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.
AI Backend, Integration & Modernization Studio
From AI Ambition to Working Production Systems
Inovativi builds the backend, integration, retrieval, approval, and modernization layers that make LLM-driven workflows reliable in production — deterministic orchestration, custom evaluation gates, and enterprise-grade security wrapped around every model call.
Senior technical ownership and direct engineering accountability for VC-backed startup CTOs, enterprise transformation leaders, and tier-1 delivery partners across the US, UK, and DACH.
MCP Tool Gateways · PLAN → EXECUTE → COMPOSE Orchestration · LLM Eval Harnesses · Tool-Level RBAC · Human-in-the-Loop Approvals · Durable Audit Logs · Shadow-Core Modernization · GDPR / SCC Ready
Portfolio-backed delivery across production RAG, MCP tool gateways, deterministic PLAN → EXECUTE → COMPOSE orchestration, LLM evaluation harnesses, and legacy modernization — including public-finance and tax-administration shadow-core rebuilds.
Production RAG and retrieval over regulated enterprise data
MCP tool gateways with policy-controlled, audited tool calls
Deterministic orchestration with LLM eval gates and human approval
Legacy modernization via governed shadow-core architectures
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.
Production RAG & Hybrid Search
Retrieval over messy, regulated enterprise data — PDFs, contracts, tenders, policies, emails, reports, tables, and internal knowledge bases. Hybrid search (BM25 + vectors), reranking, metadata filters, source citations, factual groundedness, 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 Orchestration Layers
Controlled AI workflows split into deterministic PLAN → EXECUTE → COMPOSE sub-workflows with defined states, business rules, approval steps, and audit logs — LangGraph-style state machines, conditional routing, idempotent 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.
LLM Evaluation Suites & Quality Gates
Custom eval harnesses, prompt regression testing, and automated quality gates in CI. Factual groundedness, context recall, answer relevance, hallucination checks, prompt-injection controls, and sensitive-data protection — measured before anything ships.
Legacy Modernization & Shadow-Core Architectures
Modernize existing systems without a risky big-bang replacement — enterprise shadow-core architectures, Oracle/PostgreSQL synchronization, data migration, idempotent runtimes, comparison engines, and read-only adapters. 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.
Flagship engagements
Three ways to engage — from scoping to production
Pick the entry point that matches where you are: define the build, embed senior engineers in your stack, or modernize the systems underneath.
2–4 weeks · fixed scope
AI Implementation Sprint
A targeted engagement to define enterprise use cases, architecture boundaries, retrieval and evaluation strategy, and a concrete technical roadmap — the fixed-scope step that turns an LLM ambition into an accountable build plan.
Embedded · monthly
Forward-Deployed AI Engineering
Hands-on delivery inside your production stack — production LLM applications, secure API integration layers, Model Context Protocol (MCP) servers, and deterministic data orchestration built and hardened alongside your team.
Phased · quarterly tranches
Modernization & Integration Tranche
Phased architectural replacement of brittle operational workflows with governed services, auditable data connectors, and legacy shadow-core architectures — proven on SIGTAS, public-finance, CRM, and HR system integrations.
- Step 1
AI Implementation Sprint
Define use cases, architecture, retrieval & eval strategy
- Step 2
Forward-Deployed Engineering
Build MCP servers, integrations, and orchestration in your stack
- Step 3
Modernization Tranche
Replace brittle workflows with governed, auditable services
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
Move Your LLM Workflow From Ambition to Production
Whether you need production RAG, an MCP tool gateway, deterministic orchestration with evaluation gates, or a governed legacy modernization, Inovativi can design and build the production-ready backend underneath it.