How We Built Clove, an AI Project Management SaaS That Plans, Staffs and Schedules Work

Anuj Kumawat
AI/ML Engineer, THE TISA
Review Focus: OpenAI API integration, AI-powered project planning, structured outputs, workflow validation, and AI architecture.

Nishant Agarwal
Full Stack Developer, THE TISA
Review Focus: Full-stack SaaS architecture, Node.js and Express APIs, MySQL database design, real-time collaboration, performance, and application security.

Ask a project manager at any growing US company what eats their week, and the answer is rarely “the work.” It’s everything around it: turning a client’s goals into milestones, guessing deadlines, figuring out who’s free, then chasing people for updates until launch day.

That’s the problem Lane Snuffer set out to solve with Clove, a project management platform for startups, agencies and software teams. We built it for him in four months with three developers. Then we integrated the OpenAI API so Clove’s AI does the planning grunt work. It breaks projects into milestones and sprints, draws the workflow, sets deadlines, builds the team, assigns tasks and sends reminders.

This is the full story: the business problem, the six AI features, how they change a team’s week, how we engineered them, and what it all cost in time. If you run a SaaS company or an agency and are weighing AI integration services, this is a practical look at AI integration for SaaS platforms: what we built, where OpenAI fits, and what stays in your own code. For the one-page summary, see the Clove case study page.

Clove at a glance

The short version: Clove’s OpenAI-powered planning layer takes a project brief and a list of team members and returns a plan a manager can approve in minutes: milestones, sprints, a workflow chart, deadlines, a team and assigned tasks. It works because every task, skill and status change lives in one structured system, not five apps.

What problem was the client trying to solve?

Lane Snuffer wanted to launch a project management product for teams that had outgrown spreadsheets but didn’t want the setup weight of enterprise tools. His target users were US startups, agencies and software teams juggling several projects and clients at once.

The problem he kept seeing was scattered context. Tasks lived in spreadsheets, decisions lived in email and chat, and status lived in someone’s head. That pattern is common: Atlassian’s State of Teams 2025 survey of 12,000 knowledge workers and 200 executives found teams lose about a quarter of their time searching for information.

Scattered context is also the reason most AI features in project tools disappoint. A model can’t summarize a sprint, flag a delay or suggest an owner if the sprint, the delay and the owner are spread across five apps.

Six problems we heard in discovery

The last row shaped the whole build. Clove had to be a multi-tenant SaaS: one platform, many customer workspaces, each with its own data and permissions.

What did THE TISA build?

Building Clove meant connecting several parts of the product that teams rely on every day. We worked on everything from the interface and project workflows to the backend, database, and real-time collaboration. The challenge wasn’t getting each feature to work independently. It was making sure tasks, sprints, permissions, and team updates worked together as one reliable SaaS platform.

Two modules matter most for AI. The activity log records every meaningful change as structured data. The workflow-rules engine already decides who gets a task and when a status moves. Both feed Clove’s AI features, covered next.

What AI features did we build into Clove?

Clove has six live AI features, all running on the OpenAI API, and they work as one planning assistant rather than six separate tricks. A manager describes the project. Clove drafts the milestones, sprints, workflow, deadlines, team and assignments. The manager reviews and approves it all in one pass, and reminders keep the sprint on track after that.

How Clove’s six AI features work together, from brief to live sprint

A project brief and the team’s skill profiles go in. The AI layer drafts the plan and the staffing. Nothing goes live until a manager approves it. When a task slips during the sprint, the deadlines re-plan and reminders adjust.

1. AI sprint planning and milestone breakdown

What it does: A manager types a goal like “Launch a client onboarding portal by March 15.” Clove drafts milestones (design, build, QA, launch), splits each into epics and stories, estimates them in story points and packs them into sprints that fit the team’s real velocity.

Why it matters: Big, vague tasks are where projects go wrong. In a McKinsey and University of Oxford study of 5,400+ IT projects, large projects ran 45% over budget and 7% over time while delivering 56% less value than predicted. One fix it describes is breaking every activity down to under 20 person-days and four weeks. Clove’s AI does that breakdown by default.

How it works: The AI reads the brief, the project template and the team’s past velocity (Clove teams average 42 points a sprint), then proposes a plan. Every item is a normal, editable task, not a separate AI document.

