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Heeler vs Pixee.
Heeler is an end-to-end Agentic Development Security platform that prevents, fixes, and operates — modeled around the service across code, dependencies, runtime, and cloud — versus a triage-and-remediation layer that runs downstream of your scanners. This isn't feature-for-feature; it's a category difference.
Built for the AI SDLC, not a triage step downstream of your scanners.
Heeler was built for the AI SDLC, modeled around the service — it runs the whole loop (Prevent, Fix, Operate) across SAST, SCA, secrets, supply chain, and agent files on one context engine that unifies code, dependencies, runtime, and cloud, with its own detection, runtime-aware prioritization, and remediation built and repaired in your CI until green. Pixee is a triage-and-remediation layer that runs downstream of your scanners: it ingests findings from other tools (or any SARIF), triages them with an auditable justification trail, and generates developer-reviewed fixes — it's explicitly not a scanner, and a connected scanner is required.
Note: the difference is category, not features.
Heeler runs the whole loop; Pixee triages and fixes what a scanner found.
Heeler owns the full loop — prevent, detect, prioritize, fix-and-prove, and operate to closure — across SAST, SCA, secrets, supply chain, and agent files, on one context engine spanning code, dependencies, runtime, and cloud. Pixee sits downstream of your scanners: it triages their findings and generates fixes — those two steps, and only those.
Agentic triage & remediation, downstream of your scanners
Triage findings, generate trustworthy fixes.
- →Ingests findings from 15+ scanners (Checkmarx, CodeQL, Semgrep, Snyk Code, SonarQube, Fortify, Veracode, GitLab, Trivy…) or any SARIF — explicitly not a scanner, a connected scanner is required
- →Triage of scanner findings with an auditable justification trail; remediation via open-source deterministic codemods + constrained AI, validated by an independent LLM evaluator
- →SCA remediation (exploitability-verified atomic upgrade PRs), plus container/IaC misconfig fixes; developer-reviewed PRs, never a direct commit
- →No detection, no prevention, no runtime/cloud context, no runtime-aware prioritization, no gen-time/IDE security, no SBOM, no merge-gating, no SLO — and it doesn't build/test its fixes
Context-engine native, service-modeled
Prevent, Fix, and Operate on one model.
- →One context engine across six dimensions (code, cloud/runtime, business, ownership, threat, agent), sensor-less and read-only
- →Its own detection — SAST (20 languages), build-emulation SCA (14 ecosystems), secrets — no second tool required
- →Prioritizes by runtime reachability and internet exposure; guardrails gate new risk; workflows operate to a runtime-verified close
- →Remediation makes the change and proves it: first-party code and dependency edits, built in a sandbox and repaired in your CI until green, opened as a merge-ready PR
Side-by-side, with a verdict per row.
Five states. Heeler-leaning where Heeler advances; explicit when Pixee leads; honest about parity.
Heeler advantage
Heeler delivers a capability Pixee does not, or in a fundamentally different way that changes outcomes.
Heeler edge
Both deliver the capability. Heeler's implementation is materially better on a verifiable dimension.
Parity
Both products deliver the capability comparably.
Pixee edge
Both deliver the capability. Pixee's implementation leads on a verifiable dimension.
Pixee advantage
Explicit signal that Pixee leads on this row.
