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HEAD-TO-HEAD COMPARISON
Heeler vs Apiiro.
Agentic Development Security, built for the AI SDLC — versus Apiiro, code-analysis ASPM with agentic-development security. Heeler is one context engine that unifies code, dependencies, runtime, and cloud as a single model, proves every fix with a real build and CI repair to green, and confirms closure in production; Apiiro validates fixes against a risk model and ingests cloud posture from a CNAPP. Here's where Heeler advances and where the two meet.
EXECUTIVE SUMMARY
Heeler proves fixes and observes runtime; Apiiro validates and ingests.
Heeler runs one context engine that unifies code, dependencies, runtime, and cloud as a single model, and its edge concentrates in the fix loop: every fix is proven by a real sandbox build and CI repair to green, committed into the PR, prioritized by the runtime exposure Heeler observes itself, and closed only when production confirms it.
Apiiro overlaps on detection and prioritization; its AutoFix validates against a risk model and its cloud posture is ingested from a CNAPP.
THE FUNDAMENTAL DIFFERENCE
Heeler proves the fix and observes runtime. Apiiro validates the fix and ingests runtime.
Both analyze code, trace code-to-runtime, and secure agentic development. What sets Heeler apart is a single context engine that unifies code, dependencies, runtime, and cloud — so a fix is proven by a real build and CI repair to green, exposure is computed from runtime Heeler observes itself rather than ingested from a CNAPP, and closure is verified in production.
APIIRO
ASPM + agentic development security
Understand the code, guard the agent, prioritize the risk.
- →Deep Code Analysis (AI-SAST, SCA, secrets+validation, API discovery, code-to-runtime matching) + a Risk Graph for prioritization
- →Guardian Agent (Secure Prompt) steers AI coding agents in the IDE; AutoFix Agent delivers context-aware, model-validated fixes; design-phase and material-change detection
- →Ingests third-party SAST/SCA/secrets/pentest/bug-bounty findings — an ASPM aggregation layer; lifecycle, SLA, and MTTR tracking
- →Cloud posture ingested from a CNAPP (e.g. Wiz); fixes delivered as IDE/PR changes validated against risk models rather than built and CI-repaired to green
HEELER
Modeled around the running service
Prevent, Fix, and Operate on one runtime-aware model.
- →One context engine across six dimensions (code, cloud/runtime, business, ownership, threat, agent), sensor-less and read-only — runtime exposure is a native input
- →Its own detection — SAST, build-graph SCA (14 ecosystems, GitHub Actions first-class), validated secrets, agent-file governance — plus an MCP server, Agent Skills, and a CLI
- →Prioritizes by runtime reachability and internet exposure; remediation is built in a sandbox and repaired in your CI until green, committed into the PR
- →Guardrails gate new risk at the PR; workflows operate to a runtime-verified close with per-finding SLOs
VERDICT FRAMEWORK
Side-by-side, with a verdict per row.
Five states. Heeler-leaning where Heeler advances; explicit when Apiiro leads; honest about parity.
●
Heeler advantage
Heeler delivers a capability Apiiro doesn't, or in a 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.
◑
Apiiro edge
Both deliver the capability. Apiiro's implementation leads on a verifiable dimension.
○
Apiiro advantage
Explicit signal that Apiiro leads on this row.
