Noetis Customer Intelligence · Business engine
Customer Feedback Analysis API
Analyzes customer interviews, tickets and feedback.
Answers: "Can an engine find themes in interviews and tickets with real examples?" For research, support and product teams.
Themes are tied to supplied examples. It does not connect to survey or support tools.
Available today
ImplementedImplemented: runs on the shared runtime with supplied material. One bounded development check on 2026-10-12 took 123s with 15 model calls and ended "incomplete". That is a development signal, not an evaluation. Customer access is by approved account.
What you send
- Question
- Supplied text material
- Text material up to 150,000 characters per run
What comes back
- Voice of customer report with traceable examples.
- Findings with the located quote and a support status from claim review
- Release status: released, incomplete with reasons, or failed
- Executed roles, usage and measured cost
Sample
Authored illustration. Written by Encevra to show the workflow. Not customer data and not a recorded model output.
What are customers telling us about onboarding?
Interview A (small agency): "Setup took a day, the templates saved us." Interview B (enterprise): "We needed 3 weeks and a consultant." Ticket 4412: "Import failed on CSV with commas." Ticket 4419: same CSV error. Survey comment: "Onboarding was easy." NPS survey: 41 responses out of 2,300 customers, average 38. Interview C (enterprise): "Permissions setup was confusing."
- Onboarding experience differs by segment, with quotes from each
- Repeated CSV import failures in tickets 4412 and 4419
- Survey response rate of 41 of 2,300 limits what the NPS figure shows
The real response shape is documented in the API reference.
How the workflow operates
Each role is a responsibility in the workflow, served by a third party language model. More roles are not automatically better; depth and routing decide which run.
Built with
Models this engine uses: OpenAI (GPT), Anthropic (Claude), Kimi (Kimi (Moonshot AI)). Specialists run on OpenAI models and the claim review and final brief on Anthropic models. If a provider has a temporary failure, the step can fall back to an OpenAI model, and the run records that.
Feedback Organizer
Normalize sources while preserving provenance.
Theme Analyst
Identify recurring needs and issues.
Segment Analyst
Examine differences across supported customer groups.
Contradiction Reviewer
Preserve minority views and inconsistent feedback.
Opportunity Analyst
Connect findings to evidence backed opportunities.
Evidence and deployment
Evidence
This engine reads the text you send in the request. Each released finding carries the exact quote and its character span in that text, located mechanically before release. Quotes that cannot be located are labelled Insufficient evidence and never shown as source verified. Interpretation is reviewed as a separate step, and gaps such as a missing clause describe what was searched instead of citing text that is not there.
Deployment
Runs on Encevra hosted infrastructure using the third party models listed above. Every model call and fallback is checked against the deployment policy first. Private cloud and air-gapped deployment are planned; discuss deployment requirements.
What it does not handle
- Population estimates from anecdotal frequency
- Uses only supplied material. No freshness guarantee beyond the documents provided.
- Required for every output during trials.
How to integrate
Send "engine": "customer-intelligence" with your question and material from your server. Keys stay server side.
How to evaluate it
No published result for this workflow yet. Build a small answer key from your own work and compare against a strong single model baseline. See the evaluation checklist and method.
Cost and rights: pricing and licensing. Showing outputs to your own customers needs a written agreement.
Related engines
Content reviewed 2026-10-12 by the Encevra engine team. Engine version 0.1.0 on noetis-core-1.