AI STRATEGY
Identify where artificial intelligence can create concrete value across products, operations and business processes.
AI & MACHINE LEARNING CONSULTING
TURN AI OPPORTUNITIES INTO SYSTEMS READY TO BE ENGINEERED
We frame AI initiatives before development to align value, data, models, architecture, risks and production strategy.
AI FRAMING
01Opportunities & Objectives
02Use Cases
03Data & Feasibility
04Architecture & Models
05Prototype & Validation
06Roadmap & Production
CAPABILITIES
STRUCTURE AI BEFORE INDUSTRIALIZING IT
AI consulting turns opportunities, data and constraints into actionable engineering decisions.
USE CASE PRIORITIZATION
Compare opportunities by value, complexity, available data and technical feasibility.
DATA & READINESS
Assess data sources, quality, accessibility and the transformations required for intelligent systems.
AI ARCHITECTURE
Define the models, services, APIs, pipelines and cloud components required for an operational AI architecture.
PROTOTYPE & POC
Validate technical assumptions quickly before committing to large-scale engineering or integration.
AI ROADMAP
Turn technical decisions into clear engineering, integration, validation and production milestones.
INTELLIGENT ARCHITECTURE
CONNECT MODELS, DATA AND INFRASTRUCTURE
We approach AI as a complete architecture connecting models, data, APIs, cloud and production systems.
MODELS & LLMs
Model selection, LLMs, specialized models and inference strategies aligned with the product context.
RAG & KNOWLEDGE
Semantic search, embeddings, vectorization and RAG architectures connected to business data.
INTELLIGENT SYSTEMS
NLP, vision, classification, recommendation, prediction and automation according to product requirements.
MLOPS & PRODUCTION
Deployment, versioning, observability and pipelines for operating models in production environments.
AI ECOSYSTEM
TECHNOLOGIES CONNECTED TO THE PRODUCT
01LLM
02RAG
03NLP
04Computer Vision
05Embeddings
06Vector Search
07Python
08FastAPI
09PostgreSQL
10Redis
11Docker
12Cloud
PROCESS
FROM OPPORTUNITY TO AI ROADMAP
UNDERSTAND
Clarify the problem, users, objectives and expected business or product value.
ASSESS
Analyze data, constraints, risks, feasibility and technology options.
DESIGN
Define architecture, models, integrations and the prototype scope.
VALIDATE
Test assumptions through experimentation, prototypes or proofs of concept.
PLAN
Build the roadmap toward integration, MLOps and production deployment.
PRODUCT VALUE
REDUCE UNCERTAINTY BEFORE ACCELERATING
FEASIBILITY
Verify that an AI use case is technically realistic before investing in industrialization.
VALUE
Focus AI engineering on problems where its impact can be meaningfully measured.
INTEGRATION
Design AI as part of the product and its architecture rather than as an isolated system.
GOVERNANCE
Include security, data control, traceability and oversight in architectural decisions.
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