Code security triage that saves AppSec hours, without the noise.
Correlate SAST/SCA/IaC findings with code context, explain likely reachability, and create review-ready fixes without suppressing real vulnerabilities.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Scanners generate duplicates and low-context findings; developers struggle to understand exploit path and remediation, while AppSec becomes a queueing bottleneck.
AppSec engineers, developers, security champions and engineering managers
Trigger to act: Finding backlog is growing, developers ignore scanner output, release gates are noisy, or an AppSec team must scale across repositories.
The result you can model before you sign.
Illustrative only: 12,000 findings/quarter × 6 minutes removed × $100 loaded hourly cost ÷ 60 = $120,000 quarterly engineering/AppSec capacity. Safety depends on critical recall and test validation.
The outcome, plainly: Correlate SAST/SCA/IaC findings with code context, explain likely reachability, and create review-ready fixes without suppressing real vulnerabilities.
Inputs in. A cited, review-ready result out. Your expert decides.
A Code-aware agent + deterministic validators. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra, selected on the client’s code/security golden set; Deterministic scanners, policy rules and tests remain authoritative; use a smaller model for labeling only. Read-only tool adapters; Sandboxed execution; Static analysis / test framework.
- Finding and rule
- code/data flow
- dependency graph
- version
- reachability
- runtime exposure
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
AppSec/developer owners approve severity, suppression, patch, merge, and exception; true-positive candidates are never silently hidden.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
50–500 repositories or 5,000–50,000 code/test tasks/month plus CI compute.
Planning assumptions, not vendor quotations. Your SAST and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. AppSec/developer owners approve severity, suppression, patch, merge, and exception; true-positive candidates are never silently hidden. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no autonomous suppression or merge; no exploit generation; no claim that a generated patch is secure without tests/review; no upload of proprietary code to unapproved endpoints.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $600 to $5,200 per month (50 to 500 repositories or 5,000 to 50,000 code/test tasks per month; CI compute), plus a $3,000 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. You need an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Snyk?
Tools like Snyk, Semgrep, and SonarQube are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
We baseline the true-positive precision first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work