Knowledge-base assistant
Answers with your documents, policies, and procedures, citing sources so the result can be verified.
Grounded answers, not model opinions.
AI agents connected to your real context.
We connect models with documents, data, CRM, and tools so agents answer with grounding, execute actions, and leave traceability.
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Context
01The agent starts with your information
Documents, knowledge bases, CRM data, and history enter a source the agent can consult.
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Judgment
02It reasons over your data, not guesses
It retrieves the right fragment, understands intent, and builds an answer grounded in verifiable sources.
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Action
03It acts with connected tools
It schedules, creates records, updates the CRM, or triggers workflows with clear limits and human approval when risk requires it.
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Supervision
04It remains traceable and improves
Every query and action is recorded with its source, so you can measure, correct, and refine behavior.
What usually breaks AI initiatives.
A model without business context is a demo. An agent with context, permissions, and traceability can become part of the operation.
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Generic chatbots answer nicely but know nothing about the business.
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Knowledge is scattered across documents, chats, and people’s heads.
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Agents hallucinate because they do not have a connected source of truth.
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AI answers but does not execute, so it never reaches operations.
Cases where context changes the result.
The value appears when AI can read what matters, decide within limits, and act through the right tools.
CRM-aware copilot
Reads customer history, opportunities, and notes to suggest the next commercial action.
Better sales support with real context.
Agent with actions
Uses tool calling to create records, schedule meetings, or trigger workflows under defined permissions.
AI that moves work, not just text.
Memory and traceability
Records sources, decisions, and outcomes so behavior can be audited and improved.
Control over how the agent learns and acts.
Tools
The model is only one piece.
We connect the model, context layer, business tools, permissions, and observability needed for real use.
- OpenAI Models, tool calling, assistants, and structured outputs.
- Anthropic Claude for reasoning and context-heavy workflows.
- Gemini Google AI models and Workspace-adjacent context.
- LangChain Agent orchestration and retrieval workflows.
- OpenRouter Model routing and experimentation.
- GitHub Copilot Developer productivity and code assistance.
- Vector databases and business systems We connect the context and tools the agent needs.
What remains working.
An AI workflow with context, permissions, and evidence, ready to improve after launch.
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Context map and source selection
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Prompt and behavior rules
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Tool connections and permissions
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Human approval points
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Traceability and evaluation logs
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Runbook for operating the agent
Frequently asked questions.
How do you prevent AI from inventing answers?
We ground answers in selected sources, define when the agent must say it does not know, and log sources so outputs can be checked.
Can an agent use my existing tools?
Yes, if those tools expose APIs, webhooks, databases, or reliable export paths. We define permissions and limits before enabling actions.
Which model should I use?
It depends on context size, task type, privacy, cost, and latency. We choose the model after defining the workflow, not before.
Next step
Let’s detect the first AI agent worth connecting.
In one call we review your data, tools, and current AI use to choose a workflow that can move real work safely.
or email us at hola@lirroy.com