2. Automatic flowchart design

What it does: From the approved milestones, Clove draws the project as a flowchart: phases, hand-offs, approval steps and which tasks block which.

Why it matters: Clients and executives rarely read a backlog. A one-page flow shows them the shape of the project and where it can stall, which makes kickoff calls shorter and approvals faster.

How it works: The chart is generated from the same task dependencies the board uses. Move or re-link a task and the flowchart updates, so it never drifts out of date the way a hand-drawn diagram does.

3. Automated deadlines

What it does: Clove sets a due date on every task from its estimate, its dependencies, the sprint calendar and the assignee’s other work. When one task slips, it shows which later dates move with it.

Why it matters: Hand-set deadlines are usually optimistic, and nobody updates the downstream ones when something slips. Automated dates are honest about capacity and keep the whole plan consistent.

How it works: The deadline engine walks the dependency chain from the milestone date backward. Managers can lock any date, and the AI plans around it.

4. Smart reminders

What it does: Clove nudges task owners before a deadline, reminds reviewers when work is waiting on them, and escalates to the project manager only when something is actually at risk.

Why it matters: Most reminder systems spam everyone, so people mute them. Clove stays quiet on tasks that are on track and speaks up when a blocker threatens a milestone. That replaces the daily “any updates?” messages managers send.

How it works: Reminders run off the activity log and the deadline engine, through the same notification system as comments and mentions (in-app and email).

5. AI task assignment

What it does: When a task is created or a sprint is planned, Clove suggests the best owner based on skills, current workload, availability and who worked on related tasks.

Why it matters: On a busy team, the same experienced developer often gets assigned the difficult tasks simply because everyone knows they can handle them. The problem is that their workload keeps growing while other capable team members have availability. Clove’s AI-assisted assignment helps managers look beyond familiarity by considering skills, existing work, and capacity before suggesting an owner.

How it works: Each member has a skills profile (for example React, QA, copywriting or client calls). The AI matches task requirements to profiles, then checks open story points per person so no one is pushed over capacity. It builds on the rule-based assignment Clove already had.

6. Skill-based team building

What it does: For a new project, Clove proposes a team from the workspace’s members based on what the project needs and who has those skills and the time. It also flags gaps, like “no one with QA capacity in sprint 2.”

Why it matters: Agencies and multi-project companies staff projects from a shared pool. Picking a team usually means a spreadsheet and a few Slack threads. Clove does it in one step and explains its choices.

How it works: The AI compares the skills the plan needs (from the milestone breakdown) with member profiles and their booked capacity across all active projects in the workspace.

How does AI integration for SaaS platforms work in Clove?

We integrated the OpenAI API as a planning engine behind Clove’s own backend, not as a chatbot on the side. OpenAI reads the brief and drafts structured proposals. Clove’s code checks permissions, calculates dates and capacity, and saves the result. The model never touches the database.

The hard part was never getting a model to list tasks. It was turning a loose project brief into reliable, editable records without breaking tenant permissions, overloading anyone’s sprint, or producing dates that look plausible but can’t work.

From project brief to live sprint, step by step

1. Request. A signed-in manager enters a brief, constraints and a target date in the React app.
2. Authorize and gather context. The Express API checks workspace and project access, then loads only what the job needs: the project template, sprint calendar, member skills and open workload.
3. Structured generation. The server calls the OpenAI API with instructions and a JSON schema. With OpenAI’s Structured Outputs, the response always matches that schema: milestones, tasks, dependencies, required skills and suggested owners.
4. Validate in code. The backend checks task types, project links, dependency cycles, valid members, permissions and capacity. Dates are calculated by Clove’s scheduling logic, not guessed by the model.
5. Human review. The manager edits, removes or approves items. Nothing changes until they approve.
6. Commit and broadcast. Approved items are saved through the same services as manual edits, logged in the activity history and pushed to the team over Socket.IO.

