We design and operate AI agents that go beyond answering: they plan, decide and execute tasks autonomously — integrated into your systems and governed end to end, from the business case to continuous monitoring.
The challenge of generating value with AI
95% of organizations report no measurable return.
MIT State of AI in Business (2025)
Most initiatives stay stuck in experimental mode, without generating P&L impact.
How do you move from AI experimentation to value creation at scale?
- Productivity gains in day-to-day activities
- Ready-to-use tools and a "plug-and-play" model
- Lower implementation cost; operating costs make up most of the total cost
- Direct impact on performance and P&L results
- Tailored solutions to address business opportunities
- Greater investment in developing or acquiring the solution, plus ongoing operating costs
Four fronts that structure the agent lifecycle — from value thesis to continuous operation.
From business case to agent in production.
Vision & Business Value
- Business case definition
- Stakeholder alignment
- Value levers and KPIs
- Risk-exposure assessment
- Readiness assessment (data and infrastructure)
Project Strategy
- PoC, PoV, MVP or production deployment
- AI approach definition: Automation, AI Workflow or AI Agent
- Scope and technical-maturity assessment
Agent Design & Engineering
- Agent type (Single, Multi-Agent, Hierarchical)
- Orchestration model
- Memory architecture (short and long term)
- Techniques (RAG, Text-to-SQL, Reasoning, Web Search)
- Tool and API integration
- Security and governance layers
Monitoring & Continuous Optimization
- Agent performance metrics
- Business-impact measurement
- Feedback loops
- Cost monitoring
- Risk management
Supercharge your GBS with AI agents. Faster decisions, smarter execution.
30–70%
productivity gains in complex, judgment-based service flows
15–30%
reduction in cost per transaction in high-volume operations
Up to 50%
reduction in exception-handling workload
AI agents applied to real business contexts.
Results that prove themselves.
Government
AI-driven analysis of court documents and report drafting for the STF (Supreme Court)
Development of an AI solution to automate the analysis of case documents and support the drafting of judicial reports. Through a dedicated web application, lawyers interact with the AI agent to identify appeals and contested decisions, analyze complex case records, extract legal references and structured insights, answer specific queries and generate refined drafts using natural language processing and machine learning — accelerating case preparation.
90%+ classification accuracy and 1,500+ petitions processed per month.
Telecom
GenAI virtual assistant for HR services
Implementation of a GenAI virtual assistant to automate responses to HR questions, providing accurate, instant answers based on official policies and internal documentation. The solution reduces repetitive operational load on HR teams, strengthens employee self-service and establishes a scalable, AI-enabled communication channel for additional services and workflows.
67%+ automated response rate and 50+ average weekly queries handled.
Data Center
AI assistant for RFP management
Implementation of an AI solution to interpret RFP requirements, query internal knowledge bases and automatically generate high-quality draft responses and follow-up questions. The solution accelerates proposal development, reduces review time and lets commercial teams focus on strategic opportunities and client relationships.
~90% accuracy in generated responses, ~70% reduction in review time and 4%+ SLA improvement.
