Your systems stay the source of truth
Finance, HR, ERP, CRM, and legacy apps keep running as the system of record. AI reads and writes through governed interfaces — never around them.
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.
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
Featured reference architecture
Nervora — a secure MCP gateway for enterprise AI tool execution
Nervora provides secure, permission-controlled access to business tools and APIs. The controlled AI workflow layer sits above it and manages durable workflow state, routing, step execution, human approval, recovery, real-time synchronization, and operational visibility.
MCPFastAPIRBACOIDCAzure Service BusDatabricksOpenTelemetryPostgreSQL
View the Nervora reference architectureControlled AI Workflow Platform
Durable state, routing, approvals, recovery, real-time sync, operational visibility
Nervora / MCP / OpenAPI Gateway
Authenticated, permission-controlled, auditable tool access
CRM · ERP · Databases · Documents · APIs
Your real business systems
How we put AI into production
One controlled pattern, from your systems to a governed AI workflow
AI only creates value when it can act on real systems — safely. We wrap finance, HR, ERP, CRM, and legacy apps in a governed gateway, run controlled AI workflows on top, surface them through an operator-facing layer, and keep every action audited and reviewable.
Fiscora · Personora · Existing systems
Finance · HR · ERP · CRM · Legacy apps
Nervora
Identity · RBAC · Tools · Approval · Redaction · Idempotency · Audit
Controlled AI workflows
RAG · Agents · Rules · Structured action
Operanto
Inbox · Workflow · Quote · CRM · Human-in-the-loop
Audit · Monitoring · Review
Every action logged, traceable, and reviewable
Governed access through Nervora
Every tool call is authenticated and permission-checked: identity, RBAC, approval, redaction, idempotency, and a full audit trail on each action.
Controlled AI workflows, not free-roaming agents
RAG, agents, and rules run inside durable, inspectable workflows that take structured actions — with a human in the loop wherever it matters.
Operated and overseen
Operanto turns the workflow into a working surface — inbox, quoting, CRM, approvals — while monitoring and review keep the whole system accountable.
Selected Work
Systems That Prove the Pattern
Across AI operations, institutional modernization, and computer vision, each project demonstrates the same pattern: connect real data, real users, real rules, and real operational constraints.
AI Operations & Workflow Platform
Operanto — 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 StudyVertical AI CommerceKlarwerk — Photo-to-Quote Window Sales & Orchestration
Photo-assisted window qualification, deterministic pricing and controlled hand-offs from customer inquiry to professional survey and fulfilment.
Pilot-Stage InitiativePhoto-to-QuoteDeterministic PricingNext.jsPostgreSQL
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.
Sovereign Multi-Model AI Infrastructure
Vendor-independent AI systems with private deployment, model routing, audit trails, fallback paths, and human control — open-weight, EU/Swiss hosting, premium escalation, and provider-blackout continuity.
- 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
Vendor independence
AI infrastructure without single-vendor lock-in
Inovativi builds vendor-independent AI systems that combine open-weight models, European infrastructure, premium frontier APIs, deterministic business logic, and human oversight.
The result is an AI architecture that can adapt when providers, prices, regulations, or operational requirements change.
Open-weight models
For control, portability, and cost efficiency.
European infrastructure
For sovereignty and procurement flexibility.
Premium frontier APIs
For advanced reasoning when appropriate.
Deterministic logic & human fallback
Approvals and deterministic software fallback for operational continuity.
No single model provider should be able to shut down your business.
Productized engagements
Start with one controlled production workflow
Choose the smallest engagement that proves value inside your real systems.
2–4 weeks
Enterprise MCP / OpenAPI Gateway Sprint
Safely expose selected business capabilities to AI agents through authenticated, permission-controlled, and auditable tools.
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.
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.