A simplified version of the generation step:

// Simplified: the planning service, server-side only
const response = await openai.responses.create({
  model: process.env.OPENAI_MODEL,
  input: buildPlanningPrompt(brief, scopedContext),   // only permitted data
  text: {
    format: { type: 'json_schema', name: 'sprint_plan',
              schema: sprintPlanSchema, strict: true }
  }
});

const proposal = JSON.parse(response.output_text);
const checked  = validatePlan(proposal, project);   // cycles, members, capacity
const dated    = scheduleTasks(checked, calendar);  // deterministic dates
return saveAsDraft(dated, user);                     // manager approves later

What OpenAI does, and what Clove’s code controls

Why we split the work this way

Letting the model schedule everything looks great in a demo and is hard to trust in production. So we went hybrid: OpenAI handles the fuzzy part, understanding what a brief means. Clove’s code handles anything with a right answer, like dates, capacity and access. The same thinking applies to context. Sending a whole workspace history to the model might feel smarter, but it leaks more data than the task needs. We send only the records relevant to the request. It took more engineering, and it gave us testable rules, a clear audit trail and managers who stay in control.

Engineering controls we built in

• OpenAI is called only from the server, and API keys never reach the browser.
• Structured Outputs fixes the response shape. Business rules are still checked separately in code.
• Tenant scoping and permission checks run before any data is sent to the model.
• Each call has a timeout, a retry limit and an idempotency key, so a retry never creates duplicate tasks.
• Generating a proposal and changing records are separate steps.
• AI-assisted changes are logged as “suggested by AI, approved by [user],” without storing more prompt content than troubleshooting needs.
• Before release, planning output was tested against realistic sample briefs for completeness, correct dependencies and safe assignments.

The OpenAI calls sit behind one internal planning service, so moving to a newer model doesn’t mean rewriting features. We explain why that matters in our vendor-agnostic AI guide. The event-driven triggers come from our AI workflow automation work, and the planning prompts and schemas follow the patterns we use in generative AI development.

What the AI never does on its own: delete work, change permissions, close sprints, or publish a plan without a manager’s approval.

Developer’s Perspective: Making OpenAI Work Reliably Inside Clove

Anuj Kumawat – AI/ML Engineer, THE TISA

“The challenging part wasn’t getting OpenAI to generate a project plan. It was making sure that plan could actually work inside Clove. A model can suggest milestones and task assignments, but it doesn’t automatically understand every dependency, team constraint, or permission rule.

We approached this by separating AI-generated recommendations from application-level decisions. OpenAI handles interpreting project requirements and producing structured suggestions, while Clove’s backend validates dependencies, workload, and scheduling before anything reaches the project board.

That separation was important because we wanted AI to assist project managers without taking away their control. A generated plan still needs to be practical, editable, and consistent with the way the rest of the platform operates.”

How does Clove change the way a company works?

The biggest change is where managers spend their time. Planning, staffing and chasing shrink. Judgment calls, client conversations and unblocking people grow.

That shift matters because coordination is the real cost center in knowledge work. In Asana’s Anatomy of Work Index 2021, a survey of more than 13,000 knowledge workers across seven countries including the US, people spent 60% of their day on “work about work” instead of the job they were hired for. The same report found US workers missed 36% of their deadlines each week.

A project kickoff, before and after

Before Clove: A new client project lands on Monday. The project manager spends the day writing a plan in a doc, breaking it into tasks in a spreadsheet, and messaging leads to ask who’s free. Deadlines are best guesses. By Wednesday the team has a plan, and by the second week half the dates are already wrong. Nobody updated the tasks downstream.

With Clove: The manager pastes the brief into a new project. Clove drafts milestones and sprints, a workflow chart for the client, deadlines that fit real capacity, a proposed team and assigned tasks. The manager adjusts a few items and approves. The team starts the same day. When a task slips, the dates that depend on it move, and only the people affected hear about it.

What changes for each role

How does AI built into the platform compare with other approaches?

AI built into the data model can plan and staff work. A chatbot bolted on the side can only talk about it. Here’s how the common options compare for the six jobs Clove automates.

When this approach is not the right fit: If your team is small, runs one project at a time and doesn’t sell software, a standard tool plus good habits will serve you better. Built-in AI pays off when you staff many projects from a shared pool, or when the planning engine is part of the product you sell, as it is for Clove.

How does Clove’s architecture make AI integration possible?

Clove is AI-ready because every change goes through one API, one permission check and one data model. An AI feature uses that same path. It doesn’t need a side door into the database.

Clove architecture · one API, one permission check, AI on the same path

A request from the board passes the REST API and the workspace and role check before it touches MySQL. Task changes go out live over Socket.IO to authorized users only. The OpenAI API layer receives only data the requesting user is already allowed to see.

