The challenge 01
Feedback was easy to collect—and hard to use.
Teams needed one path from an individual comment to a pattern, a response, or an escalation.
Case study · Feedbacker
From customer comments to clear next steps.
Dataplicada designed and built a complete feedback operation for service teams.
See what we built ↓The system 02
One connected workflow.
- 01CollectForms, QR, CSV and WhatsApp
- 02ReadTranslation, sentiment and keywords
- 03UnderstandAI summaries and searchable evidence
- 04RespondRewards, PQRS and escalation
What we built 03
A working product—not an AI demo.
Product. A secure multi-company workspace for feedback, users and service recovery.
Intelligence. A Python analysis service for language, trends and assisted interpretation.
Operations. Queued messaging, signed webhooks and traceable customer follow-up.
Laravel · FastAPI · MySQL · NLP · AI agents · messaging workflows
The accomplishment 04
We took Feedbacker from concept to an end-to-end system.
The work gave Dataplicada reusable experience in multi-tenant software, language pipelines, applied AI and customer operations.