Blueprint Advanced

Build a CRM with AI

The full path to a CRM that fits your sales process — define the contacts, deals, and pipeline, model the data that ties them together, then build the roles, integrations, and pipeline UI, and ship.

Overview

A CRM is not a generic app — it's a specific data model with a sales process running through it: contacts and the accounts they belong to, deals moving through pipeline stages, the activities that push them forward, and the lifecycle from lead to customer. Build it like a blank SaaS and you get a database with forms; build it as a CRM and the contacts/accounts/deals/pipeline model is the spine everything hangs off. This blueprint builds that spine deliberately. It pins what your sales process actually needs, cuts it to a first release, then models the CRM data (the step that makes or breaks it), designs the API around contacts and deals, builds the roles that decide who sees whose pipeline, wires the email and lead-source integrations a CRM lives on, designs the pipeline-and-contact UI, and ships. It is specifically a CRM — not a SaaS MVP with a different label, not an internal admin tool, not a two-sided marketplace — so every stage serves the customer relationship and the sales workflow. Each stage is a NewPrompt playbook you can run on its own; together they carry a CRM from a sales process to a platform your team works deals in. You own the process; the blueprint keeps the CRM model at the center.

The journey

Each stage runs a NewPrompt playbook, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.

  1. Define what your CRM must do

    Pin the CRM to your actual sales process — the contacts and accounts you track, the deal stages your pipeline moves through, the activities that count — so the build serves how your team really sells, not a generic template.

    Outcome Requirements grounded in your contacts, deals, and pipeline.

  2. Cut it to a first release

    A full CRM is years of features; a first release is the core loop — capture a contact, open a deal, move it through the pipeline. Separate that from everything that can wait.

    Outcome A first-release scope centered on the core sales loop.

  3. Model the CRM data

    This is the step that makes it a CRM: model contacts, the accounts they belong to, deals and their pipeline stages, and the activity history that ties them together — the relationships a sales team lives in. Get this right and the rest is plumbing; get it wrong and it's a CRM in name only.

    Outcome A contacts/accounts/deals/pipeline schema that fits the sales process.

  4. Design the API around contacts and deals

    Design the endpoints the CRM is built on — contacts, accounts, deals, pipeline transitions, activities — as a contract before code, so the UI and integrations develop against a stable surface shaped to the CRM model.

    Outcome An API contract for contacts, deals, and pipeline operations.

  5. Build roles and record ownership

    A CRM's access model is its own problem: reps see their own deals, managers see the team's, admins see everything, and record ownership decides who can touch whose pipeline. Design the roles and permissions before the data is exposed.

    Outcome Roles and record-ownership rules for reps, managers, and admins.

  6. Integrate the sales stack

    A CRM that doesn't connect to email, calendar, and lead sources is a data-entry chore nobody updates. Wire those integrations via webhooks and events — with the retries that keep a logged email or a captured lead from vanishing — so the CRM fills itself.

    Outcome Email, calendar, and lead-source integrations wired reliably.

  7. Design the pipeline UI

    Design the components a CRM is used through — the pipeline board, the contact and account views, the deal cards reps drag between stages — as a reusable system, so the interface reflects the sales process instead of a generic CRUD grid.

    Outcome A component system for the pipeline board, contacts, and deals.

  8. Ship the CRM

    Cross from built to live — readiness checked, rollback planned, monitoring in place — and put the CRM in front of the team whose deals will now live in it.

    Outcome The CRM shipped with a rollback path and monitoring.

Expected outcome

A CRM built on its real model — a contacts/accounts/deals/pipeline schema, an API and roles designed around it, the sales integrations a CRM runs on, a pipeline-and-contact UI, and the whole thing shipped — a platform your team works deals in, not a generic app with the word CRM on it.

Recommended playbooks

Playbook · Operations Workflows AI Product Requirements Workflow Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD. View Playbook → Playbook · Operations Workflows AI MVP Planning Workflow Cut a product idea down to the smallest first release that proves the core value — separate the real must-haves from everything that can wait, then define the MVP and its success signal. View Playbook → Playbook · Coding Workflows AI Database Design Workflow Design a schema on its data, not a hunch — model the entities and relationships, set the constraints that protect integrity, plan indexes around real queries, then document the schema and migration. View Playbook → Playbook · Coding Workflows AI API Design Workflow Design an API on its contract instead of discovering it endpoint by endpoint — model the resources, design the endpoints and payloads, pin the contract, then review it before code locks it in. View Playbook → Playbook · Coding Workflows AI Auth & Identity Workflow Design access control before you build it, not after a breach — choose the authentication approach, model the roles and permissions, review the design for gaps, then document the identity model. View Playbook → Playbook · Coding Workflows AI Integration & Webhook Workflow Connect systems so they don't break each other — map the integration boundaries, design the event and webhook contracts, plan retries and failure handling, then document the integration. View Playbook → Playbook · Operations Workflows AI UI & Component Design Workflow Structure a UI so it stays consistent as it grows — inventory the screens, break them into reusable components, specify the component system and its rules, then review the structure for drift. View Playbook → Playbook · Coding Workflows AI Deployment & Release Workflow Cross the gap between 'tests pass' and 'safe in production' — assess release readiness, plan the deploy and its rollback, and set up the monitoring and launch checks before you ship, not after. View Playbook →

Supporting resources

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Tip: Each stage opens its playbook — work them in order and carry the output forward.

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