AI agents and multi-agent systems
Specialized agents that coordinate research, classification, decisions and actions across a defined business process.
Agent orchestrationTool callingHuman approval
Services / AI & Automation
AI that connects to real business systems.
Our AI automation services connect agents and workflows to APIs, databases and business processes, with human review where judgment matters.
Capabilities
Move from AI experiments to dependable workflows.
Our engineering foundation lets us connect models to the systems where the work actually happens.
Workflow automation
Automate repetitive operational work by combining LLMs, business rules, webhooks and system integrations.
n8nMakeZapierCustom workflows
Knowledge and retrieval
Ground AI responses in company documents, databases and approved sources through retrieval and structured context.
RAGVector searchDocument pipelines
LLM application development
Purpose-built internal tools and customer applications using OpenAI, Claude and Gemini models.
OpenAIClaudeGeminiMCP
System and API integration
Connect AI to CRM, ERP, property, commerce, finance and operational platforms with clear permissions.
RESTGraphQLPostgreSQLWebhooks
Evaluation and safeguards
Define test cases, confidence checks, escalation paths and observability before important workflows reach production.
EvaluationsAudit trailsGuardrails
How we approach AI
Start with the process, not the model.
A useful AI system needs a clear job, reliable context, permissioned tools and measurable success criteria. We map those elements before selecting the architecture.
- Identify the highest-friction decisions and manual handoffs
- Define what the system may read, recommend and change
- Design failure handling and human escalation
- Test quality, cost and latency against real cases
- Release in controlled stages and improve from evidence
1 / UnderstandProcess · Users · Decisions · Risk
2 / EngineerPrompts · Retrieval · Tools · Integrations
3 / ControlEvaluation · Approval · Monitoring · Audit
4 / ImproveFeedback · Metrics · Iteration
Featured system
Multi-agent hotel operations architecture
A hotel workflow can involve guest questions, booking context, service requests, policy checks and staff escalation. A single prompt is not enough.
This illustrative hotel scenario shows how DigiLynx’s multi-agent engineering approach can assign focused responsibilities to agents while an orchestration layer manages context, tool use and handoffs. The architecture is designed to be easier to test, observe and improve than an all-purpose assistant.
Explore the illustrative hotel operations architecture →
Specialized agentsClear responsibilities
Tool-connectedData and actions
Human-awareEscalation paths
Technology
A flexible stack for production AI.
We choose components around the workflow, existing systems and operational risk—not around a fixed vendor list.
OpenAIAnthropic ClaudeGoogle GeminiPythonTypeScriptNode.jsLangGraphMCPn8nMakeSupabasePostgreSQLREST & GraphQL APIs
From idea to a working AI workflow
Answers to the questions that shape a useful first release.
What can DigiLynx connect AI to?
We can design workflows around a company's existing APIs, databases, documents and operational tools. First we identify what the system needs to read, what it may change and where a person must approve an action.
How do you keep people in control?
We define access permissions, approval points, escalation paths and records of important actions as part of the workflow design. The controls depend on the data and the consequences of an incorrect action.
How does an AI project start?
We review one process, its current tools and a few real examples. We then define a limited first use case and agree how to measure whether it saves time or improves the work. Expansion follows evidence from that first stage.
Do we need to replace our existing software?
Usually the starting point is to connect to the systems your team already uses. Where an integration is missing or unreliable, we assess the work needed before proposing an implementation.