Grounded answers
Support answers use approved knowledge resources.
Ergo Global AI Customer Operations System
Chat once depended on two people noticing each message, with immediate pickup estimated at just 5%. The connected system now delivers a typical 36-second website-chat response and keeps personalised follow-up moving after assessment.
AI support automation and operations platformErgonomics / Customer Operations
36 seconds
typical first response on website chat
11,000+
conversations across seven support inboxes over five months
~2,350/month
conversations per month over the five-month period
Rounded from 11,782 conversations over five months.
15,000+
messages exchanged over five months, roughly 3,000 per month
The monthly figure is a rounded five-month average.
3,000+
personalised check-ins across 1,749 employee profiles
From a 5% Chance of Immediate Pickup to a 36-Second Typical Response
This was not simply a faster reply. It was a shift from support constrained by staff availability to a connected operation that could stay responsive and continue contact after an assessment.
Before
Chat was available to just two clients and depended on two people noticing each message. Immediate pickup was estimated at roughly 5%. When a chat was missed, users were generally redirected to email with a one-business-day response expectation.
After
Five months later, website chat had a typical first response of 36 seconds. The connected operation was averaging approximately 2,350 conversations and roughly 3,000 messages per month across seven inboxes, while ErgoWOW had sent 3,019 personalised check-ins across 1,749 employee profiles.
Automation Had to Know Its Limits
Fast answers mattered, but the system also had to recognize when a conversation needed context, sensitivity, or human judgment. Treating every message as a chatbot question would have created more risk than value.
Fragmented channels
Website chat, internal app chat, and email inboxes needed one consistent operating model.
Sensitive exceptions
Pricing, bugs, unhappy users, unknown answers, sales opportunities, and explicit human requests required handover.
Client-specific context
Knowledge, parameters, and follow-up behavior had to remain scoped to the right client context.
Our Role
We worked across support workflow design, AI behavior, Chatwoot deployment, Outlook integration, self-assessment UX, and follow-up logic. The central product decision was to automate routine movement through the system without hiding the point where a person should take over.
Unify the operating model
We mapped separate channels and follow-up processes into one connected customer operations flow.
Define safe handover
We established the cases that should move from AI triage to a team member instead of forcing an answer.
Connect the product around the AI
We integrated the channels, knowledge sources, assessment experience, and scheduled follow-up needed to make the workflow usable.
From Incoming Message to the Right Next Action
The system treats each conversation as an operational decision, not only a request for generated text.
1. Receive the conversation
A message enters through website chat, internal app chat, or an email inbox.
2. Ground the response
The AI uses approved resources, manuals, website content, and relevant order information.
3. Check the handover rules
The workflow identifies sensitive, uncertain, commercial, or explicitly human cases.
4. Continue or escalate
Routine work continues automatically while the right exceptions move to a team member.
What Adamant Code Built
Ergo Global needed more than a chatbot. We connected support channels, knowledge, self-assessment, follow-up, and human handover into one customer operations system.
Human handover
Pricing, bugs, unhappy users, sales opportunities, and edge cases are escalated.
Follow-up
Assessment check-ins trigger short questions, tailored tips, and escalation when needed.
The Product Beyond the Reply
The AI sits inside a broader operations system that supports assessment, follow-up, routing, and consistent service across channels.
Centralized support workspace
Chatwoot brings customer conversations into a shared interface for AI and human handling.
Conversational self-assessment
Free-text input and clarifying questions replace a rigid assessment experience while preserving scoring and escalation logic.
Scheduled follow-up
Short check-ins, relevant guidance, and problem escalation keep the workflow moving after an assessment.
Privacy-conscious scoping
Client-specific information and parameters remain separated inside the operating model.
The Outcome
Over five months, the support platform managed 11,782 conversations across seven support inboxes and exchanged 15,120 messages. Website chat achieved a typical first response of 36 seconds. ErgoWOW also sent 3,019 personalised check-ins across 1,749 employee profiles. The automated follow-up programme delivered the equivalent of approximately 150–250 hours of manual outreach over five months. The estimate assumes three to five minutes of manual work per check-in. This is a modelled comparison, not a measurement of time saved.
What This Proves
Customer operations AI becomes useful when it can move routine work forward, stay grounded in approved information, and make human handover part of the product rather than an exception added later.
Planning an AI customer operations system?
We can help turn fragmented support channels into a grounded workflow with clear routing, follow-up, and human handover.
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