AI automation and workflow automation for real business processes

It can include workflow discovery, use-case design, data and permission planning, integration, Human review, testing, installation, monitoring, and handover. The actual scope depends on existing systems and the risk of the workflow.

A chatbot usually answers within a conversation. An AI Agent can respond to triggers, read authorised data, use tools, and hand work through a defined workflow. Every action still needs explicit permissions and observable conditions.

It can fit SMEs, factories, and large organisations when a repeatable workflow has a clear owner and testable outcomes. An internal AI team is not required, but the project needs operational experts and decision-makers for data, permissions, and pilot goals. We work with teams in Bangkok, across Thailand, and internationally through remote delivery.

It can when the system provides an approved API, webhook, file exchange, or other supported interface. We review each system's documentation, permissions, and constraints before confirming an integration rather than claiming universal compatibility.

An FDE stays close to users and the real workflow from discovery through working deployment. The role connects business context with integration, AI Agent development, testing, adoption, and handover instead of ending with an advisory document or context-free code delivery.

Cost depends on workflow scope, integrations, data quality, permissions and risk, infrastructure, monitoring, and post-launch ownership. We therefore begin with a pilot that has explicit acceptance criteria before estimating a broader rollout.

Work that repeats, has clear inputs, and produces results people can verify—for example classifying documents, preparing summaries, moving validated data, or routing work against defined conditions.

The system can remain available to respond to defined triggers. The required uptime, monitoring, and recovery design still depends on how critical the workflow is; 24/7 is not a promise of zero downtime.

Not always. We design around the organisation's data, permission, and approved-provider requirements to decide what remains on customer-controlled infrastructure and what may use an external service.

Steps affecting money, access, legal duties, customers, or sensitive data should have approval gates based on risk. Fully automatic handling is reserved for steps that are observable and reversible.

A production system should have logs, monitoring, fallbacks, an owner, and recurring quality reviews. When the workflow, data, or model changes, it should be tested against agreed cases and the runbook updated before permissions or automation are expanded.

Timing depends on the number of integrations, data quality, permission requirements, and acceptance criteria. We define a pilot scope and milestones after examining the real workflow rather than promising one timeline for every business.