Four integration decisions that support AI

1. One REST API for every action. The web app, notifications and any future integration (Slack, GitHub, an AI service) call the same endpoints. Nothing writes to the database another way.
2. Permissions enforced on the server, not in the UI. Access is checked by workspace, project and role on every request. An AI feature can’t expose a task to someone who couldn’t open it themselves.
3. A structured activity log. Every status change, assignment, comment and file action is stored with who, what and when. That gives AI a clean history to summarize or learn from, instead of scraping free text.
4. Scoped real-time events. Socket.IO pushes updates only to users with access to that workspace. AI suggestions can travel the same route, so they show up on the board as work happens.

How does Clove model projects, tasks and sprints?

Clove uses one flexible work-item table for all five task types, tied together by parent links and scoped to a workspace. That one decision keeps the board, backlog, reports, search and AI features reading from the same place.

The hierarchy teams see looks like this:

We could have created separate database tables for epics, stories, bugs, tasks, and subtasks. But that would have made everyday operations harder to maintain. Each new report, filter, or AI workflow would need to handle five different structures. Since these work items share most of the same information, a common structure made more sense. It also gave Clove one consistent way to organize and retrieve project data.

A simplified version of the core relationships, for illustration (not Clove’s production schema):

CREATE TABLE work_items (
  id            BIGINT PRIMARY KEY AUTO_INCREMENT,
  workspace_id  BIGINT NOT NULL,          -- every row is tenant-scoped
  project_id    BIGINT NOT NULL,
  parent_id     BIGINT NULL,              -- epic > story > subtask
  sprint_id     BIGINT NULL,              -- NULL = in the backlog
  type          ENUM('epic','story','task','bug','subtask') NOT NULL,
  status_id     BIGINT NOT NULL,          -- custom status per project
  assignee_id   BIGINT NULL,
  priority      TINYINT NOT NULL,
  story_points  SMALLINT NULL,
  due_date      DATE NULL,
  title         VARCHAR(255) NOT NULL,
  created_at    DATETIME NOT NULL,
  updated_at    DATETIME NOT NULL,
  INDEX idx_board (workspace_id, project_id, sprint_id, status_id),
  INDEX idx_assignee (workspace_id, assignee_id, status_id)
);

Multi-tenancy in practice. Every table carries workspace_id, and every query filters by it before anything else. The composite indexes start with workspace_id for the same reason: one customer’s board query never scans another customer’s rows. This is the cheapest way to run many customers on one database, and it’s simple to audit.

Task dependencies are modeled as links between two items (item A blocks item B), so a task can depend on work in another epic without breaking the parent hierarchy.

How do custom workflows and statuses work?

Each project in Clove can define its own statuses and board columns. A design agency can run Brief, Design, Client Review, Approved. A dev team can run Backlog, In Progress, Code Review, QA, Done.

Flexible statuses create a reporting problem. If every project names “done” differently, how do you count completed tasks across a workspace? The answer is to give every custom status a fixed category underneath it:

Teams name and order their columns however they like. Reports, the 84% completion rate, velocity and any AI summary read the category, so the numbers stay comparable across every project and every customer.

Project templates package a set of statuses, columns and default task types. A new project can start from “Scrum software team” or “Agency client project” instead of a blank board, which shortens onboarding for new workspaces.

How does the Kanban board stay fast with drag-and-drop?

A card move in Clove updates the screen instantly, saves the new status and position in one API call, and tells everyone else on the board. Getting all three right is the hardest frontend problem in any project management product.

1. Instant feedback (optimistic UI). When a user drops a card, Redux updates the board state right away, before the server replies. If the save fails, the card snaps back and the user sees an error. Without this, every drag feels laggy.

2. One save for status and position. Moving a card from “In Progress” to “Code Review” changes two things: its status and its place in the new column. Both go in a single request, so the database never holds a half-finished move.

3. Ordering without renumbering. If cards are ordered 1, 2, 3, 4, inserting between 1 and 2 means renumbering the whole column. A common fix is to store a sortable position value with gaps between cards, so a drop only writes the moved card.

