Operational Excellence
What we sell,
we practice
first.
Most consultancies sell a methodology they don't apply internally. Izertis develops its own AI tools, processes and standards and uses them in real production before bringing them to the client.
Architecture makes the difference between a 37% and an 80% success rate in AI projects.
Companies with a formal AI strategy achieve twice the success. Those that also invest in architecture obtain $3.70 for every $1 invested. That's what we build.
78%
of companies already deploy AI in production
Deloitte State of AI 2026
70-85%
of AI projects fail
McKinsey Global Survey 2025
42%
of executives say AI is creating tensions within their company
Deloitte State of AI 2026
CONTEXT
AI has stopped being an experiment. It's now critical business infrastructure.
78% of companies already deploy AI systems in production. But 70–85% of projects fail. The difference isn't the technology — it's the architecture and the method.
Talk to an expertTWO POSSIBLE PATHS
37%
Without architecture
→ Data and model silos without governance
→ Lock-in with a single cloud provider
→ Inability to audit AI decisions
→ Opaque costs that erode the margin
→ Every project starts from scratch
80%
With architecture
→ End-to-end governance and lineage
→ Vendor independence by design
→ Auditable traceability from the first commit
→ Native FinOps with alerts and rightsizing
→ Reuse from the Corporate Marketplace
AI ARCHITECTURE: THE FRAMEWORK THAT HOLDS EVERYTHING TOGETHER
10 dimensions for AI that reaches production and stays there
72% of executives admit their AI applications are developed in silos. These 10 dimensions are the antidote: a corporate, federated and agnostic framework to design, build and operate AI and Data solutions in a coherent, secure and scalable way.
Infrastructure
Deploy in cloud, on-premise or edge with the same architecture.
→ Hybrid and multicloud platforms (AKS/K8s, VMs, On-Prem)
→ Standardized input/output ports
→ Blue/green and canary patterns with integrated observability
Engineering
Business logic evolves without depending on external technologies.
→ Microservices, APIs and shared libraries
→ Maximum portability with technology adapters
→ Archetypes and scaffolding in the Corporate Marketplace
Networking
Segmentation, traffic control and connectivity without coupling business logic.
→ DMZ, subnets and federated Zero Trust
→ WAF/API Gateway as a standardized boundary
→ Abstract, interchangeable connectivity
Security
Data and model protection by design. In synergy with Cybersecurity.
→ Centralized OIDC/OAuth2 controls
→ Protection against prompt injection, data poisoning
→ Zero-trust and anomaly detection
Data & AI
Complete, governed management of the data and model lifecycle.
→ Lakehouse integrated with feature store and model store
→ Standardized batch/stream ingestion
→ SQL/vector/object data serving with lineage
xOps
DevOps · MLOps · LLMOps — full lifecycle decoupled from the tools.
→ CI/CD, training, validation and deployment
→ Model Registry Port and Observability Port
→ GitHub Actions, MLflow, Terraform, Docker adapters
Regulation and Ethics
Regulatory compliance built in from the design stage.
→ Auditable risk assessment and traceability
→ Risk Assessment Port, AIA Intake Port
→ Audit Evidence Port and Compliance Report Port
Quality
Only the assets that pass the quality contracts progress between environments.
→ Quality Contract Port per business domain
→ Quality gates in CI/CD with degradation alerts
→ Great Expectations, Evidently AI, dbt tests
Governance
Control and visibility over all AI & Data assets.
→ Management of policies, roles and responsibilities
→ Corporate catalog and end-to-end lineage
→ Apache Atlas, OpenMetadata, Unity Catalog, Purview
FinOps, Cost as a native dimension
Economic visibility and optimization from the design stage. Without native FinOps, AI eats into the margin. Tracking, budgeting, alerting and cost governance for infrastructure, CPU/GPU compute, storage, APIs and licenses.
WHAT OPERATIONAL EXCELLENCE IS
Three initiatives that work as laboratory, standard and culture, so the client can already verify that these are the very same practices applied in their projects.
iNNOLAB: From idea to proof-of-concept with AI
CROSS-CUTTING STANDARDS
AI Center of Excellence
Best practices, continuous training and homogeneous standards across the entire company. The guarantor that what is deployed in banking meets the same standard as in pharma or the public sector.