Scorecard — 24 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ◑ Pixee edge | ○ Pixee advantage |
|---|---|---|---|---|---|
| Prevent | 5 | 0 | 0 | 0 | 0 |
| Fix | 1 | 1 | 1 | 1 | 0 |
| Operate | 14 | 0 | 1 | 0 | 0 |
| Total | 20 | 1 | 2 | 1 | 0 |
| Capability | Heeler | Pixee | Verdict |
|---|---|---|---|
| Prevent · stop risk before and as it enters | |||
| Security at AI code generation | Heeler's MCP server + auto-loaded Agent Skills secure code as the agent writes it — across the full surface: SAST weaknesses, secrets, vulnerable and compromised dependencies, and license and minimum-package-age policy — injecting org-specific context to steer secure generation. | No documented capability. | ● Heeler advantage |
| CLI / local developer scanning | The Heeler CLI runs local scans across the full surface — SAST, SCA, and secrets (with live validation) — for shift-left use pre-commit or in any pipeline. | The Pixee CLI is a thin client to the platform — it doesn't scan or fix locally (the open-source codemodder can apply codemods locally, but it doesn't scan). | ● Heeler advantage |
| PR guardrails & policy enforcement | Block / Warn / Observe guardrails gate pull requests on new SAST, SCA, secrets, and SLO violations — plain-English, runtime-scoped, native status checks across GitHub, GitLab, Bitbucket, and Azure DevOps — with an in-PR validated fix. | Not offered. Pixee is explicitly non-blocking — 'Pixee does not replace your existing gates'; your CI/CD pipeline decides what merges. | ● Heeler advantage |
| Software supply-chain prevention (deps) | Malicious/compromised-package blocking, typosquat detection, minimum-package-age cooldown, unpinned-dependency and unpinned-Action detection, dependency-hygiene scoring, and posture validation across ecosystems — enforced at the PR and continuously re-evaluated. | Not offered. Pixee remediates dependency CVEs a scanner already found, but has no preventive program (typosquat, minimum-age, unpinned, posture) to stop risky dependencies entering. | ● Heeler advantage |
| IaC pull-request guardrails | Heeler evaluates the Infrastructure-as-Code findings introduced by a pull request instead of failing a change because of pre-existing repository debt. It returns a native source-control check controlled by program policy: Observe records the violations and the decision that would have been made, Warn reports them without preventing merge, and Block fails the check so repository rules can require it. The same policy can move from observation to enforcement without creating separate rules or CI logic. | No documented capability. | ●Heeler advantage |
| Fix · resolve findings — code and dependencies | |||
| SAST autofix | Deterministic, strategy-matched transforms (Parameterize / Escape / Allowlist / Path-Normalize) anchored to the exact source-to-sink flow, precomputed on every scan; confidence- and effort-scored, with human review. | Deterministic codemods (AST transforms encoding OWASP/SANS patterns) auto-routed to constrained AI for novel or multi-file cases, with fix explanations and quality scores, delivered as developer-reviewed PRs. | ✓ Parity |
| SCA autofix | Deterministic, multi-signal upgrade selection over the resolved dependency graph: Heeler picks the version that clears the most risk with the least disruption — one that adds no new known vulnerabilities, is proven in real-world adoption, and has cleared your minimum-package-age cooldown — and for transitive vulns finds the smallest bump to the direct ancestor you actually control. Auto-fixable across Java, Go, JavaScript, Python, and C#. | Exploitability-gated 'atomic' upgrade PRs that bump the manifest and update downstream source together, with transitive resolution and breaking-change detection, across Python, Java, JS, and .NET. Acts on a scanner's finding and bumps to a patched version, rather than optimizing the target across risk-cleared, new-vulnerabilities-introduced, adoption, and freshness. | ◐ Heeler edge |
| Validated, merge-ready fixes (build + CI repair) | Heeler validates every fix twice: a real build in an isolated sandbox with your project's toolchain before the PR exists (compile for compiled languages, install/resolve for interpreted), then — once the PR is open — it runs your CI and repairs its own build/test failures with up to five follow-up commits until green, or hands off with an explanation. Draft PR if the sandbox build didn't pass; human review, no auto-merge. | Pixee validates each fix with constrained generation plus an independent LLM evaluator (a separate model scoring Safety / Effectiveness / Cleanliness, suppress-if-fail); it does not compile the project, run tests, or repair CI — the build/test validation is delegated to your pipeline and reviewers after the PR opens. | ● Heeler advantage |
