Applied AI · engineering first
AI applied to the business. Without operational risk.
When intelligence goes into production, governance and engineering have to arrive together. We design and operate AI, data and ML for environments where failing is not an option.
What enterprises buy
What mature enterprises buy when they "buy AI".
AI only becomes an advantage when it's operable.
That requires architecture, data, processes and governance: before the model, during the model and after the model.
- Strategy with prioritized use cases
- Data and integration
- Deploy and continuous operation (MLOps/LLMOps)
- Value and risk metrics
The fronts
Six fronts, and none of them is worth anything alone.
The order matters more than the list. Without reliable data, a model is an expensive guess. Without operations, a good model becomes an incident.
- 01
AI strategy that becomes a backlog, not a slide.
- Financial and operational impact
- Technical viability and data dependencies
- Risk, compliance and security
- Incremental plan: useful pilots → scale
- 02
Data: the foundation no one wants to do, but everyone needs.
- Quality and lineage
- Pipelines and observability
- Governance and access
- Catalog and semantics
- 03
Analytics isn't about looking at the past. It's about deciding the next step.
- Actionable metrics
- Alerts and anomaly detection
- Forecasts and scenarios
- Learning loops with the business
- 04
Machine Learning that holds up in production: versioned, monitored, auditable.
- Validation and testing
- Drift and performance monitoring
- Retraining when it makes sense
- Audit trail and explainability
- 05
GenAI and agents: real value, with clear limits.
- Support and service with context (RAG)
- Internal productivity and task automation
- Assisted generation with validation
- Intelligent search in corporate knowledge
- 06
AI does not live in a chat. It lives in the workflow.
- ERP/CRM
- Portals
- Service
- Compliance
MLOps / LLMOps
Machine Learning that holds up in production: versioned, monitored, auditable.
Useful ML is operable ML. We work with production practices:
- Model versioning
- Centralized feature store
- CI/CD pipelines
- Drift monitoring
- Performance alerts
- Audit logs
Data Foundation
Data: the foundation no one wants to do, but everyone needs.
- Data architecture + pipelines
- Quality checks + monitoring
- Data contracts / governance
- Unified semantic layer
- Privacy-by-design and access patterns
Squad Amplification
Squad amplification (when it makes sense).
In some scenarios, the biggest lever is accelerating teams:
- Internal copilots
- Routine automation
- Assisted generation with governance
- Reducing operational friction
Amplification is a capability, not the whole offer.
Real Experience
Experience built in real environments.
Without naming brands, here are delivery patterns we have already operated in complex contexts:
High-volume portal + observability
Challenge:
Spikes, SLAs, reputational risk
Approach:
Performance + monitoring + governance
Corporate knowledge with intelligent search
Challenge:
Scattered information and low efficiency
Approach:
RAG, curation, guardrails
Actionable analytics for operations
Challenge:
Dashboards without decision
Approach:
Metrics + alerts + action loops
Automation connected to systems
Challenge:
Manual processes and bottlenecks
Approach:
Events + integrations + automation
A model with no operations is an incident with a date on it.
The order matters more than the list. Without reliable data, a model is an expensive guess.
The decision
When AI is worth it, and when it is not.
Worth it when
- There is a decision repeated many times a day, and today it depends on someone looking
- The data behind that decision exists, even if scattered and dirty
- You can say what counts as right and what counts as wrong, and measure both afterwards
- Someone inside is answerable for the process that will change
Not worth it when
- The expectation is that the model will discover what the company wants, instead of executing what it already decided
- There is no data behind the decision, and the project would become data collection dressed as AI
- The result has to be explained line by line and there is no tolerance for any error at all
The useful question is not whether AI can. It is whether your operation has a decision worth automating, and who answers for it afterwards.
Governance and Security
Reliability is part of the product.
- Privacy and security by design
- Audit trails when necessary
- Access control and segregation
- Observability and operational SLAs
- Quality and continuous evaluation
This infrastructure is ours, not a slide.
- 108
- CloudFormation stacks in active operation
- 252
- Lambda functions sustained in production
- 170
- DynamoDB tables in operation
- 15
- named specialists, senior and lead
- 3
- own products running at scale
- 6
- years operating, without interruption
Figures from our own AWS account and our own team, not from a client project. That is the difference between operating and advising, and it is why we can publish them without asking anyone.
Where this has run
The offer above has proof, and the proof has an address.
Let's apply AI with maturity, and without theater.
If you want AI connected to the business, with real data, governance and operation, a technical and honest conversation is worth it.
Engineering without intelligence is incomplete.
Intelligence without engineering is fragile.