Harden AI-generated code before it ships

Find almost-right code before it fails on the first busy day

The demo works. Then the security questionnaire arrives, and nobody on the team wrote the code or trusts it. Agents audit what your AI tool built, rank what would break in production, and fix it in small pull requests with a test for every change.

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YOU HAND OVERvendor-portal · built with an AI tool

audit · vendor-portalMap…

0/ 23Mapping the codebase · 10 of 41 files

SecurityAPI key shipped in the client bundle—

Error handlingUnhandled promise in checkout—

Data integrityTotals summed without quantities—

TestsNo tests on the payment path—

PerformanceN+1 query on the vendor list—

StructureSame pricing rule copied 4 times—

1Map

2Audit

3Harden

4Test

YOU GET23 findings closed in 9 PRsA test for every fix · no rewrite

Built by Syncfusion, serving 47,000+ organizations.25+ years of developer tools, now reimagined for the agent-first era.

47,000+Syncfusion customers

1,600+Syncfusion UI components

25+Years of Syncfusion developer tools

Syncfusion component licensing is separate.

The audit

Review AI-generated code across six risk areas

Before anything changes, the agent maps the app: entry points, data flow, and every external call. Then it checks six areas and ranks each finding by the damage it would do in production, with the file and the line.

SecuritySecrets, auth on every route, injection

Error handlingFailures that still answer “ok”

Data integrityTotals, rounding, lost writes

Find untested money-handling codeThe money paths with no test at all

PerformanceQueries in loops, unbounded lists

Duplicated business rulesThe same rule copied into four files

vendor-portal · readiness by area23 FINDINGS OPEN

Security6 found

Errors5 found

Data3 found

Tests4 found

Speed2 found

Structure3 found

Almost right

What working builds and demos still miss

AI tools write code that looks finished. The gaps hide where a demo never goes: a live key in source, a total that ignores quantity, a declined card that still says ok. The agent finds them line by line and fixes each one with a test.

01Names and types can hide missing checksNames, comments, and types are all there, so reviewers skim it.
02Demo data leaves failure paths untestedDemos use one user, one item, and a card that never declines.
03Check failure cases the demo missedReal traffic finds the missing check on the first busy day.

api/checkout.ts · generated by an AI tool✓ builds · demo works

1app.post('/api/checkout', requireAuth, async (req, res) => {

2 const stripe = new Stripe(process.env.STRIPE_KEY)

3 const total = sumLines(req.body.items) // qty × price, in cents

4 const user = await db.users.find(req.user.id)

5 const charge = await stripe.charges.create({ amount: total })

6 if (charge.status !== 'succeeded') return res.status(402).json({ ok: false })

7 res.json({ ok: true, chargeId: charge.id })

8})

1app.post('/api/checkout', async (req, res) => {

2 const stripe = new Stripe('sk_live_51H8…')

3 const total = req.body.items.reduce((s, i) => s + i.price, 0)

4 const user = await db.query(`SELECT * FROM users WHERE id = ${req.body.userId}`)

5 const charge = await stripe.charges.create({ amount: total })

6 res.json({ ok: true, charge })

7})

SECURITYA live payment key in source. Moved to the vault.

DATAIgnores quantity: three items at $10 bill $10.

SECURITYSQL built from user input. Now a parameterized lookup.

ERRORSA declined card still returns ok: true.

No rewrite

Fix the highest risks in small pull requests

The app you have is not thrown away. Each finding becomes one small pull request your team reviews and merges, so feature work keeps moving while the code gets safer.

One finding, one pull requestSmall and focused, ordered by production risk.

Capture the reported behavior in a regression testEach fix includes a test that fails before and passes after.

Recheck before releaseRerun the same six checks on demand, so the gaps don’t come back.

MERGED BY YOUR TEAM0 / 6 PRs+0 tests

#101Move secrets to the vaultSecurity · +24 −9 · +3 testsqueued

#102Require auth on 14 routesSecurity · +61 −12 · +14 testsqueued

#103Parameterize every querySecurity · +38 −30 · +8 testsqueued

#104Handle declined paymentsErrors · +42 −3 · +6 testsqueued

#105Count quantities in totalsData · +19 −4 · +5 testsqueued

#106One pricing rule, not fourStructure · +22 −71 · +4 testsqueued

The same day, twice

Dana reviews the risks in an AI-built portal

Illustrative example, not a customer result.

Here is what the work looks like today, and what changes when an AI team takes the busywork off the plate.

Without BoldCreate

With BoldCreate

09:00

The vendor portal demo lands. It was built in two weeks with an AI tool.Launch date promised

Dana points BoldCreate at the repository.5 minutes of setup

11:00

The first enterprise customer sends a security questionnaire.82 questions, no answers

10:15The audit ranks 23 findings by production risk, with file references.Fix plan ready

14:00

Engineers open the code. Nobody wrote it and nobody trusts it.Talk of a rewrite

The first fixes arrive as small pull requests, each with its own test.Reviews started

17:30

The launch slips by a quarter.3 months lost

The questionnaire has a plan: each answer mapped to a finding and its PR.Launch back on track

A rewrite avoided, and a plan the customer can see

Frequently asked questions

What do agents check when they harden AI-generated code?

The agent maps entry points, data flow, and external calls, then checks six areas: security, error handling, data integrity, tests, performance, and structure. Each finding is ranked by production risk, with the file and line.

Do I have to rewrite the app?

No. Each finding becomes one small pull request, ordered by production risk, so feature work continues while the code gets safer.

How is each hardening fix checked?

Each fix includes a test that fails before and passes after, and your team reviews the pull request before it merges.

How do we stop the same gaps coming back?

Rerun the same six checks on demand from the CLI, or let a Cloud Agent automation run them on a schedule or whenever a pull request opens.

Explore common use cases

All use cases

PR #2214 · review✓ Team standards met! Missing null check, line 42✓ Docs updatedCode review and quality gatesOne rulebook, applied to every PR.CVE-2026-1182 · lodash✗ Exploit test fails firstlodash 4.17.20 → 4.17.21✓ Patch PR, 418 tests greenSecurity fix backlogClear the findings before the audit.refunds/ · coverageBefore · 41%+58 tests written✓ After · 86%Test coverageCover the riskiest code first.

Bring the app your AI tool built

Connect a repository and review the ranked audit. Reduce production risk in small, tested pull requests your team merges.

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