Building your own analytics is more than picking a model: connectors, a category tree, model upkeep, security. In Heravox, it all comes ready.
A demo on sample data. The result looks promising.
Prompt upkeepA connector for every source; customers matched across channels.
Source connectors, Customer matchingCategories drift, the same problem multiplies under new names, trends break.
Category upkeep, RelabelingA new model ships; switching needs measurement and relabeling.
Evaluation set, Model switchThe security and compliance review arrives. The team now runs infrastructure.
Masking, Access and audit logs, KVKK reviewConsistent analysis starts with a stable category list, but that list ages as your processes change. Heravox builds its category tree from your organization's own data and extends it when a new topic appears, with no manual relabeling.
Language models change every month. Heravox tests new models on its own evaluation sets with records like yours; unless accuracy, consistency and cost improve together, there is no switch. When a switch happens, prompts and vocabulary are updated with it.
| Model | Accuracy | Consistency | Cost efficiency |
|---|---|---|---|
| Model in useIn use | high | high | medium |
| Candidate ARejected: consistency | high | low | high |
| Candidate BRejected: cost | high | high | low |
| Candidate CRejected: accuracy | low | high | high |
Language models are probabilistic: ask twice about the same text and you can get two categories. Heravox labels each record once and stores the label with it, so trend lines don't move when someone asks again, and every number goes down to the records behind it.
What is the main cause of complaints at the assessment step?
Ingesting multichannel data every day, matching the same customer across channels, protecting personal data and keeping it all running in your own data center is engineering work in itself. In Heravox these layers come installed; we handle updates and upkeep.
| Topic | If you build it | With Heravox |
|---|---|---|
| Source connectors | A connector per system, repaired whenever a system changes. | Already connected; we maintain them. |
| Category tree | Built by hand, relabeled whenever a process changes. | Built from your data, extended with new topics. |
| Consistency | The same text can get a different category on another run. | Labels stored with the record; trends don't drift. |
| Model changes | An evaluation and migration project for every new model. | Tested continuously, switched only when measured better. |
| Evidence | Unclear which records a summary is based on. | Every number goes down to records. |
| Customer context | Cross-channel matching and journeys are a separate data project. | Ready at customer and journey level. |
| Security and compliance | Masking, access, audit logs and KVKK are on you. | On-prem, masking, SSO, audit logs; in line with KVKK. |
| Action | Going from finding to task needs another integration. | Tasks in Jira, a CRM or Heravox; before-and-after measurement. |
| Team time | Engineering and data teams get tied to infrastructure. | Your team looks after customers. |
If customer feedback analytics is what you sell, this investment is your business.
If no platform meets a requirement and you are ready to maintain it for years.
A permanent engineering and data team for the years of upkeep, not just the first version.