSeek).
HR Systems.NET 10Next.jsPostgreSQLMulti-Provider AIAudit Logs
View Personora Case StudyThe modernization pattern
- Legacy system analysis
- Domain model extraction
- Business-rule preservation
- Security remediation
- API-first rebuild
- Automated workflow tests
- Audit logs
- AI-ready extension points
How we work
From Prototype to Production
A bounded, evaluation-driven path from a first conversation to a system your operators rely on every day.
- 01
Discovery & System Mapping
We map data sources, workflows, users, risks, and integration points.
- 02
Architecture & Proof of Confidence
We design a bounded architecture and validate it with real documents, real queries, and measurable evaluation criteria.
- 03
Build & Integrate
We implement APIs, retrieval pipelines, workflow logic, databases, and integrations with existing systems.
- 04
Evaluate & Harden
We test retrieval quality, hallucination risk, permissions, latency, cost, and operational reliability.
- 05
Deploy & Improve
We support deployment, monitoring, iteration, and controlled expansion into additional workflows.
Technology
Production-Oriented AI Stack
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.
- Python
- FastAPI
- PostgreSQL
- pgvector
- Qdrant
- Redis
- Docker
- Next.js
- TypeScript
- LangGraph
- OpenAI / Anthropic / local LLMs
- Docling / LlamaParse
- RAGAS / TruLens
- MCP
- REST APIs
- Webhook integrations
- Observability tools
Why Inovativi
Why Teams Work With Inovativi
What buyers consistently get: senior technical ownership, hands-on engineering leadership, and no hand-off between sales and implementation.
Production systems, not demos
We build AI that runs every day inside real operations — with monitoring, retries, audit trails, and the boring engineering work that demos skip.
Messy data, messy systems
We understand enterprise documents and legacy systems as they actually exist — PDFs without structure, tables in scans, applications without APIs.
Backend + AI in one team
We combine backend engineering with AI workflow design, so the same team owns retrieval, orchestration, integrations, and the APIs underneath.
Auditability and human control
We design for evaluation, approval flows, and clear audit logs. AI proposes, humans confirm where it matters, and every step is recorded.
Start with a proof of confidence
A bounded first engagement validates the architecture on real data and real queries, with measurable evaluation criteria before scope expands.
Technically accountable delivery
Hands-on technical leadership stays close to architecture and implementation throughout the engagement — no hand-offs to a sales layer.
Built for operational environments where reliability, traceability, and human control matter.
NDA / DPA / SCC-ready
CET-aligned delivery
Kosovo-based company serving EU, UK, Swiss, and DACH clients
Cloud, hybrid, or on-prem deployment
Audit logs and approval workflows by design
Production-oriented backend engineering
Next step
Build a Reliable AI System Around Your Documents, Data, or Workflow
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.