# AI that builds Salesforce from your org, not from a guess.

> SyncOnAI 360 AI Engineering is org-grounded AI for Salesforce work. The chat and specialist agents work from each connected org's metadata and source, plan changes, draft them on visual builders for Flows, Apex, Lightning components and pages, validate them against the org, help repair failures and hand every change to approval as a proposal.

Source: https://synconai360.com/platform/ai-engineering

## Key facts

- **5**: Specialist agents: Admin, Flow, Apex, LWC and data
- **4**: Visual builders: Flow, Apex Logic, LWC and Lightning pages
- **0**: Changes deployed without approval
- **Every**: Reply shows its tools and sources

## Why generating Salesforce code was never the hard part

Turning a sentence into metadata is now table stakes; most tools in the market can do it. The hard part is everything around it: knowing the org, not duplicating what exists, validating against the real thing, and getting the change approved and recorded.

- **Plausible but wrong.** Generated Apex and Flows look right and reference fields, queues and record types that do not exist in your org.
- **More of the same.** Without seeing existing automation, AI adds another Flow on a trigger that already has three, and the org grows harder to change.
- **Errors found in production.** Changes copied from a chat window are tested by deploying them, and the first real validation is a failed release.
- **No line to accountability.** Nobody can say which prompt produced a production change, who reviewed it or how to reverse it.

## What does org-grounded AI mean for Salesforce?

The chat works from a copy of the connected org's metadata and source. It picks the right tool for each question: the metadata itself, the dependency map, a live SOQL query, the health score, audit findings or the Knowledge Hub, and every reply shows which tools ran and cites its sources.

Before building, it checks what already exists in the org, then drafts the change to match how the org is built: its objects, fields, queues and conventions. A question like "Change Account.Rating to a picklist. Is that safe?" is answered from your org's own dependencies, not a generic rule.

- Answers from the org's metadata, dependencies, health and audit
- Existing automation checked before anything is built
- Tools and sources shown on every reply

## How do you plan Salesforce work with AI?

Plan Mode lets the AI think a change through without being able to make it: scope the work, compare approaches and explain the plan before anything is drafted. The verified prompt library adds repeatable workflows whose fields are filled with pickers that read your org.

Specialist agents bring focused instructions and tools to each job: Salesforce Admin, Flow and Automation, Apex Engineer, LWC Developer, and SOQL and Data. Each lists its tools and whether each reads or writes; a tool can be allowed, denied or held for confirmation, and every call is recorded.

- Plan Mode with no ability to change anything
- A verified prompt library with org-aware pickers
- Specialist agents with per-tool permissions

## Which Salesforce builders does the AI work in?

Name a Flow, page or component and it opens beside the chat. The Flow builder shows everything that runs on save in order, adds fault handling in one edit and renames safely. Apex Logic edits Apex as logic or code and refuses any edit that would not compile. The Component Builder configures Lightning Web Components from a library of more than 700. The Lightning Page Builder draws pages as they render, with X-Ray and persona preview.

The AI's edits appear in each builder as previews you accept or reject, every edit is versioned and restorable, and each builder explains the thing in plain sentences before you edit it.

- Flow, Apex Logic, LWC and Lightning Page builders
- AI edits as previews you accept
- Every edit versioned and restorable

## How are AI-built changes validated and repaired?

Every change is validated against the connected org before it is offered, so the errors you see are real Salesforce errors, not guesses. The change becomes a proposal with named pre-flight checks and its blast radius, and a proposal that fails a check cannot be deployed.

When a deploy fails, Salesforce's reason is shown and Fix in chat opens the conversation with the error so the AI can repair the change and propose it again. Data-changing Apex run from the chat waits for you to press Allow, and production always needs an admin other than the author.

- Validated against the org before approval is asked
- Fix in chat from any failed deploy
- Data-changing Apex waits for Allow
- Production approved by a second admin

## How do teams keep the AI and each other consistent?

Org rules set what the AI must respect in each org: naming conventions, forbidden patterns and the preferred type of automation. Every change the chat or an agent drafts is built inside those rules, so a team's conventions apply to AI-built work as well as their own.

Agents can also suggest something worth remembering, such as a convention they noticed, but nothing is remembered until a person approves it, and memories never cross into another workspace. Standards and decisions recorded in Pages are searchable by everyone, including the AI.

- Per-org naming conventions and forbidden patterns
- A preferred automation type the AI follows
- Memories saved only with approval

## From a request to an approved change

1. **Ask or plan.** Describe the change, or plan it first in Plan Mode.
2. **Ground.** The AI reads the org, checks what exists and shows its tools.
3. **Build.** The change is drafted on the right builder as a preview you accept.
4. **Validate.** It is validated against the org and saved as a proposal with checks.
5. **Approve.** A second admin approves production; failures go back to chat to fix.

## Salesforce AI, generic versus org-grounded

| Without | With SyncOnAI 360 |
|---|---|
| Code written for a demo org | Changes built to your org's own metadata |
| New automation on top of old | Existing automation checked first |
| First validation is a failed release | Validated against the org before approval |
| One assistant for everything | Specialist agents with their own permissions |
| No record of what produced a change | Receipts that include the request behind each deploy |

## Frequently asked questions

### What is AI Salesforce engineering?

Using AI to plan, build, validate and repair Salesforce changes. In SyncOnAI 360 the AI works from the connected org's metadata and source, builds on visual builders, and turns every change into a validated proposal for approval.

### How is this different from Claude or Cursor on their own?

General assistants can write Salesforce code but do not hold your org's metadata, dependency map, validation, approval path, receipts or rollback. SyncOnAI 360 provides those, and Claude or Cursor can still read your orgs through its read-only MCP connection.

### Can the AI deploy changes on its own?

No. It drafts and validates. Every change is a proposal, sandbox deploys go straight through for testing, and production needs an approval from an admin other than the author.

### Which models does it use?

Models from Anthropic and OpenAI, chosen in the workspace's settings. Chat and agents can use different models, and neither provider trains on API traffic.

### What can the AI build?

Flows, Apex classes and triggers with tests, Lightning Web Components, Lightning pages, validation rules, SOQL queries and other configuration, each opened in the matching builder for review.

### What happens when an AI-built change fails?

Salesforce's reason is shown on the deploy, and Fix in chat opens the conversation with the error so the change can be repaired and proposed again.

### Does the AI learn our conventions?

It builds to the conventions visible in your org, and agents can suggest memories such as naming conventions. A memory is saved only after a person approves it, and stays within your workspace.

### Do we need our own AI key?

Only on the Free plan, which runs on your own Anthropic or OpenAI key. Paid plans include AI, and you can still bring your own key if you prefer.

### Is AI engineering included on every plan?

Yes. Every plan includes every feature; plans differ in users, production orgs and whether AI is included.

### Can the AI follow our naming conventions?

Yes. Org rules set naming conventions, forbidden patterns and the preferred automation type for each org, and AI-drafted changes are built inside them.

### Can the chat use tools from other companies?

Yes. Admins can add another company's tool server, such as Atlassian for Jira and Confluence, and every outside tool call is recorded in the activity log.