A simplified sketch of the move flow (illustrative):

// Card dropped between two neighbours in a new column
const position = (prev.position + next.position) / 2;

dispatch(moveCardOptimistic({ id, statusId, position }));   // UI updates now

try {
  await api.patch(`/work-items/${id}/move`, { statusId, position });
  // server checks permissions, saves, logs activity, emits a socket event
} catch (err) {
  dispatch(rollbackMove(id));                              // snap back on failure
}

4. Staying fast on big boards. Long columns render in pages, selectors are memoized so moving one card re-renders two columns instead of the whole board, and filters run on the server for large projects.

5. Mobile. Small screens get a simpler card and a “move to” menu, because precise dragging on a phone is frustrating.

How does real-time collaboration work in Clove?

Clove pushes task changes, comments and assignments to everyone viewing the same project within moments, using Socket.IO. The rule we built around: a real-time event must never reach someone who couldn’t open that task in the app.

How an update travels:

1. A user changes a task. The REST API checks permissions and saves the change.
2. The server writes an activity-log entry (who, what, before, after, when).
3. The server emits one event to the project’s “room”.
4. Every connected user in that room receives it, and Redux updates their board without a page refresh.

Rooms are the security boundary. When a browser connects, the socket handshake is authenticated with the user’s token. The user can only join rooms for workspaces and projects they have access to. If their access is removed, they’re removed from the room too.

A simplified sketch (illustrative):

io.use(authenticateSocket);                 // verify token on connect

socket.on('project:join', async (projectId) => {
  if (await canViewProject(socket.user, projectId)) {
    socket.join(`project:${projectId}`);
  }
});

// after a successful save in the REST API
io.to(`project:${item.projectId}`).emit('item:updated', {
  id: item.id, statusId: item.statusId, position: item.position, by: user.id
});

Keep payloads small. Events carry only what changed, not the full task. The client fetches details on demand. That keeps traffic low when a busy board has hundreds of moves a day.

Notifications use the same events. An assignment or @mention creates an in-app notification and, based on the user’s settings, an email. Real-time and email alerts both come from one event source, so they always agree.

How are roles and permissions designed?

Clove checks permissions at two levels, workspace and project, for three core roles. That’s enough for an agency to give a contractor one project without exposing every client in the workspace.

Three rules that made permissions reliable:

• Check on the server, every time. Hiding a button in React is a convenience, not security. The API refuses the action even if someone calls it directly.
• One permission function, used everywhere. The REST API, the Socket.IO room join, file downloads, search results and AI features all call the same check. Five separate checks drift apart over time.
• Search and reports respect access. A team member searching “invoice” only gets results from projects they belong to. This is the most commonly missed leak in multi-tenant SaaS.

How are sprints, velocity and burndown calculated?

Clove’s sprint reports come straight from the activity log, so a manager never compiles them by hand. Here’s how the numbers in this case study are defined, so readers can compare them with their own teams.

Backlog and sprints. Unplanned work sits in the backlog. Sprint planning moves items into a sprint with a start and end date. When a sprint closes, unfinished items go back to the backlog or roll into the next sprint, and the move is logged.

Velocity is the story points a team finishes in a sprint. Clove teams average 42 points.

Velocity = Sum of story points for items marked Done at the end of a sprint.

Burndown plots remaining story points each day of the sprint against an ideal straight line from the starting total to zero. A line that stays flat early warns a manager days before the sprint fails.

Completion, on-time and overdue rates:

Because every status change is a logged event with a timestamp, the same data also gives cycle time (how long work spends in progress) and workload per person. It’s also exactly the history an AI feature needs to predict which tasks will slip.

How do workflow automation rules work?

Clove’s automation follows a simple pattern that non-technical users can set up: when something happens, if a condition is true, then do an action. Rules remove the small, repeated admin steps that make project managers chase people.

How rules run. Every saved change already produces an activity-log event. The rules engine listens to those events, checks the conditions, and calls the same REST API a person would. Actions go through the same permission checks and show up in the activity log as “by automation”, so nothing happens invisibly.

Where AI fits. Rules are good at fixed logic: “if bug, assign to QA.” They can’t judge. They can’t tell who has capacity this week, whether a task description is clear, or which overdue task actually matters. That’s the gap AI fills in a project tool. Because the rules engine already listens to events and acts through the API, an AI step slots in as one more action type, with the same audit trail.

How does Clove handle search, filters and performance at scale?

Search and filters matter more as a workspace grows. A team with 50 tasks scrolls; a team with 5,000 searches. We built Clove for the second team from the start.

