Custom AI agents – secure, integrated and measurable
Lyron builds AI agents that understand email, documents and company knowledge, then handle defined tasks in CRM or Microsoft 365 – controlled, privacy-aware and measurable.
- GDPR by design
- Clear approvals
- Measured in production
What an AI agent actually does in a business
An AI agent combines a language model such as Claude or GPT with company knowledge and specific tools. It can understand a goal, plan the next steps and take action within clearly defined boundaries.
Unlike a chatbot, an agent can retrieve data, validate results, start a workflow in n8n, prepare a CRM record or request approval from the responsible employee.
Understands context
It works with natural language, emails, documents and relevant knowledge instead of relying on rigid rules alone.
Uses your systems
Controlled interfaces connect CRM, ERP, Microsoft 365, knowledge bases and internal APIs.
Stays controllable
Permissions, approvals, logs and thresholds define what can run autonomously and when a person takes over.
Routine work stays until a system takes it over
AI agents create value when employees repeatedly read content, gather information, transfer results or prepare standard cases. We establish the current workload first and then automate only the share that can be delegated reliably.
- Less processing time: preparation and information retrieval happen automatically.
- Faster response: requests are classified and prioritised immediately.
- More focus: your team handles exceptions and decisions instead of copy and paste.
When is it worth building a custom AI agent?
The best starting point is not a huge transformation programme but a clear process with recurring volume, measurable effort and an outcome that can be validated.
Strong pilot candidate
Signals that an AI agent can create value
- Similar cases occur repeatedly each week and consume meaningful team time.
- Emails, documents or system data are available digitally and can be accessed safely.
- A good result can be checked against examples, rules or measurable criteria.
- Uncertainty and sensitive actions can be routed through a clear human approval.
Build the foundations first
When an agent would be premature
- Every case is completely unique and difficult for subject-matter experts to validate.
- Critical information is missing, contradictory or exists only in employees' heads.
- Process ownership, permissions and expected outcomes have not been clarified.
- The agent is expected to make high-impact decisions without accountable review.
What you get from the initial fit check
You receive a candid assessment of value, data readiness, integrations, protection needs and a sensibly scoped pilot – including a measurable outcome rather than a vague AI idea.
Where AI agents reliably reduce workload today
We start with a clear business outcome and a limited responsibility – not an agent that supposedly does everything.
Email & support
Understand and prioritise requests, retrieve knowledge and prepare sourced responses.
See support automation →Sales & leads
Enrich and qualify leads, prepare CRM records and suggest the next useful action.
See lead automation →Knowledge & research
Search internal documents, provide sourced answers and structure information.
Learn about RAG →Documents & back office
Classify invoices, contracts or forms, extract data and prepare checks.
See document processing →Reporting & analysis
Combine data from multiple sources, explain variances and prepare recurring reports.
Automate status reports →Operations & orchestration
Coordinate tools and subprocesses, validate outcomes and escalate exceptions with context.
Understand agentic automation →Autonomous where useful. Controlled where important.
Production AI agents need more than a good prompt. We build guardrails and traceability into the architecture.
Data & hosting
Model and hosting choices match the protection level, including EU or self-hosted options.
Roles & permissions
The agent receives only the tools and data access required for its responsibility.
Human in the loop
Sensitive actions require approval; uncertain cases go to an accountable employee with context.
Logs & monitoring
Actions, failures and quality are logged, monitored and improved systematically.
We also document relevant EU AI Act requirements and AI literacy measures.
Can your existing AI subscription be reused?
Yes – an existing business workspace can be a practical starting point for pilots, internal knowledge work and employee assistants. An AI agent that works autonomously with your systems in the background will usually need separate API access.
Reuse your business workspace
ChatGPT Business or Enterprise and Claude for Work are suitable for internal assistants, projects, knowledge work and a controlled pilot.
API for the production agent
When an agent processes CRM, email or documents automatically, we normally use an API. Chat subscriptions and API usage are provided and billed separately by OpenAI and Anthropic.
Match the model to the data class
Depending on the protection level, we integrate OpenAI, Anthropic, Azure OpenAI, AWS Bedrock or an EU-hosted or self-hosted alternative.
Vendor information: OpenAI enterprise privacy · OpenAI subscription and API · Anthropic commercial data · Claude subscription and API
How we build your AI agent
- 1
Define the task and metric
We agree what the agent owns and how value, quality and boundaries will be measured.
- 2
Connect data and systems
Knowledge sources, CRM, Microsoft 365, APIs and permissions are integrated cleanly.
- 3
Build the agent and guardrails
We implement tools, prompts, validations, approvals, failure paths and cost limits.
- 4
Test with real cases
Test sets evaluate accuracy, edge cases, security and hand-offs to employees.
- 5
Roll out and optimise
After the pilot, we monitor usage, quality, cost and exceptions and improve deliberately.
AI agent, workflow or chatbot?
| Solution | Strength | Typical use |
|---|---|---|
| Rule-based workflow | Reliable with clear rules | Data transfer, notifications, approvals |
| Chatbot | Conversation and knowledge access | FAQs, internal search, first support response |
| AI agent | Understands context and uses tools | Multi-step tasks, research, preparation and orchestration |
The best architecture is often a combination: stable workflows for rules and AI only where context is required.
Practical guides for production AI agents
Frequently asked questions about AI agents
Which AI agent would give your team the most time back?
In a free consultation, we assess the task, data, risks and measurable opportunity – clearly and without technology theatre.
Discuss your AI agent opportunity