Scorecard — 30 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ◑ Apiiro edge | ○ Apiiro advantage |
|---|---|---|---|---|---|
| Prevent | – | 2 | 3 | – | – |
| Fix | 3 | 2 | – | – | – |
| Operate | 3 | 5 | 9 | 1 | 2 |
| Total | 6 | 9 | 12 | 1 | 2 |
| Capability | Heeler | Apiiro | Verdict |
|---|---|---|---|
| Prevent · stop risk before and as it enters | |||
| Security at AI code generation | Heeler's MCP server hands the coding agent its runtime-aware context as it writes — 30+ read-only tools exposing live SAST, SCA, and secrets findings, endpoint exposure, runtime and deployment risk, guardrail status, and SLO pressure, plus named secure-development prompts — and can trigger Heeler's validated remediation with the right scope. Drop-in Agent Skills and the CLI cover the same surface locally, for Claude Code, Cursor, Codex, and VS Code. | Apiiro's Guardian Agent works from the IDE/CLI via its own MCP server and uses patent-pending Secure Prompt technology to rewrite a developer's prompt with the app's architecture, threats, and org policy — steering AI coding agents (Copilot, Cursor, Gemini) to generate secure, compliant code the first time, grounded in its Software and Risk Graph. A gen-time prevention layer; both companies are recognized in Forrester's Agentic Development Security landscape. | ✓Parity |
| CLI / local developer scanning | The heelercli runs local scans across the full surface — SAST, SCA, secrets (live credential validation), license, and malicious-package checks — with non-zero exit for pre-commit and CI, SARIF output, and policy-as-code; plus IDE reach through the MCP server. | Apiiro reaches developers locally through its Guardian and AutoFix agents in the IDE and CLI (VS Code, Cursor, Gemini Code Assist) over its MCP server — answering risk questions, guiding fixes, and applying context-aware changes in-editor, grounded in the same Deep Code Analysis the platform runs. A comparable local/IDE developer surface. | ✓Parity |
| PR guardrails & policy enforcement | Block / Warn / Observe guardrails post a native status check on GitHub, GitLab, Bitbucket, and Azure DevOps and gate PRs on new dependency vulnerabilities, dependency hygiene, SAST, secrets, and SLO breaches — scoped by runtime context (enforce only on Tier-1, production, or internet-facing services) — with a validated fix committed straight into the PR branch. Block relies on SCM branch protection to enforce. | Apiiro's Risk Graph policies comment on or block pull requests and break builds when a risky material change is introduced, and route changes that need review (threat modeling, pentest) to the right stakeholders. Risk-based PR gating; it comments/blocks and can deliver a one-click IDE fix rather than committing a validated fix into the PR branch, and gating keys on material-change and risk policy rather than a runtime-exposure scope. | ◐Heeler edge |
| Install-time package cooldown | Refuses too-new (and likely-malicious) dependency versions at install time — wired into the package manager on the developer's machine and in CI across npm, pnpm, Yarn, Bun, uv, pip, and Deno — backed by a PR-time minimum-age guardrail. | Offers a package cooldown policy (e.g. 72 hours on new versions) as part of OSS Package Reputation & Health, enforced through its platform and PR/CI flow to blunt newly published malicious packages. | ◐Heeler edge |
| 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. | Apiiro evaluates Infrastructure-as-Code changes in its Software Graph and can enforce pre-merge policy using change risk and application context. | ✓Parity |
| 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 per finding on every scan, confidence- and effort-scored, and delivered as PRs validated the same way as dependency fixes (sandbox build + CI repair). Findings needing an architectural change get written guidance instead. | Apiiro's AutoFix Agent generates context-aware fixes for SAST findings — tailored to your architecture, policies, and compensating controls — delivered as one-click changes in the IDE (via its MCP server) or as pull-request suggestions, and validated against contextual rules, risk models, and runtime exposure to avoid over- and under-correction. AI-generated and model-validated, rather than deterministic transforms proven by a build. | ◐Heeler edge |
| SCA autofix (upgrades) | Deterministic, multi-signal upgrade selection over the resolved graph — Heeler picks the version that clears the most risk with the least disruption (no new known vulnerabilities, proven adoption, cleared minimum-age cooldown), and for transitive vulns finds the smallest bump to the direct ancestor you control (npm, Go, Cargo). Every fix is built in a sandbox and repaired through your CI until green before merge. Auto-fixable across Java, Go, JavaScript, Python, and C#. | Apiiro's AutoFix Agent also remediates SCA findings with context-aware, runtime-exposure-validated changes surfaced in the IDE or as PR suggestions, tied to the owning developer. It fixes with AI + risk-model validation rather than deterministic multi-signal upgrade selection built and CI-repaired to green before merge. | ◐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 (mvn/Gradle, npm/pnpm/Yarn, pip/uv/Poetry, go build, dotnet), 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. | Apiiro validates a fix against contextual rules, risk models, and runtime exposure to prevent patches that break functionality or miss the root cause, and delivers it as a one-click IDE change or PR suggestion. That validation is model- and policy-based; the docs don't describe building the project in a sandbox and running your CI to repair the fix's own build/test failures until green. | ●Heeler advantage |