Global search looks across tasks, projects and comments, but only within projects the user can access. Saved filters let a manager keep views like “My open bugs this sprint” or “Overdue, high priority, client X” one click away. Search is debounced, so the server isn’t hit on every keystroke.

Performance work, by layer:

Public pages vs private data. Clove’s marketing pages are built for SEO with clean URLs, structured headings, FAQ schema, Open Graph tags, a sitemap and Agile and Scrum landing pages. Private workspaces, projects and team records are blocked from indexing and, more importantly, sit behind login. Noindex rules are a second layer, never the first.

How does Clove keep each customer’s data separate?

For a SaaS platform serving multiple businesses, keeping customer data separate is non-negotiable. A project manager from one company should never see another company’s tasks, files, or team information. Adding OpenAI doesn’t change that responsibility. We kept workspace permissions in the backend so AI-assisted features follow the same access rules as the rest of Clove.

What we built into the platform:

• JWT authentication, password hashing and secure session handling
• Role-based access at workspace, project and admin level
• Protected routes and REST APIs, request validation and input sanitization
• API rate limiting
• Controlled file access, so a shared file is visible only inside its workspace
• Activity and audit logs for task, file and user actions
• Noindex rules that keep private workspaces out of search, with SEO only on public marketing pages

What US buyers should ask before adding AI to a SaaS product

These controls are a foundation, not a certification. Clove’s architecture supports a later SOC 2 audit, but the platform itself does not claim one.

If you’re planning something similar, ask your development partner:

1. Where is access checked? It should be on the server for every request, including AI requests.
2. What leaves your system? Know which fields go to the AI provider and whether names or client data can be left out.
3. What does the provider keep? Review the AI provider’s data retention and training settings for the exact API you use.
4. Can you show an audit trail? Enterprise customers will ask who changed what, including AI-suggested changes.
5. Which state privacy laws apply? CCPA and similar state laws depend on your customers and data. Your legal team should confirm.

We cover these in more depth in our AI SaaS security and compliance guide. You can also read about our own security practices.

How long did it take to build Clove?

Clove went from discovery to launch in four months with three developers. That covered the full SaaS product: UX, frontend, backend, real-time layer, reports, testing and deployment. It followed the stages of our AI software development process.

1. Requirement analysis and planning. We mapped how PMs, developers, designers and agency clients actually move work from setup to done: roles, task types, sprint flow and reports.
2. UI/UX design. Our UI/UX design team built role-based dashboards, workspace navigation, simple task forms and mobile layouts.
3. Core development. Backend, REST APIs, MySQL schema and the React + Redux frontend, delivered as custom software development on a SaaS model.
4. Kanban board. Drag-and-drop linked to status updates and saved records, tuned to stay smooth on long boards.
5. Real-time collaboration. Socket.IO events for comments, assignments and status changes, sent only to users with access.
6. Testing and deployment. Functional, permission, performance and security testing before launch.

Technical challenges and how we solved them

Developer’s Perspective: Keeping Project Workflows Connected

Nishant Agarwal – Full Stack Developer, THE TISA

“One of the more demanding parts of building Clove was keeping the frontend, backend, and real-time updates consistent as tasks moved between different stages. A single change could affect the Kanban board, sprint progress, activity history, and what other team members were seeing.

We focused on keeping those updates connected through the application’s APIs and Socket.IO, while enforcing permissions at the backend. That helped us build workflows that remained responsive without losing track of the underlying project data.”

What results did Clove deliver?

The real test of Clove wasn’t whether we could launch it in four months. It was whether teams could use it to manage their everyday work. The platform now reports 1,850 monthly active users across 126 workspaces, with an 84% task completion rate. These figures show how the broader platform is being used. We evaluate OpenAI’s contribution separately through measures such as the time required to prepare and approve a project plan.

These figures cover the whole platform, not the AI features on their own. The AI’s job is to make planning, staffing and follow-up faster, and the kickoff example earlier in this article shows what that looks like day to day.

Measuring the Impact of OpenAI-Powered Project Planning

Clove’s platform metrics demonstrate how teams use the application. To understand the impact of OpenAI integration, we also evaluate how efficiently project managers can move from an initial project brief to an approved sprint plan.

Our primary measure is planning time, including the time spent reviewing and adjusting AI-generated milestones, tasks, dependencies, and assignments. This gives us a clearer picture of the workflow improvement than simply counting how many AI features the platform offers.

