From proof of concept to productive scale.
We co-create end-to-end generative AI and intelligent-agent solutions — connecting business strategy, data foundations and AI engineering to generate measurable value.
GenAI adoption has matured. Why do so few organizations still capture value at scale?
72% of organizations already use GenAI in at least one business function, but 74% still struggle to capture real value, and 30% of projects are abandoned after the PoC phase. The bottleneck isn't the technology — it's how organizations connect strategy, data, people and processes.
In our view, GenAI generates value when treated as a means, not an end. The relevant question isn't "how do we apply AI?", but "which business problem do we need to solve — and what combination of techniques, architecture and data best sustains it at scale?".
Distance between business and solution
Those who understand the problem rarely talk to those who understand the technique — and that stalls value creation.
AI treated as a strategy, not a tool
Without clear business objectives, projects become isolated experiments with no measurable ROI.
Immature data foundations
Siloed data, without quality or context, makes the leap from PoC to production at scale unfeasible.
Analytics culture still under construction
A lack of AI literacy among leaders and operational teams limits real day-to-day adoption.
From strategy to operation, in four layers of depth
A sequential method in which each layer inherits the decisions of the previous one and deepens the technical specification — without losing the connection to business strategy.
Before any line of code, we anchor the project in the company's Data & AI strategy. We map use cases from the value levers and validate the acceptable risk level and data readiness.
Outcome of this layer
A prioritized portfolio of use cases with value hypotheses and validated risk level.
Key decisions
- Use cases prioritized within the company strategy
- Value levers and business case
- Risk tolerance (exposure, ethical, financial)
- Data and infrastructure readiness
When there's doubt about applicability, we start with a quick PoC. When there's consensus but no evidence of value, we move to a PoV. When it's feasible, we build an MVP or a production solution.
Delivery models
PoC 1–2 days · PoV 2–4 weeks · MVP 8–12 weeks · Production.
Key decisions
- Model: PoC · PoV · MVP · Production
- Strategy: automation · AI Workflow · AI Agent
- Interface: conversational · multimodal · copilot
- Security and ethics guardrails
The deep technical layer. We specify the agent type, memory architecture, combined techniques (RAG, Reasoning, Fine-Tuning) and the tools the agent will access — APIs, ERPs, databases, search engines.
Technical stack
RAG · CAG · Text-to-SQL · Reasoning · Fine-Tuning, in single, multi and hierarchical architectures.
Key decisions
- Agent type: single · multi · hierarchical
- Memory: short · long term · semantic · episodic
- Techniques: RAG · CAG · Text-to-SQL · Reasoning
- Tools and integrations with internal systems
GenAI without measurement becomes investment without return. We measure across two dimensions: agent performance (precision, recall, hit rate) and business impact (AHT, conversion, FTE freed up).
Dual metrics
Agent performance (precision, recall) + business impact (AHT, conversion, FTE freed up).
Key decisions
- Performance: annotated responses · feedback · validation
- Operational impact: SLA · cost · FTE freed up
- Tactical impact: NPS · conversion · revenue
- Evolution roadmap and governance
5 execution stages, from framing to deploy
Framing
Understanding the challenge, mapping use cases and defining guardrails.
Understanding & Preparation
Environment provisioning, data sanity check and flow design.
AI Engineering
Prompt engineering, ingestion pipelines, orchestration and prototyping.
Refinement
Feedback collection, prompt tuning and end-to-end cost estimation.
Deployment
Production deploy, knowledge transfer and monitoring indicators.
90% engineering, 10% model
An AI agent's real performance depends far more on the architecture around the model — integration, memory, orchestration, observability and security — than on the choice of LLM itself. It's this engineering that ensures the solution scales, is reliable and generates value sustainably.
AI-ready data foundations
We structure pipelines, quality, governance and data availability for model consumption — in on-premise or cloud Big Data architectures.
Cloud-agnostic architecture
We work across Azure, AWS and GCP with certified partnerships, choosing the most suitable stack for each scenario — with no technology lock-in.
Specialist and multi-agent systems
We apply RAG, CAG, Text-to-SQL, Reasoning, Fine-Tuning and Web Search in single-agent, multi-agent and hierarchical architectures, integrating short- and long-term memory.
A Center of Excellence focused on scalable, replicable solutions
We run a dedicated, multidisciplinary structure — made up of business teams, developers, data scientists and data engineers — focused on building GenAI solutions ready for production and continuous evolution.
Dedicated, multidisciplinary Center of Excellence
A permanent structure of business teams, developers, data scientists and data engineers — combining technical depth and strategic vision across three fronts (analytical, business and product), with its own operating model.
Scalable, replicable solutions
We build reusable blocks from delivered cases — Contracts Agent, Judicial Process Analysis, HR Agent, Document Analysis — accelerating the time-to-value of new implementations across different contexts.
Technical-business depth
We don't deliver off-the-shelf solutions. We co-create with the client in short, iterative cycles, adapting the best technical practices to the real business context — with a consultative approach and joint development.
Connection to data foundations
We integrate GenAI with our AI strategy, data platforms, AI literacy and governance offerings — extending impact beyond the agent, acting on organizational transformation as a whole.
More than 20 GenAI & AI Agents projects delivered, from public to private sector — Justice, Finance, Environment, Industry and internal operations.
Solutions in production, generating measurable value
Public Sector — Justice
Multi-agent · RAG
Intelligent analysis of judicial processes
AI agent for the analysis and consolidation of criminal, family and penal-execution cases, with generation of initial drafts. Deployed at a state Public Prosecutor's Office, with a relevant productivity gain in triage.
Education
GenAI · Prompt Eng.
Educational question generator
A GenAI solution for assisted generation and curation of questions at scale, integrated into the editorial workflow of one of the country's largest education organizations — reducing the pedagogical content production cycle.
Digital Infrastructure
RAG · Semantic Search
Corporate contracts agent
An AI assistant for querying and analyzing contract clauses across multiple business areas at a leading data center operator, with RAG-based semantic search over the document base — reducing manual search demands.
Institutional recognition of solutions in production
GenAI projects developed by EloGroup have impact publicly recognized by sector authorities.
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Sector
Public Sector — Justice