| Runtime-verified remediation | A fix isn't done until runtime proves it. Findings run Active → Fixed → Deployed with per-finding rollout tracking (affected vs resolved deployments, % deployed); closure is gated on runtime correlation confirming the vulnerable code is gone from every running deployment — verified in production, not at PR merge. | Tracks the risk lifecycle from detection to remediation or acceptance with SLA/MTTR and evidence collection (AutoManage), and maps runtime-scanner findings back to code to orchestrate fixes to SLA — but closure is tracked at the remediation/SLA level, not gated on runtime confirmation that the fix reached every deployment. | ●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. | No documented capability. | ●Heeler advantage |
| Operate · detect, prioritize, and run the program | |||
| SAST detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files — plus structural (Graphscan) and heuristic methods — from Heeler's own engine, with detection rules adapted per codebase, automatic reachability/exposure triage, and step-by-step data-flow proof. | Apiiro's native AI-SAST analyzes source with context from its software graph (code to runtime and business risk), validating true positives and filtering false positives — checking whether input is sanitized multiple layers deep in the call stack, filtering non-production and test code paths, and learning organizational sanitization patterns. A comparable context-aware static engine. | ✓Parity |
| Dependency vulnerability (CVE) detection | Build-graph SCA across 14 ecosystems that resolves the full direct-and-transitive graph itself from your manifests — and from committed lockfiles when present, without requiring one or changing your build — covering first-party and bundled dependencies too, matched with CVSS, EPSS, and known-exploited signals. GitHub Actions and reusable workflows are a first-class dependency ecosystem, scanned like any other. | Apiiro's native SCA analyzes dependencies to the leaf node — direct, sub-, and custom internal dependencies — flagging vulnerable and malicious OSS and risky licenses with software-graph and code-to-runtime context, and can also ingest third-party SCA findings. Comparable dependency coverage; it doesn't treat GitHub Actions and reusable CI workflows as a first-class scanned dependency ecosystem. | ◐Heeler edge |
| CI/CD & GitHub Actions supply chain | Treats CI/CD as a first-class supply chain: inventories every workflow and resolves referenced Actions and reusable workflows several layers deep, recording exact pinned commit SHAs. Scores action-level integrity — unpinned actions, impostor commits, typosquats, abandoned/archived sources, and compromised actions — plus token-permission, OIDC, and dangerous-trigger checks, and enforces an unpinned-action and minimum-age PR guardrail. | Software Supply Chain Security inventories CI/CD pipelines (including shadow pipelines) and enriches GitHub Actions with code context, flagging pipeline misconfigurations and weak branch protection, with guardrails at commit/PR/CI time; action SHA-pinning is recommended and enforceable as org policy. | ◐Heeler edge |
| Supply-chain hygiene beyond CVEs | Flags supply-chain risk beyond CVEs — malicious and compromised packages, OpenSSF Scorecard hygiene, unmaintained and typosquatted packages, and unpinned or downgraded dependencies — with per-package hygiene scoring. | Evaluates open-source risk beyond CVEs — malicious and unmaintained packages, typosquatting and dependency confusion, and OpenSSF Scorecard–based reputation and health (maintainers, activity, popularity). | ✓Parity |
| Secrets detection & validation | First-class secrets: full git-history scanning across all branches with commit attribution, active live validation (read-only calls to the issuing provider; an active secret is weighted as maximal impact), offline structural validation, and 700+ rules across 20+ languages; contained and gated at the PR. | Apiiro natively detects and validates exposed, hardcoded, and leaked secrets across code history, pipelines, AI tools, and documentation, validating them in production and correlating them in the Risk Graph for prioritization. Comparable first-class secrets detection with live validation. | ✓Parity |
| Dependency reachability & noise reduction | Heeler treats a dependency vuln as reachable only when two independent checks agree: runtime reachability (the library is loaded in a running deployment) and static call-graph reachability to the vulnerable functions across six ecosystems (Go, Java, Python, JS/TS, Rust, Ruby). Unreachable findings are auto-mitigated; elsewhere it uses runtime reachability and errs toward reachable. | Apiiro contextualizes open-source vulnerabilities by whether they're internet-exposed, used in code, and exploitable — combining call-flow and data-flow reachability with code-to-runtime context to cut noise. Contextual reachability, but not a strict dual-signal gate (runtime-loaded AND static call-graph to the vulnerable function) with automatic mitigation of unreachable findings. | ◐Heeler edge |
