Applied AI with the discipline of a mission-critical operation.
A methodology that helps traditional companies move beyond experimentation and put artificial intelligence to work, with organized data, security, governance and costs under control.
Why so many AI initiatives never make it past the pilot
In traditional companies (manufacturing, retail, distribution, services, healthcare), the challenge is rarely the model. It is everything that comes before and after it.
Starting with the tool
The company buys an AI solution before knowing which business problem it is supposed to solve.
Scattered data
Information lives in spreadsheets, the ERP, email and people’s heads. Without organized data, AI gets it wrong.
Informal, risky usage
Employees use tools on their own, with customer and contract data, without policy or control.
A pilot with no owner
There is no owner, no success metric and no criteria to decide whether the project continues.
Nobody runs it afterwards
In production, quality drops, costs rise and there is no monitoring or incident response.
The FGLabs answer
Treat AI like any other critical system: assessment before promises, a solid foundation, observable operations and continuous improvement.
Six rules behind every project
Problem before tool
Every initiative starts with a business process and a business metric, not with the latest trend.
Data before models
Organizing, integrating and qualifying data is what separates a prototype from a result.
Security and privacy from day one
Data classification, access control and vendor assessment are part of the design, not an afterthought.
Humans in the loop
For decisions that matter, AI recommends and people decide. Autonomy grows as trust is proven.
Value measured in business terms
Cycle time, cost per process, rework and service levels, compared against the baseline.
AI in production is a critical system
SLOs, observability, cost control and incident response, with the same SRE discipline.
From assessment to continuous operation
The methodology mirrors the FGLabs Journey. Same language, same rituals and the same SRE discipline, applied to artificial intelligence.
Where does AI create value here, and are we ready?
What we do
- Mapping processes with volume, repetition and clear rules
- Data and systems inventory: ERP, CRM, spreadsheets, documents
- Readiness assessment: data, technology, people and risks
- Baseline for the metrics that will be affected
What you get
- AI maturity level
- Opportunity map (value × feasibility × risk)
- Recommended first use case
Who decides, what is allowed and how do we measure success?
What we do
- Executive sponsor and an owner for each use case
- AI usage policy and data classification
- Success and stop criteria defined before starting
- Communication and training plan for the teams involved
What you get
- AI acceptable use policy
- RACI and governance rituals
- Business case for the first use case
Can the foundation handle AI safely?
What we do
- Data source integration and quality
- Secure cloud environment with access control and secrets management
- Choosing the lowest-cost, lowest-risk approach: managed API, open model or classic ML
- Guardrails, audit logging and infrastructure as code
What you get
- Reference architecture
- Data pipelines
- Version-controlled, auditable environment
Does it work in the real world, with real users?
What we do
- One use case, fixed scope and real users
- Quality evaluation with a test set built from your own business
- Objective comparison against the baseline
- Documented decision: scale, adjust or stop
What you get
- Pilot in use
- Results vs. baseline report
- Go / no-go decision
How do we run this every day without surprises?
What we do
- SLOs for quality, latency and availability
- Observability of responses, cost per transaction and drift
- AI FinOps: budgets, alerts and usage optimization
- Runbooks and AI-specific incident response
What you get
- AI operations dashboard
- Actionable alerts
- Runbooks
How does AI improve and pay for itself over time?
What we do
- Periodic quality and cost reviews
- Tuning prompts, data and models based on real usage
- New use cases prioritized from the opportunity map
- Ongoing team enablement
What you get
- Quarterly AI roadmap
- Value delivered report
- Prioritized backlog
Where is your company today?
Discovery places your company on this scale and defines a realistic next level. Skipping steps is the shortest path to a pilot that never scales.
- Level 1
Individual experimentation
Personal use of tools, with no policy, no internal data and no measurement.
- Level 2
Guided usage
Usage policy, approved tools and basic team training.
- Level 3
Assisted processes
AI built into specific processes with internal data and a defined owner.
- Level 4
Integrated operations
AI in core systems, with SLOs, monitoring, controlled costs and governance.
- Level 5
Data and AI driven
A portfolio of initiatives managed by value; decisions supported by trusted models.
Typical use cases in traditional companies
Examples of starting points. Real prioritization comes from the Discovery opportunity map, weighing value, feasibility and risk.
Manufacturing
- Predictive maintenance with sensor and PLC data
- Quality inspection with computer vision
- Technical assistant for manuals and procedures
Retail and distribution
- Demand forecasting and inventory replenishment
- Customer service with automatic triage
- Assortment and pricing analysis
Finance, tax and HR
- Reading and checking documents: invoices, bills, contracts
- Reconciliation and anomaly detection
- Triage of internal requests
Sales
- Lead qualification and prioritization
- Proposals built from your knowledge base
- Meeting summaries and follow-ups
Healthcare and services
- Scheduling and request triage
- Summarizing documents and records
- Knowledge base for customer service
IT and operations
- Alert correlation and noise reduction
- AI-assisted incident analysis
- Automation of repetitive work (TOIL)
Trust is a requirement, not a final step.
The same Security by Design approach we apply to infrastructure, extended to data and models.
- Sensitive data only leaves the controlled environment after being classified and approved
- Role-based access control and an audit trail of relevant interactions
- Model vendor assessment: data retention, region and terms of use
- Compliance with applicable data protection laws (such as LGPD in Brazil): legal basis, purpose and data minimization
- Mandatory human review for high-impact decisions
- Industry frameworks such as the NIST AI RMF and ISO/IEC 42001 guide our governance
The first step is an AI Discovery.
Readiness assessment, opportunity map and a recommended first use case. Scope and investment are defined after Discovery.