| Policy-managed remediation memories | Heeler stores administrator-approved remediation guidance as memories in program policy and scopes each memory to an organization or repository. On later remediation runs, the Remediation Agent retrieves the applicable memories and applies the repository’s conventions and constraints when generating a fix. The guidance is centrally governed and reusable; it is not a one-off prompt, a triage-suppression rule, or generic model memory. | Pixee stores repository and organization preferences and uses pull-request outcomes, including merged as-is, merged with modifications, closed or declined, and triage overrides, to influence future remediation behavior. | ◑Pixee edge |
| Operate · detect, prioritize, and run the program | |||
| SAST detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files, from Heeler's own engine, with rules adapted per codebase and automatic triage. 20 languages. | Not offered. Pixee 'is not a scanner' and requires at least one connected SAST tool — without a scanner, it produces nothing. | ● Heeler advantage |
| SCA detection | Build-emulation SCA across 14 ecosystems (no lockfile, no build required) — direct, transitive, first-party, and bundled dependencies, plus GitHub Actions as a first-class ecosystem. | Not offered. Pixee remediates dependency findings but detects none itself — it ingests them from your SCA tool. | ● Heeler advantage |
| Secrets detection & validation | First-class secrets: full git-history scanning with commit attribution, live/active validation via per-provider parsers, scheduled re-validation, rule- and entropy-based, offline in the CLI. | Not offered. Pixee triages secrets findings from other tools and fixes a few unambiguous patterns; it is not a secrets scanner or validator. | ● Heeler advantage |
| Agent-file detection & governance | Heeler inspects pull-request changes to agent instructions, skill files, hooks, subagents, and MCP configuration, including CLAUDE.md, AGENTS.md, and SKILL.md. It combines deterministic checks, LLM-based intent analysis, and analysis of external references to identify prompt injection, tool poisoning, unsafe command execution, secret access or exfiltration, permission bypass, and related agent-control risks. The evidence and verdict are returned through Observe, Warn, or Block before the changed artifact is trusted by coding agents. | No documented capability. | ● Heeler advantage |
| Triage & false-positive reduction | Automatic triage suppresses false positives and low-impact findings and surfaces the real ones before you ever see them — across SAST, SCA, secrets, and agent-file findings — with false-positive and mitigation verdicts that persist across scans. (Whether a real finding is deployed and internet-facing then drives ranking — see Runtime-aware prioritization below.) | Three-tier triage (deterministic analyzers → AI codebase investigation → adaptive) determines exploitability at the code level — is the vulnerable path reachable in the code, are there security controls between source and sink, is it test vs production — for both SAST and SCA findings, with an auditable True / False / Won't-Fix justification trail. The exploitability is code/dataflow-based, not runtime-aware. | ✓ Parity |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) prioritizes SAST and SCA findings by real exposure — assessed differently for each. For SCA, a dependency vuln must clear a gate: is it reachable (the library loaded in a running deployment, with a call-graph path to the vulnerable function) and internet-exposed (an active deployment on internet-reachable compute), then scored by compromise and chaining. For SAST, exposure is path-driven — is the vulnerable data flow reachable from an internet-facing entry point, behind authentication, touching sensitive data. Both are weighted by service tier and threat (KEV/EPSS for dependencies; CWE class for code), with a per-finding SLO, re-scored continuously. | No documented capability. | ● Heeler advantage |
| Unified context graph | One graph unifying six dimensions — code, cloud/runtime, business, ownership, threat, and agent — sensor-less, with everything downstream (prioritization, SLOs, workflows) reading from it. Cloud/runtime and endpoint discovery are broken out in the rows below. | Not offered. Pixee's context is codebase and dataflow only — no native context engine unifying signals across the application. | ● Heeler advantage |
| Cloud & runtime context | Fingerprints running services to the exact commit, detects internet-facing exposure, and maps deployments and service-to-service connections across AWS, GCP, Azure, Kubernetes, and Vercel — sensor-less. | Not offered. Pixee has no cloud or runtime model — no deployment topology, internet-exposure awareness, or running-service mapping. | ● Heeler advantage |
| Endpoint & API discovery | Enumerates application API endpoints and schemas from source, and models per-endpoint authentication — method, protocol, provider, protected-or-not, auth chain, and token issuer. | Not offered. Pixee builds no API or endpoint inventory and models no per-endpoint authentication. | ● Heeler advantage |
| SBOM & dependency inventory | A live dependency inventory and CycloneDX SBOMs at five scopes — a global SBOM across your whole environment, application, repository, and runtime SBOMs per running service and deployment. | No documented capability. | ● Heeler advantage |
