Services
Custom tools, automations & AI agents
Many industrial needs justify neither an ERP/MES program nor heavy development.
Our approach
What we actually do
Many industrial needs justify neither an ERP/MES program nor heavy development: quality deviation tracking, supplier portals, workflows across five legacy systems, AI agents on technical documentation.
Vanguard Systems assembles the right mix for the site context: no-code automation (n8n, Make, Power Automate), lightweight databases (Airtable, Dataverse, PostgreSQL), AI agents via LLM APIs deployed on Azure or AWS Bedrock EU zone, custom web apps (Next.js) when no-code reaches its limits.
Always with industrial requirements in mind: confidentiality, OT/IT integration, audit trail, lifespan beyond 5 years.
Our expertise
Technologies
Use cases and references
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Frequently asked questions
How do we choose between a no-code tool (n8n, Power Automate), a lightweight database like Airtable or Dataverse, and a fully custom web app?
No-code tools such as n8n, Make and Power Automate suit orchestrating workflows across existing systems. A lightweight base like Airtable or Dataverse fits when you need a business tool with tracking, versioning and an audit trail over time. A custom web app in Next.js comes into play when no-code reaches its limits: customer portals, heavy user tools, offline requirements. The right choice depends on the site's context, not a default technology preference.
Can an AI agent process our data without sending it outside Switzerland or Europe?
For AI agents, Vanguard Systems deploys Anthropic Claude and OpenAI models as SaaS or via Azure OpenAI / AWS Bedrock EU zone to respect data sovereignty. On a pharma site under strict confidentiality constraints where cloud LLMs are not allowed, the answer is an on-premise deployment instead. The choice follows the client's confidentiality constraints, not a default setup.
How do you automate a flow that runs through several legacy systems (ERP, MES, quality)?
At a Swiss fine chemistry manufacturer, redesigning the new raw material introduction process took three streams: end-to-end flow mapping and redesign with business stakeholders, Power BI analysis of historical data to identify bottlenecks, then RPA and Power Automate automation of the orchestration across five heterogeneous legacy systems (ERP, MES, quality, purchasing, safety). The technical automation comes after the flow mapping, not before.
Can an AI agent draft a quality deviation analysis in a GMP environment?
Yes. On a pharma site, deployed on the non-critical zones of a GMP facility, an AI agent (Anthropic Claude via Azure, EU zone) drafts the deviation analysis from LIMS and CMMS context. Human validation stays systematic before submission to the official quality system, with full traceability of AI suggestions and their acceptance. The agent supports the drafting, it does not replace the quality decision.
How long does it take to set up an AI agent on technical documentation?
For a French-speaking Swiss special-machine OEM, the AI agent built for field technicians was live in 6 weeks: orchestration via self-hosted n8n, retrieval of technical documentation and failure history through RAG, an LLM call (Anthropic Claude via AWS Bedrock EU zone), delivered on mobile and tablet in the workshop or at the end customer site. Full handover to the internal team happened after the pilot.
Should we run n8n self-hosted, or use a SaaS tool like Make or Power Automate?
Self-hosted n8n is recommended for pharma sites and contexts where data must not pass through a third-party service. Make and Power Automate remain relevant for less sensitive workflows, especially when the client is already built around Microsoft 365. The choice depends on the confidentiality level required and the IT ecosystem already in place at the client.
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