Data structure, predictive models and AI agents on the same team. From mapping to the agent in production, with LGPD from the very first datapoint and every generative AI traceable.
BI up and running, data scattered across legacy systems and cloud, disconnected AI initiatives. What's missing is centralization, governance and consistent value generation.
Specialists in data engineering, machine learning, generative agents and visualization.
turning data into critical-operation decisions.
from raw data to the model in production, from dashboard to agent. Multi-cloud and multi-LLM.
What should be an automated decision still requires human approval.
BI meets the current cycle. The next generation of analytics demands different infrastructure.
Each new AI goes into production without continuous model monitoring.
Data engineering at the core. LGPD from the first datapoint and traceable generative AI in every delivery.
A structure oriented to data strategy, intelligence and execution.
Every Data & AI operation has flow and structure. TATe AI cuts across both as a governance and orchestration layer, connecting business, data and technology at every step.
Three reasons connecting what we deliver to what changes in your operation.
From consulting to production delivery, with no handoffs to third parties between stages.
A squad that evolves with the operation, not just consulting that hands over documents.
Strong governance without slowing the team, with TATe AI orchestrating.
Agri credit analysis, with registration distributed across multiple stages and integration with CAR, IBGE and banks.
Standardize and automate credit analysis, in a flow with inconsistencies and rework that hindered efficient, reliable decision-making on credit approval.
Credit intelligence model with machine learning, classifying producers (individuals/companies) by FICO score from AA to E, integrated into the company web portal. Automated data pipelines and operations across the different regions.
More agility in analysis, the end of constant re-evaluations and more confidence in results. Model replicated for other units.
Specialists in cosmetics regulatory affairs, with registration in European Union markets.
Validate the legal compliance of cosmetic formulas with the regulations of each European Union country. A manual process, researching legal databases and interpreting regulations in different languages.
Custom AI agent with an interface for specialists. Ingestion of regulatory data (REACH, CPNP) and NLP models trained to identify substances, concentrations and restrictions.
Fast lookup on formula compliance and the adjustments needed for registration in each country.
Veloe's technology leadership.
Organize and prioritize AI demands, structure governance and create solutions with measurable financial impact.
Exclusive Taking AI squad dedicated to Veloe. Deliveries: virtual assistants, a centralized AI platform and license-plate recognition systems. Clear financial metrics (ROI) and Azure infrastructure.
Optimized internal efficiency and productivity, improved customer experience and measurable ROI. Veloe as a benchmark in digital innovation.
Credit area with identity verification in audio interviews.
Automate identity verification in audio credit interviews, ensuring LGPD compliance and reliability of declared data.
End-to-end AI solution: transcription models (Whisper), LLM-based enrichment and automated verification of captured data. Architecture with data governance, secure audio pipelines and continuous model performance monitoring.
A more agile and scalable operation, with manual steps eliminated and errors reduced. Better experience for customers and credit teams.
A company with high transaction volume and multiple units, subject to the Central Bank and other regulatory bodies.
Meet regulatory requirements in a high-volume, multi-unit environment. Robust, reliable solutions to avoid risk and maintain efficiency.
Dedicated team for regulatory projects, such as delivering CADOCs. Data Lakehouse processing more than 2.5 billion records, with cloud migration for resilience and scalability.
Real-time visibility for the compliance team. An operation meeting regulatory requirements with agility and security.
It begins with Data Advise: a data assessment with technology inventory, data lineage, quality analysis and gap identification. The output is a prioritized modernization roadmap with an executive report. The full journey follows Discover → Structure → Intelligence → Governance → Operate, from raw data to the agent in production.
Both. A dedicated AI squad or a closed, one-off project. In the dedicated squad, an exclusive Taking team evolves with the operation, with structured governance and a clear financial metric (ROI) per delivery.
Yes. Taking operates on Azure, AWS, Google Cloud and Oracle Cloud. The stack includes Databricks, Microsoft Fabric, BigQuery, Redshift, Power BI, Tableau, Looker, Apache Airflow, Apache NiFi, Apache Spark, Hadoop, Kafka, Delta Lake and Iceberg, among others. Supported AI models: OpenAI, Claude, Gemini and LLaMa, with frameworks such as LangChain, LlamaIndex and CrewAI.
Privacy by Design across all solutions, with data minimization, pseudonymization and full auditing. Compliance with Law 13.709/2018 includes Data Flow Mapping, explicit consent mechanisms and data-subject rights features. In the LEV project, the audio pipeline is secure end to end with continuous model performance monitoring.
Yes. Every generative AI goes into production with a complete, auditable prompt and LLM history. Every interaction is traceable, with governance and regulatory compliance built in. In the VSAT model (Virtual Squad AI with TATe), TATe AI acts as a governance and orchestration layer, with continuous learning and native auditing.
Yes. In the Edenred project, Taking built a Data Lakehouse processing more than 2.5 billion records to meet the Central Bank regulatory requirements (CADOC delivery). In the Veloe project, we integrated relational databases, DynamoDB, APIs and TXT files into a unified Data Lake with near-real-time ingestion, on Amazon EC2 infrastructure with Apache Spark and PySpark.
Hands-on delivery, not just advisory. Taking delivers from architecture to the agent in production, in a dedicated squad that evolves with the operation. Strong governance without slowing the team.
We reply within one business day to schedule the conversation. No proposal, no charge, no pitch. If it doesn't make sense for Taking, we'll say so in the meeting.
30–45 minutes. No charge, no proposal, no pitch.
Technical and business team from Taking — not a generic SDR.
You leave with a preliminary read of your scenario — and a clear decision to move forward, dig deeper, or stop.
Received. Taking will be in touch within one business day to schedule the conversation.
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