planned

Mission Run

Hackathon MVP: SaaS Customer Support Dashboard (Inbox + SLA + AI Summaries + Notes + Admin Settings)

Build a SaaS customer support dashboard with an inbox, SLA warning indicators, AI-written ticket summaries, internal notes, and an admin settings page.

Created: 14 Jun 2026, 7:35 am

Updated: 14 Jun 2026, 7:35 am

Repository Context

Greenfield Next.js app. Need modern UI, route handlers, local persistence first, and architecture that can later support real-time updates and role-based access.

Constraints

Keep MVP focused for a hackathon. Prioritize the inbox workflow, AI summaries, and a clear demo story. Avoid unnecessary enterprise complexity.

AI Software Execution Operating System

Primary users

Founders, product leads, architects, delivery teams, and AI-native engineering teams that need a system of record between idea and execution.

Problem solved

It transforms mission intelligence into PRDs, technical designs, engineering plans, AI execution packs, architecture maps, risk models, and traceable workflows.

Product Flow Diagram

Idea to execution intelligence

User

Submits a software idea and constraints

Mission Plan

Tasks, dependencies, owners, risks

Document Studio

PRD, TDD, engineering plan, AI pack

Mission Memory

Reusable organizational knowledge

Architecture Diagram

System components for this idea

Users

Founders + product teams

Edge

Route 53 / CDN / WAF boundary

Application VPC

Web App

Next.js App Router

API Server

TypeScript

Worker

Mission/document generation jobs

Cache

Saved docs + fast reloads

Data Plane

Primary DB

Local JSON for prototype, SQLite/PostgreSQL for production knowledge storage.

Object Storage

Generated docs, exports, artifacts

missionstasksartifactsdecisionsrisks

AI + Operations

OpenAI API

PRD, TDD, plan, AI pack

Observability

Logs, risks, decision trail

IAM / Secrets

Server-side keys + access control

Alerts

Execution and risk signals

Cloud Diagram

Deployment-ready shape

ChannelExperienceMiddlewareResources

Browser

User session

Mission UI

Dashboard + document studio

API Routes

Validate + orchestrate

Storage

Missions + cached docs

AI Client

Codex / external tools

AI Pack

Portable execution context

Doc Engine

Timeout + local fallback

OpenAI

Optional enrichment

Security posture

Keep API keys server-side, validate payloads, and preserve audit logs.

3 risk signals

Risks become visible before execution moves to tools.

Mission Document Studio

Export mission intelligence

Turn the mission into production-ready documents for executives, engineers, delivery teams, and external AI execution tools.

Choose a document type to generate an export-ready artifact.

Traceability Map

Why every task exists

This replaces vague “AI said so” planning. Each path shows which goal, requirement, task, architecture choice, or risk explains the work.

goal

Build a SaaS customer support dashboard with an inbox, SLA warning indicators, AI-written ticket summaries, internal notes, and an admin settings page.

requirement

Define MVP scope, user stories, and demo script

task

Define MVP scope, user stories, and demo script

requirement

Bootstrap Next.js project structure and tooling

task

Bootstrap Next.js project structure and tooling

requirement

Design data model and local persistence layer

task

Design data model and local persistence layer

requirement

Implement API route handlers for tickets and notes

task

Implement API route handlers for tickets and notes

requirement

Inbox UI: ticket list with filters, sort, and status

task

Inbox UI: ticket list with filters, sort, and status

requirement

SLA computation + warning indicators

task

SLA computation + warning indicators

requirement

Ticket detail UI: conversation, metadata, and internal notes

Trace paths

Kept because traceability is the product moat; renamed from relationships for clarity.

Build a SaaS customer support dashboard with an inbox, SLA warning indicators, AI-written ticket summaries, internal notes, and an admin settings page.drivesDefine MVP scope, user stories, and demo script

Define MVP scope, user stories, and demo scriptsatisfied byDefine MVP scope, user stories, and demo script

Build a SaaS customer support dashboard with an inbox, SLA warning indicators, AI-written ticket summaries, internal notes, and an admin settings page.drivesBootstrap Next.js project structure and tooling

Bootstrap Next.js project structure and toolingsatisfied byBootstrap Next.js project structure and tooling

Define MVP scope, user stories, and demo scriptunblocksBootstrap Next.js project structure and tooling

Build a SaaS customer support dashboard with an inbox, SLA warning indicators, AI-written ticket summaries, internal notes, and an admin settings page.drivesDesign data model and local persistence layer

Design data model and local persistence layersatisfied byDesign data model and local persistence layer

Define MVP scope, user stories, and demo scriptunblocksDesign data model and local persistence layer

Bootstrap Next.js project structure and toolingunblocksDesign data model and local persistence layer

Build a SaaS customer support dashboard with an inbox, SLA warning indicators, AI-written ticket summaries, internal notes, and an admin settings page.drivesImplement API route handlers for tickets and notes

Mission Decision Log

Explain the important choices

Use Mission Control as the documentation system of record

The mission needs traceable planning artifacts before execution moves into external tools.