| Endpoint & API attack-surface discovery | Heeler enumerates every endpoint from source — REST, GraphQL, gRPC, SOAP, and WebSocket, plus the MCP tools/resources/prompts and Spring Boot actuator routes your code exposes — capturing each one's authentication posture and computing internet-accessibility from deployment topology, so your externally reachable, unauthenticated surface is inventoried with no runtime agents or traffic capture. | Apiiro discovers APIs from source through Deep Code Analysis and adds true runtime API endpoint matching — using machine learning to match live runtime traffic to the code APIs that serve it — inventorying the API surface with sensitive-data and exposure context. Comparable API discovery, with a runtime-matching approach. | ✓Parity |
| Cloud, runtime & business context | Heeler builds its own model of where code runs — no CNAPP required: containers, VMs, and serverless fingerprinted directly across AWS, GCP, Azure, Kubernetes, and Vercel, each deployment mapped to the exact running changeset, internet-accessibility auto-detected, service tier (1–4) and prod/non-prod classified, service-to-service and identity edges resolved, and ownership resolved to a team (Tech Lead / Security Lead). All read-only and sensor-less — because Heeler observes runtime itself, exposure and prioritization don't depend on ingesting a separate cloud tool. | Apiiro's Code-to-Runtime matching passively traces running container images back to source (GCP/Azure/AWS, no tags/labels) and enriches findings with runtime context. For cloud posture it ingests from CNAPPs (e.g. Wiz) and production context and positions itself as complementary to CNAPP — so the deeper cloud/runtime picture is correlated from an ingested CNAPP rather than a deployment model Heeler builds and observes itself. | ◐Heeler edge |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) ranks SAST and SCA findings by real exposure. For SCA, a vuln must clear a gate: reachable (runtime-loaded, plus a call-graph path where supported) AND internet-exposed (an active deployment on internet-reachable compute) AND not mitigated — fail any and it Defers, however high the CVSS. For SAST, exposure is path-driven from an internet-facing entry point, behind auth, touching sensitive data. Both are weighted by service tier and threat (KEV/EPSS; CWE class), with a per-finding SLO, re-scored continuously. | Apiiro's Risk Graph prioritizes with likelihood and impact from Deep Code Analysis, runtime context, and third-party threat databases — factoring application architecture, business nature, exploitability, whether code is applicative vs test, internet-facing, and in active development. A comparably sophisticated, context-rich prioritization engine. | ✓Parity |
| Design & material-change risk detection | Heeler gates newly-introduced risk at the pull request — comparing a branch against the default-branch baseline so only findings a change actually adds are flagged, and guardrails can block the merge. It doesn't analyze feature-request tickets before code is written, or track design/architecture material change as a distinct, compliance-oriented capability. | Apiiro detects risk at the design phase — a private LLM analyzes feature-request tickets before code is written — and continuously detects material code, design, and architecture change/drift across modules, repos, APIs, and AI assets, triggering review workflows, blocking PRs, or breaking builds only when a material change is introduced (used, for example, to satisfy SEC material-change disclosure). | ○Apiiro advantage |
| Multi-tool / third-party ASPM aggregation | Heeler is its own detection-and-fix engine. It brings native SAST, SCA, secrets, and agent-file analysis and exports findings (SARIF, API), but unifying, normalizing, and deduplicating the output of many other vendors' scanners is not its focus. | Alongside its native detection, Apiiro ingests, normalizes, enriches, contextualizes, and prioritizes findings from third-party SAST, SCA, secrets, container, and cloud tools plus pentests and bug-bounty programs — an ASPM aggregation layer over the tools you already run, from a single pane of glass. | ○Apiiro 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 |
| SBOM & dependency inventory | A live dependency inventory (with OpenSSF hygiene scoring) and CycloneDX SBOMs at five scopes — including runtime SBOMs per running service and per deployment, built from real runtime context — alongside global, application, and repository scopes. | Apiiro generates a live inventory across the software graph and multiple bill-of-materials types — SBOM, API-BOM, and AI-BOM — kept current with every code change through Deep Code Analysis. Comparable inventory breadth, with API- and AI-BOM coverage. | ✓Parity |
| Open-source license compliance | Enforces an allow/deny license policy — license-violation findings across direct and transitive dependencies, surfaced in inventory and gated at the PR with a license guardrail. | Custom license policies flag unapproved or high-risk licenses, scanning every pull request against a codified policy (failing builds on violations), with a dedicated OSS Licenses view and OSI/FSF risk detail. | ✓Parity |
