ENTERPRISE SEARCH
AI enterprise search that returns answers, not files.
We unify knowledge spread across documents and systems into semantic search, answers with sources, and access that respects existing permissions.
DIRECT ANSWER
What is ai enterprise search?
AI enterprise search uses semantic retrieval and generative models to find and summarize internal content while keeping references to the sources and honoring access controls.
Who it is forCompanies with knowledge scattered across drives, intranets, documents, tickets, CRMs, ERPs, and technical libraries.
CONTEXT
Technical decisions with an operational view.
The challenge is not indexing everything, it is delivering the right information to the right person. Content quality, metadata, identity, and permissions matter as much as the model.
We build the ingestion, classification, retrieval, and answer layers with sources attached. We also deal with outdated, duplicated, and unowned content.
Evaluation measures relevance, coverage, faithfulness, and usefulness by question type, so the result does not rest on cherry-picked demos.
OUTCOMES
What the initiative has to deliver.
Technical goals only matter when they improve security, speed, cost, experience or the ability to decide.
- 01Less time spent looking for information
- 02Answers with links and sources
- 03Permissions preserved
- 04Priority content identified
- 05Relevance evaluation
- 06A foundation for enterprise agents
WHEN IT MAKES SENSE
Signs that it is time to act.
- People cannot find documents
- The same questions keep coming back
- Knowledge lives in many systems
- There is a risk of improper access
- The intranet returns little of value
- Agents need reliable context
HOW WE WORK
From assessment to operations.
Short stages, visible criteria and knowledge transfer at every decision.
Inventory
We map sources, users, permissions, and questions.
Preparation
We organize ingestion, metadata, and quality.
Search and answers
We configure retrieval, ranking, and generation with sources.
Evaluation
We measure relevance, faithfulness, and adoption continuously.
DELIVERABLES
Clarity on what gets finished.
- Source map
- Search architecture
- Connectors
- Semantic index
- Answer interface
- Evaluation suite
FREQUENTLY ASKED QUESTIONS
Straight answers.
Are RAG and enterprise search the same thing?
RAG is a technique for retrieving context before generating an answer. Enterprise search also covers connectors, ranking, permissions, user experience, and governance.
Does the answer show its sources?
We design the experience to show references so the user can check the original content.
Do we have to reorganize every document first?
No, but quality and ownership shape the result. A rollout can start with high-value sources and improve the rest in stages.
Can Gemini Enterprise be used?
Yes. It is one of the options for search and agents in the Google Cloud ecosystem, depending on sources, integrations, and requirements.
Technical sources and references
EVIDÊNCIA EM CAMPO
AI evaluated rigorously and wired into infrastructure.
Reproducibility, LLM evaluation, data, agents and operations are treated as parts of one system.
Inteligência Artificial
MLRC 2025 em Princeton: cinco princípios para avaliar LLMs com resultados reproduzíveis
Cinco aprendizados da EAGLE BS no MLRC 2025, no Princeton AI Lab, sobre benchmarks, determinismo, dados, transparência e avaliação robusta de LLMs.Ler artigo ↗
Google Cloud
Google Cloud Next 2025: IA, agentes e a nova infraestrutura digital
A participação da EAGLE BS no Google Cloud Next e Partner Summit 2025: Agentspace, Gemini 2.5, agentes, cloud, dados e aprendizados para clientes.Ler artigo ↗TALK TO A SPECIALIST
Tell us the situation. We help you see the best path.
A focused conversation to understand context, risk, priority and the first workable step.