The comparison includes the complete planning process, not just the time required for OpenAI to generate its response. Manual planning covers preparing milestones, organizing tasks, identifying dependencies, and assigning responsibilities. AI-assisted planning also includes the manager’s review and any necessary corrections before final approval.

This helps us assess whether the integration reduces real planning work rather than simply generating suggestions more quickly.

What changed before and after Clove

What the client said

“Before Clove, managing daily work and team updates through different tools was difficult. We needed one platform that could handle the complete workflow and support different team roles. Team TISA worked on Clove according to our requirements. They stayed in touch throughout the project and handled every change properly.” – Lane Snuffer, Founder, Clove

What should US SaaS founders learn from Clove?

The main lesson: design the data, permissions and events first, and AI features become small additions instead of a rebuild. These are the six things we’d tell any founder planning AI integration for a SaaS platform.

1. Plan workflows and permissions on day one. Retrofitting roles into a live multi-tenant product is slow and risky. It’s also the first thing an AI feature depends on.
2. Log activity as structured data. “Status changed from In Progress to Done by Maria at 3:42pm” is something AI can summarize. A free-text note is much harder.
3. Send real-time updates only to the right people. The same scoping keeps AI suggestions from reaching users who shouldn’t see them.
4. Make search and filters strong early. They matter more as projects grow, and AI retrieval uses the same indexes.
5. Keep the AI provider behind one service. You’ll want to change models as prices and quality shift.
6. Pick one AI feature users already wanted. In project tools, planning, assigning and reminders beat a general chatbot because they take over work managers already do every week.

For a fuller walkthrough, see our guide on how to build an AI-powered SaaS product and our AI integration for businesses guide.

Is your SaaS ready for AI integration?

• ☐ Every action goes through your own API, not direct database writes
• ☐ Permissions are checked on the server for every request
• ☐ You log who changed what, and when, as structured data
• ☐ You can name the one task AI should make faster first
• ☐ You know which customer data may go to an AI provider
• ☐ Someone owns AI output quality after launch

If you checked fewer than four, start with an integration and data review before choosing a model.

Should you build a project management SaaS or buy one?

Build only if the software is your product or your edge; otherwise, buy. Lane built Clove because he was selling it. If you only need to manage your own team’s work, an existing tool is cheaper and faster.

Signs a custom build is worth it:

• You plan to charge other businesses for it, per workspace or per seat
• Your users have a workflow general tools handle badly, such as agency client approvals, regulated sign-offs or field teams
• You want AI features that depend on your own data model, not a generic add-on
• Data residency, customer ownership of data or white-labeling matters to your buyers

Signs it isn’t: You have no plan to sell it, your workflow is standard Scrum or Kanban, or nobody will own maintenance after launch.

What belongs in the MVP, and what can wait?

Clove’s four-month launch included everything a paying team needs on day one. Integrations and billing-heavy features came later. Use this split as a starting point for your own scope.

What drives the cost and timeline of a build like this?

The biggest cost drivers are scope and integrations, not the frontend framework.

1. Number of work-item types and workflow rules. Each one multiplies board, report and permission cases.
2. Real-time collaboration. Live boards add socket infrastructure, event design and more testing.
3. Permission depth. Two levels and three roles is moderate. Custom roles per customer is much more.
4. Integrations. Each third-party tool adds auth, sync rules and failure handling.
5. AI features. Model usage costs, prompt design, evaluation and data-handling review.
6. Compliance. A SOC 2 audit or a customer’s security questionnaire adds process work on top of the code.

For price ranges and team sizes, see our AI SaaS development cost breakdown.

How do you estimate the ROI of AI project management?

The return on AI planning features comes mostly from manager hours saved and fewer missed deadlines. You can estimate the first one in five minutes with your own numbers.

Annual savings = Managers × Hours saved per week × Loaded hourly cost × 48

A worked example, with hypothetical inputs you should replace with your own:

That number leaves out the bigger prize: on-time delivery. Remember that US workers in Asana’s survey reported missing about a third of their weekly deadlines. For an agency, a late milestone can mean a frustrated client, unbilled hours or a lost renewal. Clove teams currently complete 78% of tasks on time with an 11% overdue rate. Measure your own baseline before you build, so you can show the change after.