| Lifecycle, workflows & SLOs (operate the program) | Findings run Active → Fixed → Deployed with runtime-verified closure, and event-driven workflows route to the owning team (Jira/Linear/Shortcut, Slack/Teams), auto-trigger validated remediation PRs, and close SLOs only on a runtime-verified fix. | Dashboards track merge rate and remediation velocity (MTTR), and webhooks enable Jira/Slack patterns — but there's no runtime-verified closure, and no SLO enforcement engine. | ● Heeler advantage |
| Sensitive-data inventory | Heeler analyzes application source to identify the sensitive data types handled by the code and records them in the application catalog. Each data type is connected to the repositories, services, APIs, and owning teams that process it, together with business criticality and available deployment or exposure context. A reviewer can move from a data type to the applications and interfaces that handle it and evaluate that data-handling risk with the same ownership and runtime context used for other application-security findings. | No documented capability. | ●Heeler advantage |
| Infrastructure-as-Code findings | Heeler evaluates Infrastructure-as-Code against policy and creates a persistent finding for each violation. The finding retains the affected file or infrastructure resource and links it to the repository, application, owning team, business criticality, and any connected cloud or runtime object. The same finding is used for prioritization, lifecycle tracking, workflow routing, pull-request guardrails, and remediation instead of remaining a one-time CI scanner result. | No documented capability. | ●Heeler advantage |
| GitHub audit-log behavioral checks | Heeler ingests the GitHub organization audit log for each connected organization and evaluates defined patterns across the resulting source-control events. A match creates a native finding that retains the actor, action, affected repository or organization, timestamp, and matching event context. The finding enters Heeler’s ownership, prioritization, investigation, workflow, and lifecycle tracking rather than remaining a searchable or exported log event. | No documented capability. | ●Heeler advantage |
| OpenSSF Scorecard repository health | Heeler records the repository’s OpenSSF Scorecard result and each check outcome on the repository object in the catalog. The same repository view links the signal to applications, ownership, vulnerabilities, dependency health, provenance, deployment, and exposure context. Teams can inspect which Scorecard checks reduced the result and evaluate repository-maintenance risk in relation to the applications that use the repository instead of treating the score as an external badge. | No documented capability. | ●Heeler advantage |
Where an end-to-end platform beats a remediation layer.
Every one of these maps back to the context engine — not features bolted onto a scanner.
One platform, at machine speed for the AI SDLC
AI writes code faster than any review process can keep up. Heeler runs the whole loop — prevent, detect, prioritize, fix-and-prove, operate to verified closure — continuously and at machine speed on one context engine, so security keeps pace with the volume of AI-generated code. Pixee runs two steps of that loop — triage and remediation — downstream of scanners you run separately.
One product, not a scanner plus a fixer
Heeler detects and fixes on one platform, from its own SAST, SCA, and secrets engines. Pixee is explicitly 'not a scanner' and requires at least one connected commercial scanner to have anything to act on — that's a second product to buy, run, and maintain, and Pixee only sees what that scanner reports.
Fixes proven in CI, not just LLM-scored
Heeler builds every fix in an isolated sandbox and repairs its own CI failures until green — for code and dependencies. Pixee scores each fix with an independent LLM evaluator (Safety/Effectiveness/Cleanliness) but never compiles or tests it; a fix that scores well can still break your build until your CI catches it.
Prevent and detect across the whole surface
Heeler stops risk at generation and at the PR — guardrails, supply-chain prevention, agent-file vetting — and detects across SAST, SCA, and secrets with its own engines. Pixee neither prevents nor detects; it triages and fixes what a scanner already found.
Prioritize by runtime, not just severity
Heeler ranks SAST, SCA, secrets, and agent-file findings by what's actually reachable, deployed, and internet-facing across its unified code-to-cloud model. Pixee's exploitability triage operates at the code layer, but with no runtime or exposure signal it can't tell a live, internet-facing finding from a dormant one — or operate the fix through to a runtime-verified close.
See Heeler across your AI SDLC.
Heeler secures the whole AI SDLC — not just code. A demo runs it against your real repos and cloud: detection, the runtime map, prioritization, validated remediation PRs, and the agent-file catalog. If Pixee is triaging and fixing one scanner's output today, we can show the rest of the loop around it.
