Production AI and retrieval
Grounded answers and structured extraction over messy enterprise material, evaluated before anyone relies on them.
- RAG
- Vector search
- Document intelligence
- Computer vision
- LLM evaluation
- Prompt regression tests
AI & Engineering
Engineering for systems that have to work in the real world.
Inovativi specializes in production systems where AI has to interact safely with enterprise data, business rules, humans, external systems, physical operations and regulated processes. This is the engineering underneath our ventures and our client work.
Senior-led · Client-owned repositories and environments · NDA / DPA / SCC support · Designed for regulated organizations
Where AI has to behave
Six things a model must interact with safely before it is useful
A demo answers questions. A production system reads permissioned data, follows business rules, waits for people, calls other systems, triggers physical work and leaves an audit trail. We build for the second case.
- 01
Enterprise data
Permission-aware retrieval over documents, databases and warehouses, with sources cited and quality evaluated.
- 02
Business rules
Deterministic validation, pricing, eligibility and policy checks decide what an AI proposal may become.
- 03
Humans
Approval gates, review queues and escalation paths where a decision is consequential.
- 04
External systems
ERP, CRM, payment, logistics and public APIs reached through allowlisted, idempotent, logged actions.
- 05
Physical operations
Production, fulfilment, field work and craftsmanship coordinated through structured order and state models.
- 06
Regulated processes
Identity, role-based access, audit logs, data residency and evaluation before release.
Capabilities
The stack, framed as infrastructure for business workflows
Frameworks are tools, not the product. We choose them for reliability, integration and maintainability, and keep the technical depth where it belongs: underneath the workflow.
Agentic workflows
Agents that act inside controlled surfaces: allowlisted tools, schema-validated inputs, approvals, retries and audit on every call.
- MCP
- OpenAPI tool gateways
- Approval gates
- Tool-level RBAC
- Idempotency
- Audit trails
Integration and APIs
The backend layer that connects AI to the systems a business already runs, with authentication and contracts that hold.
- Python
- TypeScript
- FastAPI
- Next.js
- PostgreSQL
- Authentication
- Webhooks
Data and modernization
Legacy systems wrapped, compared and replaced in tranches; data pipelines and governed workflows on modern platforms.
- Legacy modernization
- Data engineering
- Databricks
- Oracle / PostgreSQL sync
- Shadow-core architectures
Infrastructure and operations
Client-owned cloud, hybrid or on-prem deployment with observability and cost control designed in.
- Docker
- Kubernetes
- Azure / AWS / GCP
- Terraform
- OpenTelemetry
- Observability
Governance and resilience
Vendor-independent model access, evaluation gates and the controls that let AI run inside regulated organizations.
- Model routing
- Open-weight and frontier models
- Evaluation gates
- Data residency
- Security & delivery practices
Method
Probabilistic interpretation, deterministic control, human review
Across ventures and client systems the pattern is the same: AI interprets, business logic grounds, software executes, people review what matters, and outcomes feed back into the workflow.
- 01
Input
Photos, documents, listings, messages, measurements, preferences.
- 02
Interpretation
AI extracts meaning, intent, structure, and context.
- 03
Grounding
Business rules, databases, prices, deadlines, product catalogs, and constraints verify the output.
- 04
Workflow
The system creates quotes, recommendations, rankings, summaries, tasks, or action packs.
- 05
Human Review
Experts review uncertain or high-value cases.
- 06
Feedback Loop
Corrections and outcomes improve the system over time.
The value is not in the model alone. It is in the workflow, the grounding, and the accountable outcome around it.
Go deeper
The detailed engineering pages
Each page keeps its full technical depth. Start with the one closest to your problem.
AI Backend & Integration Engineering
Enterprise RAG, document intelligence, orchestration, evaluation and legacy modernization as production systems.
AI Backend & Integration EngineeringControlled AI Workflows
Durable, governed workflows with structured validation, human approval, reliable execution and audit trails.
Controlled AI WorkflowsProduction AI Pattern
The reference architecture from existing systems through a governed gateway to a reviewed action.
Production AI PatternAI Resilience
Vendor-independent, sovereign AI: open-weight, European and frontier models under one governed gateway.
AI ResilienceSecurity & Delivery Practices
The agreements we can sign and the controls we implement inside client environments.
Security & Delivery PracticesNearshore Engineering Teams
Senior engineers embedded in your team as a secondary delivery model.
Nearshore Engineering TeamsWhere to start
AI Implementation Sprint
A bounded, 2–4 week first engagement: one workflow, your systems and your data, senior engineers doing the work, and a roadmap you keep whether or not you continue.
Next step
Bring us the workflow that has to work
Tell us which documents, ERP, CRM, legacy application or physical process AI should operate inside. We will respond with a sprint scope, the agreements we can sign and the engineers who would do the work.