Tradeoffs

Improves clarity and handoff quality, but requires users to maintain mission context.

Alternatives

Unstructured chat logs, standalone docs, tickets, or ad hoc planning notes.

Mission Memory

Reuse organizational knowledge

Compare MissionsPlanned capability, not shown as an executable action yet. Current executable memory action is duplicate.

Mission Steps

Task Timeline

8 tasks

Task 1

Define MVP scope, user stories, and demo script

queued

Write a one-page MVP spec: primary persona (support agent), core flows (inbox triage, view ticket, see SLA warnings, read AI summary, add internal note, basic admin settings). Define demo narrative and success criteria. Explicitly defer real-time, RBAC, and external integrations to future.

Owner: plannerDependencies: 0

Task 2

Bootstrap Next.js project structure and tooling

queued

Create greenfield Next.js (App Router) structure with TypeScript, ESLint/Prettier, Tailwind + component library (e.g., shadcn/ui), and basic layout shell. Add env handling for AI provider key (stub-friendly). Set up absolute imports and a minimal CI script (lint + typecheck).

Owner: builderDependencies: 1

Task 3

Design data model and local persistence layer

queued

Define minimal schema for tickets, customers, messages, internal notes, SLA policy fields, and settings. Implement local-first persistence using SQLite (recommended) or file-based JSON via a simple repository pattern. Provide seed data generation for a realistic inbox and repeatable demo.

Owner: builderDependencies: 2

Task 4

Implement API route handlers for tickets and notes

queued

Create Next.js route handlers for: list tickets (filters/sort), get ticket detail, update ticket status/assignee, create internal note, list notes. Keep it simple (no auth), but structure for future RBAC by centralizing request context and repository access. Add basic validation (zod) and consistent error format.

Owner: builderDependencies: 1

Task 5

Inbox UI: ticket list with filters, sort, and status

queued

Build inbox page with a performant ticket table/list: unread/open/pending/solved, customer name, subject, last update, and SLA indicator. Add simple filters (status, assigned-to-me mock, search) and default sort by SLA urgency / last updated. Make each row navigable to ticket detail.

Owner: builderDependencies: 2

Task 6

SLA computation + warning indicators

queued

Implement SLA logic (e.g., first response due, next response due, breach state) computed from ticket timestamps and status. Expose computed fields through API and display clear visual indicators (e.g., at-risk, breached) in inbox and ticket header. Keep policy configurable via settings but minimal for MVP.

Owner: builderDependencies: 3

Task 7

Ticket detail UI: conversation, metadata, and internal notes

queued

Create ticket detail view: customer + ticket metadata panel, message thread, internal notes panel with add-note form, and basic actions (change status). Use server components where appropriate and client components for interactive actions. Ensure fast navigation from inbox to detail and back.

Owner: builderDependencies: 2

Task 8

AI-written ticket summaries (generation + caching)

queued

Add an API endpoint to generate a concise ticket summary from message history (and optionally recent notes). Implement provider abstraction (e.g., OpenAI/Anthropic) with a stub mode for demos. Cache summary per ticket with an updatedAt to avoid regenerating unless messages change. Add UI to view summary and a button to regenerate.

Owner: builderDependencies: 3

Audit Trail

Execution Log

1 logs

Mission plan generated successfully.

success

14 Jun 2026, 7:36 am

Mission Outputs

Artifacts

1 artifacts

Mission Plan

plan

This plan targets a hackathon-ready MVP with a tight demo story: open the inbox, triage using SLA warnings, open a ticket, read an AI-generated summary, add an internal note, and tweak SLA/AI behavior in settings. The execution starts by locking scope and the demo script to prevent overbuilding. Next, it establishes a clean Next.js App Router foundation with a modern UI kit and a local persistence layer that mimics production shape (tickets/messages/notes/settings) while staying simple. With route handlers in place, the team can build the inbox first (the core workflow), then layer in deterministic SLA calculations and clear at-risk/breached indicators that drive urgency in the UI. The ticket detail view focuses on the agent experience (thread + internal notes + status changes), then AI summaries add the “wow” moment via a generation endpoint with caching and a stub mode to avoid demo fragility. Admin settings remain minimal but functional, directly controlling SLA thresholds and AI behavior. Final steps emphasize demo polish and stability—seed data that highlights key scenarios, solid loading/empty/error states, and a small quality pass. Future support for real-time updates and role-based access is enabled through lightweight architecture guardrails (service/repository boundaries and request context placeholders) without adding enterprise complexity to the MVP.

Run the mission to generate executive summary, risks, architecture, database, and handoff guidance.