For SaaS founders, the math is different. If AI planning is part of the product you sell, it’s a pricing and retention lever, not just a cost saving. It’s the feature that justifies a higher tier and gives customers a reason not to switch.

What’s next for Clove?

The roadmap builds on the same foundation. These are planned features, not shipped ones:

• Natural-language search across projects, a good fit for RAG development
• Agents that act on workflow rules, such as drafting client status reports, through AI agent development
• GitHub and GitLab, Slack, Microsoft Teams and Google Calendar integrations
• Time tracking, timesheets, billing and project budgets
• Client portals, approval workflows and resource planning

For another example of the same approach, read our OpsPilot AI integration case study.

Frequently asked questions

Q1. How long does it take and how many developers do you need to build a project management SaaS?
Ans. Clove took four months with three developers, from discovery to launch. The launch included workspaces, sprints, Kanban, real-time updates, reports and role-based access. A smaller MVP can take less time, while more integrations or compliance requirements can extend the timeline.

Q2. Can you add AI to an existing SaaS product without rebuilding it?
Ans. Usually, yes, if the product has an API, server-side permissions and usable data. If these are missing, fixing them first is often more practical than adding AI to a weak foundation.

Q3. What makes a SaaS platform AI-ready?
Ans. Four things help: one API for actions, server-side permission checks, structured activity data and a clear first use case. Clove was built with these foundations.

Q4. Which AI model does Clove use?
Ans. Clove uses the OpenAI API. The calls sit behind one internal planning service, making it easier to switch to newer OpenAI models without rewriting individual features.

Q5. Does customer data go to OpenAI?
Ans. Clove sends only the data needed for a feature to OpenAI, limited to information the user is authorized to access. OpenAI API keys stay on the server and never reach the browser. According to OpenAI’s data controls documentation, API data isn’t used to train models unless the customer opts in, and abuse-monitoring logs are kept for up to 30 days.

Q6. How much does AI integration for a SaaS product cost?
Ans. The cost depends on the number of features, systems and data sources involved. Our AI SaaS development cost guide breaks down the main cost drivers.

Q7. What tech stack works well for a project management SaaS?
Ans. Clove uses React, TypeScript, Redux and Tailwind CSS on the frontend, Node.js and Express for the REST API, MySQL for relational data and Socket.IO for real-time updates. The data model and permission design matter as much as the technology choices.

Q8. What does multi-tenant SaaS mean?
Ans. One application serves multiple customers while keeping each customer’s data separate. In Clove, each customer has a workspace, and database queries and real-time events are scoped to the relevant workspace or project.

Q9. Why use WebSockets instead of refreshing or polling?
Ans. Polling asks the server for updates at regular intervals, even when nothing has changed. WebSockets, through Socket.IO, let the server push updates when changes occur, helping boards stay current without repeated polling requests.

Q10. How do you stop real-time updates from leaking data between customers?
Ans. Authenticate socket connections and allow users to join only the rooms for projects they can access. Send events to authorized rooms rather than broadcasting them to everyone, and update room access when permissions change.

Q11. Can a custom project management SaaS integrate with GitHub, Slack or Google Calendar?
Ans. Yes. A REST API foundation supports integrations with external tools. Clove has GitHub, GitLab, Slack, Microsoft Teams and Google Calendar on its roadmap.

Q12. Can AI create a sprint plan and adjust deadlines around fixed dates?
Ans. Clove’s planned workflow lets a manager enter a goal and deadline, then drafts milestones, epics, stories and sprints based on the team’s past velocity. Managers can edit and approve the plan, lock specific dates and review which later dates may shift when a task slips.

Q13. Can AI automatically assign tasks to team members?
Ans. Clove’s planned feature matches each task’s required skills with team members’ skill profiles, then considers workload and availability. Managers can accept, change or ignore the suggested assignments.

Q14. Can AI build a project team based on skills?
Ans. Clove’s planned team-selection feature compares project requirements with members’ skills and capacity across other projects. It can also flag missing skills before the project starts.

Planning AI for your SaaS product?

If your product has users, data and workflows but no AI yet, AI integration for your SaaS platform starts where it did for Clove: a short review of your API, permissions and data. That review tells you which AI feature to ship first and what it will take.

• Explore our AI integration services
• See why US teams choose THE TISA
• Browse more AI software development case studies

Talk to THE TISA about your SaaS project