Roadmap

Roadmap

The roadmap is intentionally capability-driven rather than date-driven. Security and tenant isolation are foundations, not later hardening phases.

Phase 0 — Foundation

Goal: establish a safe, reviewable platform skeleton before customer-facing automation.

Key outcomes:

• modular-monolith boundaries;
• tenant + membership model;
• PostgreSQL/Flyway baseline;
• application tenant context;
• PostgreSQL RLS;
• authentication and authorization foundations;
• append-oriented audit foundation;
• secret/integration abstraction;
• AI Action Gate contracts;
• tenant-isolation integration test harness;
• provider-neutral agent/development guidance.

Exit condition: a cross-tenant access attempt is demonstrably rejected and the application has a stable foundation for tenant-owned data.

Phase 1 — Customer memory and follow-up queue

Goal: make Dokene useful before outbound messaging automation exists.

Key outcomes:

• customer management;
• purchase-history capture;
• normalized contact identity;
• follow-up cadence / next-follow-up date;
• consent and do-not-contact state;
• deterministic follow-up eligibility;
• due/overdue follow-up queue;
• operator UI for reviewing customer context.

Exit condition: an operator can reliably see who requires follow-up and why.

Phase 2 — AI-assisted recommendations

Goal: improve the quality and speed of follow-up preparation while retaining human control.

Key outcomes:

• AiProvider abstraction;
• initial hosted-model adapter;
• structured FollowUpDecision-style contract (ADR 0015);
• Next Best Action recommendation;
• constrained message drafting;
• strict schema validation;
• prompt-injection-aware context assembly;
• recommendation rationale and editable draft visible to the operator in the follow-up workbench, with no approve/send path (ADR 0020);
• safe AI failure handling with bounded retry, privacy-safe metrics and audit (ADR 0019);
• AI quality/evaluation baseline: synthetic dataset, hard-invariant harness, opt-in live evaluation and baseline comparison (ADR 0021).

Exit condition: the system produces useful, explainable drafts without granting the model direct side-effect authority.

Exit evidence: the deterministic evaluation (./gradlew test --tests '*ai.eval*') passes with every delivered-layer hard invariant at 100% and matches the committed baseline. Before a provider/model is chosen for production, the opt-in live evaluation (./gradlew aiEvalLive) must still be run for it and its report, human rubric grades and known limitations recorded in docs/verification/issue-98-ai-evaluation-verification.md; no live baseline has been captured yet. Evaluation informs a human decision; it never triggers a rollout.

Phase 3 — WhatsApp integration with manual approval

Goal: complete the first end-to-end customer follow-up flow.

Key outcomes:

• MessagingProvider abstraction;
• Meta WhatsApp Cloud API adapter (initial provider);
• tenant integration configuration;
• provider template mapping;
• message state machine;
• explicit approval workflow;
• idempotent sending;
• verified/deduplicated webhooks;
• delivery/failure tracking;
• outbound audit trail;
• emergency outbound kill switch.

Exit condition: an approved follow-up can be sent exactly once and tracked safely through delivery/failure state.

Phase 4 — Scheduling and operational resilience

Goal: automate routine candidate generation without automating judgment prematurely.

Key outcomes:

• recurring follow-up scheduler;
• multi-instance-safe work claiming;
• retries/dead-letter or recovery strategy;
• operational dashboards/health metrics;
• provider health handling;
• rate limits and abuse controls;
• backup/restore validation;
• incident-response operational procedures.

Exit condition: scheduled processing is safe under retries, multiple instances, and transient provider failures.

Phase 5 — Controlled automation

Goal: allow selected low-risk workflows to send automatically under explicit tenant policy.

Key outcomes:

• policy modes such as MANUAL_APPROVAL and constrained AUTO_SEND_LOW_RISK;
• explicit allowlisted scenarios;
• confidence/risk thresholds where evidence supports them;
• contact-frequency safeguards;
• per-tenant automation controls;
• audit/explainability for automated sends;
• immediate tenant/global disable control.

Exit condition: automated actions remain bounded by deterministic policy and can never bypass consent or authorization.

Phase 6 — Product expansion

Potential directions, driven by validated demand:

• additional messaging channels;
• CRM/e-commerce imports;
• product recommendation relationships;
• campaign-like workflows with stronger consent/frequency controls;
• analytics on follow-up outcomes;
• additional AI providers/local models;
• mobile application;
• public API / SDKs;
• enterprise dedicated-database tenancy;
• richer Next Best Action strategies.

These are not commitments until user demand and operational evidence justify them.

Roadmap guardrails

Across all phases:

• do not sacrifice tenant isolation for delivery speed;
• do not turn AI output into direct commands;
• do not enable auto-send by default;
• do not store provider secrets in source/logs/prompts/plain application data;
• do not use message volume as the primary product-success metric;
• do not split into microservices without an evidence-based reason;
• do not bypass ADR/documentation updates when durable architecture changes.