Applied AI where the return can be measured
Assistants, document automation, forecasting and classification, integrated with your systems and governed on data and cost.
AI & Data
Official partner
Companies that trust Ecenture
When it makes sense / 01
When it makes sense
If one of these describes your company, it's worth a conversation.
Your team spends hours reading, classifying or retyping documents.
Customer service repeats the same answers every day.
You have historical data, but decisions are still gut feeling.
Previous AI tests never left the pilot stage.
What we deliver / 02
What we deliver
From pilot to AI in production, with the success metric defined up front.
LLM assistants
Internal and customer-facing assistants that answer from your documents and systems (RAG).
Document automation
Extraction and validation of data from invoices, contracts, reports and forms.
Forecasting and classification
Demand, churn, risk and prioritization models trained on your data.
Agents that take action
Flows where AI queries systems, fills records and opens tickets, with human approval.
Governance and security
Access control, usage logs, answer evaluation and data protection (GDPR/LGPD).
Cost and performance
Model choice, caching and monitoring to keep cost per use predictable.
How we can work together / 03
How we can work together
Lens, Build and Scale applied to this service.
- Focused consulting
Lens
A decision about AI in your operation: we assess where it pays off with the data you have, and recommend the path and next steps.
- Fixed scope
Build
One AI use case with fixed scope: from pilot to production use.
- Monthly capacity
Scale
Monthly capacity to support the models and bring new AI use cases to production, with a backlog you prioritize and an SLA.
Technologies / 04
Technologies
Broad experience: since 2012 we've worked across the market's main stacks, on projects in 11 countries. Technology is chosen for the problem and your team, not our preference.
Models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Azure OpenAI
- Amazon Bedrock
- Vertex AI
Frameworks
- LangChain
- LlamaIndex
- Model Context Protocol
- Hugging Face
- PyTorch
- scikit-learn
- XGBoost
Data and vectors
- PostgreSQL + pgvector
- Pinecone
- Qdrant
- Elasticsearch
- Databricks
- Snowflake
Operations
- Python
- FastAPI
- Docker
- MLflow
- Langfuse
- AWS
- Azure
Process / 05
How we work
- 01
Diagnostic
We pick the use case by return and set the success metric before starting.
- 02
Strategy
Data assessment, model choice, integration and controls design.
- 03
Implementation
A pilot with real users and quality evaluation in every release.
- 04
Evolution
Production with quality, cost and usage monitoring, and continuous improvement.
FAQ / 06
Where should we start with AI?
With a process that has volume, clear rules and known cost. The diagnostic helps pick the first use case by return.
How much does it cost?
It depends on scope. After the 30-minute diagnostic call, we send an investment range before any formal proposal.
Is my data safe?
Yes. We use providers that don't train on your data, control access and log usage, in line with GDPR and LGPD.
Will the AI make mistakes?
It can. That's why we measure answer quality, keep human approval where risk is high and make clear what the system does on its own.
Do you also train the team?
Yes. We offer hands-on AI training for the teams that will use the solution.
Also in ai & data / 07