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Project

ResolveAI Ticket Triage Copilot

Zendesk/OpenAPI copilot proposing macros, intents, and safe human-in-loop sending.

Client
ResolveAI Ticketing BV
Expertise
Industry, Technology
Scope
4 months
ResolveAI Ticket Triage Copilot

about project

Zendesk/OpenAPI copilot proposing macros, intents, and safe human-in-loop sending.

  • python
  • langchain
  • openai-compatible
  • celery

Tier-one agents scrolled macro libraries while SLA clocks burned.

We trained intent classifiers grounded in approved snippets with refusal paths for sensitive topics.

Suggested replies cite knowledge article IDs supervisors can veto in one click.

Quality dashboards spotlight drift weekly.

Features

  • Grounded answer templates with citations
  • Human approve/deny instrumentation
  • PII scrubbing pipelines
  • Drift dashboards by queue
  • Slack escalation for risky topics

Case
study

OUR
Approach

How we framed the challenge, shipped the solution, and measured outcomes.

Challenge

Legacy tooling could not audit changes at scale.
Stakeholders lacked a single dependable source of KPI truth.

Our approach

Introduced hardened workflows with automation at the bottleneck.
Shipped dashboards with guardrails and explicit ownership.

Results

Handle time improved without increasing escalations improperly.
New hire onboarding time shortened.
Impact

Key metrics

−19%

Avg handle time (simple tier)

up: 88%

Macro accuracy thumbs

0

PII leakage incidents
Stages

Work in stages

01

Requirement Gathering & Discovery

1

02

Project

However, all complete IoT systems are the same in that they represent the integration of four distinct components: sensors/devices, connectivity, data processing, and a user interface.

03

IOT Projects software

The software then analyzes and transmits the data to users via an app or website.

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