AI-ASSISTED ENGINEERING
Spec-Driven Development Lab
AI-assisted development with specifications at the core of the process. The client doesn't hire a team that "also uses AI" — they hire a team whose process is built on AI.
CAPABILITY ANTICIPATION
iNNOLAB
A continuous co-creation space. From idea to proof-of-concept with AI. What comes out of iNNOLAB feeds the Go To Market catalog and the Center of Excellence standards.
$3.70
Return for every $1 invested in AI architecture.
Companies with a formal strategy achieve twice the success.
THREE FORCES THAT WON'T WAIT
AI is industrializing at breakneck speed
The market went from $24B in 2024 to a projected $150–200B in 2030 (+30% CAGR). The key to moving from PoCs to scalable AI in production is architecture.
Regulation now has a date and penalties
The AI Act is in force. High-risk systems must comply by 2 August 2026. Fines of up to €35M or 7% of global turnover.
Costs spiral without visibility
Without native FinOps, AI can generate exorbitant bills due to the unpredictable consumption of resources and tokens.
WHAT OPERATIONAL EXCELLENCE IS
AI Center of Excellence
Best practices, continuous training and homogeneous standards across the entire company.
Best-practices guide
Living documentation of AI engineering, security and governance standards, updated with every project.
Tool and model evaluation
They search, filter, transform and synthesize large volumes of data. They generate insights and prepare information for other systems.
Internal role-based training
Training programs differentiated by role: engineers, consultants, business profiles and leadership.
Homogeneous quality standards
What is deployed in banking meets the same standard as in pharma or the public sector. No exceptions.
Why does it matter to the client?
→ The client doesn't get the "most available" team but the team that applies the very standard their sector requires.
→ Izertis's internal standards become, directly, the standards of the client's project.
→ Continuous internal training translates into teams always up to date with the state of the art in AI.
→ Guaranteed consistency across all projects, regardless of the client's sector or size.
AI-ASSISTED ENGINEERING
Spec-Driven Development Lab
AI-assisted development with specifications at the core of the engineering process.
Specifications — requirements, API contracts, acceptance criteria — are the starting point of the development cycle, not documentation written afterwards. From that foundation, AI assists in generating code, tests and validations.
What the client receives
→ Fewer defects in production thanks to automated validation cycles from the start.
→ Shorter cycles thanks to AI assistance in generating code and tests.
→ Full traceability from requirement to deployment — auditable at any time.
→ They don't hire a team that "also uses AI" — they hire a team whose process is built on AI.
FROM REQUIREMENT TO DEPLOYMENT
Specification as the starting point
Requirements, API contracts and acceptance criteria defined before the first line of code.
AI-assisted generation
AI generates code, tests and validations from the specifications. Not from scratch.
Automatic validation
Automated quality gates check that the code meets the defined acceptance criteria.
Deployment with full traceability
Every decision from requirement to production is recorded and auditable.
Iteration and continuous improvement
Learnings are incorporated into the Center of Excellence and the Corporate Marketplace.
iNNOLAB
iNNOLAB: From idea to proof-of-concept with AI
A continuous co-creation space that connects people, ideas and technology. It's not an event or a closed program — it's a permanent infrastructure. From "innovating for the client" to "innovating with the client".
4-8
weeks of total program duration
8-12
people in the mixed squad
MVP
+ Scaling Roadmap as a guaranteed outcome
FROM CHALLENGE TO VALIDATED PROTOTYPE IN 4 SPRINTS
SPRINT 1
Discovery
→ User research
→ Problem mapping
→ Hypothesis definition
→ HYPOTHESIS DEFINED
SPRINT 2
Ideation
→ Solution generation
→ Prioritization
→ Conceptual design
→ CONCEPTUAL DESIGN
SPRINT 3
Prototyping
→ MVP development
→ Technology integration
→ First validations
→ FIRST MVP
SPRINT 4
Validation
→ Testing with users
→ Rapid iteration
→ Scaling roadmap
→ MVP + ROADMAP
