What is the Customer Response Engine?

A human-in-the-loop customer-response workflow that combines structured message analysis, an approved communications playbook, explainable drafting, and human business-context review.

Does the public demo use an LLM?

No. The current public implementation uses deterministic pattern-based logic so the reasoning is inspectable. A model-backed architecture is described separately as a potential production evolution.

Does the system publish responses automatically?

No. Final approval and publication remain human responsibilities.

What does the human still decide?

Changed business circumstances, strategic appropriateness, sensitive context, legal or reputational considerations, and whether the response should ship.

← Back to Portfolio & Studio

Shipped · Open source

Customer Response Engine

Effective customer responses come from teams who can scalably interpret customer input, apply approved organizational knowledge and guardrails, explain the rationale - and decide what still requires human judgment.

The Customer Response Engine makes that workflow explicit: the system handles repeatable analysis and playbook application, while business context, sensitivity, strategic appropriateness, and final approval remain with a person.

Try the live demo View source on GitHub

How it works

1
Customer communication
2
Structured analysis - themes, sentiment, concerns, privacy signals
3
Approved communications playbook - messaging pillars, guardrails, examples
4
Draft response
5
Explanation / rationale
6
Human business-context review
7
Final response

The human–system decision boundary

System responsibilities

Interpret the incoming message.
Identify themes and intent.
Apply structured response guidance from the approved playbook.
Draft an aligned response.
Make its rationale and review considerations visible.

Human responsibilities

Changed business circumstances.
Strategic appropriateness.
Sensitive customer context.
Legal, policy, or reputational considerations.
Final approval and publication.

The system does not claim autonomy. It is a governed drafting and decision-support workflow.

What runs today

The public demo implements the full workflow for one use case, with the reasoning encoded rather than model-backed:

Supports customer-review responses: a customer message, star rating, and organization go in; a draft with its reasoning comes out.
Ships with an illustrative communications playbook implemented in a tutoring/education domain - theme detection, response pillars, and an approved-example set - so the reasoning is concrete. A real deployment supplies its own playbook keyed to its domain and voice.
The reasoning layer is deterministic and pattern-based: keyword-regex theme detection, sentiment logic combining a lexicon with the star rating, playbook pillar selection, and response-composition rules. No LLM is called.
Every draft is exposed with its rationale and review considerations for human review; a person edits or copies it before anything is sent.
Runs entirely client-side in the browser. There is no backend, no external API calls, and no customer data is stored server-side.

The public implementation keeps the decision logic inspectable while preserving the workflow and output shape that a governed model-backed implementation could use later - a live model call can replace the deterministic reasoning layer without changing the UI or the playbook interface.

Production evolution

Not implemented in the public demo

A production deployment of this workflow would layer in:

Secured model integration, with per-call cost and latency observability.
Organization-specific retrieval over playbook and brand knowledge.
Approved-response semantic retrieval across a curated example bank.
Knowledge-source governance - ownership and update tracking.
Evaluation and quality monitoring - offline eval sets, online drift detection.
Authentication and role-based permissions.
Full audit history - who drafted, who edited, who published.
CRM and support-platform integrations (Zendesk, Intercom, HubSpot, Salesforce).
Channel integrations (Google Business Profile, Trustpilot, in-app, email).
Observability - traces, spans, retries.
Privacy and data-retention controls.

A staged plan lives in the repository's production roadmap.

Potential applications

These are applications of the workflow pattern - repeatable analysis, governed playbook application, explainable drafting, and human approval - not shipped features. Each would be its own implementation with its own playbook, guardrails, evaluation criteria, and integrations.

Review responses for sites like G2 and Capterra
Customer feedback triage
Service concerns
Reputation responses
Customer support
Customer success outreach
Sales objection responses
Partner communications

Common Questions

What is the Customer Response Engine?: A human-in-the-loop customer-response workflow that combines structured message analysis, an approved communications playbook, explainable drafting, and human business-context review.
Does the public demo use an LLM?: No. The current public implementation uses deterministic pattern-based logic so the reasoning is inspectable. A model-backed architecture is described separately as a potential production evolution.
Does the system publish responses automatically?: No. Final approval and publication remain human responsibilities.
What does the human still decide?: Changed business circumstances, strategic appropriateness, sensitive context, legal or reputational considerations, and whether the response should ship.