| Automated response workflows & routing | A standing when-this-then-that engine across the whole estate: one trigger (new finding, fix available, compromised dependency, new secret, at-risk agent file, SLO expiring, PR merged with unresolved guardrail violations) → AND-combined conditions → up to three chained actions that share context. Actions span messaging (Slack/Teams/Google Chat/email), ticketing (Jira/Linear/GitHub/Shortcut, de-duplicated), and domain actions (Fix with Heeler Agent, Update SLO Override, Generate SBOM, Webhook), routed to the owning team (Tech Lead / Security Lead), with a plain-English builder. | Apiiro's Risk Graph policies trigger workflows on risky material changes — opening a ticket with full context assigned to the right owner (via code-owner attribution), routing to threat modeling or pentest, commenting on or blocking PRs, and breaking builds — with lifecycle tracking (SLA, MTTR) across the program. Comparable risk-based workflow automation and routing. | ✓Parity |
| SLOs & runtime-verified closure | Every tracked finding under an SLO policy carries a per-band (Urgent / Plan / Defer) SLO clock; SLO-expiring events trigger workflows, and an Update SLO Override action can extend a due date automatically. The clock only stops when the fix is confirmed Deployed at runtime (see Runtime-verified remediation), so SLO attainment reflects real production risk, not ticket state. | Apiiro tracks the full risk lifecycle from detection to remediation or acceptance, with SLA and MTTR tracking, evidence collection (AutoManage), and verification integrated at design, PR, and deployment stages. Lifecycle and SLA measurement; closure is tracked and evidence-backed rather than gated on every running deployment confirming the vulnerability is actually gone. | ◐Heeler edge |
| 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. | Apiiro scans Infrastructure-as-Code changes, maps them into its Software Graph, and manages the resulting risk through its standard application and change-risk workflow. | ✓Parity |
| 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 |
| 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. | Apiiro's Software Graph discovers PII, PCI, and PHI and maps data exposure across modules, APIs, external services, and runtime context. The resulting data paths feed change-risk analysis in the graph. | ◑Apiiro edge |
WHERE HEELER ADVANCES
Five places the difference is real.
Each maps back to the one context engine — code to cloud — and to what it proves that a code-analysis ASPM doesn't.
01
Fixes proven by a real build and CI, not validated against a model
Both platforms auto-fix — Apiiro's AutoFix validates changes against risk models and runtime exposure and delivers one-click IDE or PR fixes. Heeler goes one step further on proof: it builds every fix in an isolated sandbox with your toolchain and then, once the PR is open, runs your CI and repairs its own build/test failures with follow-up commits until green — or opens a draft PR with an explanation. A model-validated patch is not the same as one demonstrated to build and pass tests.
02
Runtime and cloud context Heeler observes, not correlated from a CNAPP
Apiiro's Code-to-Runtime matching passively traces running containers back to source, but for cloud posture it ingests a CNAPP (e.g. Wiz) and positions itself as complementary to one. Heeler builds its own deployment model sensor-less: each deployment mapped to the exact running changeset, internet-accessibility, service tier, and ownership — so exposure and prioritization run on context Heeler observes itself, with no CNAPP required underneath.
03
GitHub Actions and the CI supply chain as a first-class ecosystem
Heeler resolves the full dependency graph from manifests with no build and no committed lockfile, and treats GitHub Actions and reusable workflows as a first-class scanned dependency ecosystem — scoring pin status, provenance, and integrity (impostor commits, typosquats, abandoned sources). The CI/CD supply chain is a live attack surface that a code-and-dependency SCA alone doesn't cover.
04
Agent-file governance with a per-file malicious-intent verdict
Both platforms map the new AI supply chain — Apiiro's AI-BOM inventories models, agents, MCP servers, and skills. Heeler adds a governance engine that risk-scores each agent-instruction file (CLAUDE.md, AGENTS.md, .mcp.json, skills) 0–100 across static, LLM-intent, and external-reference risk and returns a Malicious / Suspicious / Benign verdict, CI-gated — vetting the files that now steer your code, not just cataloguing them.
05
Runtime-verified closure, not closure by lifecycle tracking
Apiiro tracks the full risk lifecycle with SLA/MTTR and evidence, and verifies across design, PR, and deployment stages. Heeler moves each finding Active → Fixed → Deployed and closes it only when every running deployment confirms the vulnerability is actually gone — per-finding SLO clocks driving the program to proof-in-production, not a lifecycle state that says a fix was applied.
See Heeler on your codebase.
A demo runs Heeler against your real repos and cloud — native detection across the surface, the runtime map, the prioritization, the validated remediation PRs, and the agent-file catalog. Apiiro and Heeler overlap more than most; where it counts, we'll show what fixes proven in CI, natively-observed runtime context, and verified-in-production closure add on top.
