AI is uncovering vulnerabilities faster than ever, and attackers use AI to weaponize them, often within a day. A backlog is no longer a place risk can wait. Burn down the backlog with deterministic fixes, exploitable first. Heeler automates security at every stage of the AI SDLC, from prompt to runtime.
More vulnerabilities than ever, often exploited within a day, and the same team to fix them. The math no longer works, and every open finding is real risk.
Stop new risk where it starts: in the agent, at the commit and on the pull request.
Every finding ranked the moment it lands, from what runs and what it reaches.
The fix version is calculated, not guessed. An agent makes the change, validates it in your CI and repairs what breaks. Your team merges it.
Heeler stops risk as code is written, ranks and routes every finding on its own, and fixes it with merge-ready pull requests. People step in only to merge and grant exceptions.
Every area reads from the same Context Engine, so each finding is validated, fixed and prevented with the same picture of your code, cloud, business and people.
Each one runs end to end without a triage meeting. Pick one.
CVE-2022-22965 in spring-beans, on checkout-api
Tier 1, production, internet accessible, function reachable
Urgent. The SLO is 14 days.
CODEOWNERS names @payments-team
Ticket in PAY, message in #payments-security
Auto-fix opens the upgrade PR, validated in CI
Add PDF export to billing-service
Heeler MCP tells the agent your guardrails: licenses, minimum package age, known vulnerable versions
The agent picks a version that passes, not the newest one
The sensor records prompts, tool calls and skills as the agent works
The CLI hook checks secrets and packages, and the session is linked to the commit
PR Guardrails check new dependencies, secrets and agent files
Malicious-package intelligence reports the bad version as soon as it is known
A minimum package age rule was already holding brand-new versions back
PR Guardrails block any pull request that adds the malicious version
Every repository and running service with it, at the exact version, even when unpinned, from what runs
Security is alerted and each owning team gets a ticket
Auto-fix opens a pull request to a safe version, validated in your CI
The CLI hook checks the key with its provider before the commit exists. It is live
The agent is told to remove the key and rotate it. Nothing reaches the repository
A key already in the change is exposed, so PR Guardrails alert the reviewer that it must be rotated
Keys on any branch or in git history are found and proven live against the provider
Rotate now. Ticket to the team that owns the repository, alert to security
A finding is days from its SLO due date
Heeler messages the owning team before the deadline passes
Past due, PR Guardrails block new pull requests in that repository
The fix PR passes, because it removes the vulnerable version. Merge it and the block lifts
Cannot fix yet? Record a reason and an end date. When it ends, the finding comes back
Many tools hand your code to an LLM and hope it reasons its way to the right answer. Security cannot run on hope: the same finding has to get the same answer every time. Heeler gathers the context itself, from your catalog and ownership to what actually runs, so it can be deterministic wherever security requires it, and use AI where it helps.
Each connection is an app install, a token or a read-only role, with nothing to change in your code or pipelines. Every one you add sharpens how Heeler ranks, routes and fixes.
It reads what your teams already use and sends work back to the same places.
Heeler models how your code becomes a running service, where it is exposed, how critical it is and who owns it. Autotriage reads that model to decide what comes first.
04 · Prevention
MCP and Agent Skills guide the agent while it plans and writes. The Workstation Sensor watches what it does, the CLI stops bad commits, and PR Guardrails gate the merge. Each layer catches what the one before it could not.
Autotriage sets Heeler Risk on each dependency and code finding: Urgent, Plan or Defer. It asks how critical the service is, whether an attacker can reach the flaw, and whether it is under attack. Teams get a short list to fix now.
An LLM asked what matters gives a different answer each run and cannot show its work. Autotriage applies fixed rules to what the Context Engine knows about your environment, so the same inputs always give the same level.
Six finding types, six rules. Choose one:
Three answers pick one cell: how critical the service is, whether an attacker can reach it, and whether it is under attack.
The traced path decides. A flaw on an open internet route outranks the same flaw behind auth on an internal service.
The misconfiguration is matched to the live cloud resource it builds, and ranked on what Heeler sees there.
An image is ranked by the workloads that run it: their tier, their environment, and whether they are internet accessible.
Heeler scans git and its full history, every branch and every commit. It discards fakes with an offline checksum check, then proves what is left with a safe, read-only live test. Each secret lands in an action bucket by what that test found.
Each agent file, skill and MCP config gets a 0 to 100 safety score from fixed arithmetic on what Heeler finds in it.
Flip what is true about one real CVE and watch the level move. Heeler does this on its own whenever your environment changes. No re-triage meeting.
Try it: turn off Internet accessible, or add a mitigation.
Heeler sequences the whole backlog, exploitable first, and works through it for you. Every fix is calculated, proven in your CI and opened merge-ready, so engineers review instead of research and AppSec stops chasing tickets.
Two steps, every time. Heeler calculates the fix deterministically, then its fix agent proves it in a sandbox and in your CI. No human in the loop until merge.
Launch the agent from any auto-fixable remediation, or select a batch. The scope shows before anything runs.
A guardrail flags the change on a developer’s PR and Heeler offers the fix there. One click, and the check flips to passing.
Run heelercli fix with remediation IDs, or let your coding agent schedule the fix over MCP. Same agent, same validation.
A workflow turns fixing into policy. A new fixable finding appears, the agent fixes it, the PR lands ready to review.
Autotriage decides how urgent it is. Heeler then walks the ownership you already keep, from a rule for one package down to the repository's team, and stops at the first real team. That team gets a ticket, a message and an Auto-fix pull request.
An LLM agent reasons its way to a fix, differently each run, and re-sends its whole context at every step. Heeler calculates the fix deterministically, then uses AI to prove it builds and repair what breaks, without spending your tokens.
In the benchmark: 783 manifest reads, 849 greps, 1,985 web searches.
Benchmarks from published third-party agent research. Model API cost is $7.16 per fix, the blended SCA and SAST average, on the lower-priced of two frontier models at public pricing as of July 31, 2026, with 20% cached input and 35% agent overhead, and excludes infrastructure and labor.
AI coding means more code, AI attackers mean less time, and together they add up to more findings than any team can work by hand. Every part of the platform answers one of the three.
Agents get your policy while they write, commits are checked before they exist, and every pull request is checked before merge.
Every agent file is scored from 0 to 100, changes to them are guarded, and the sensor shows what each session did.
Each finding is ranked the moment it lands, from what runs, what it reaches and how critical the service is.
SLOs you set, reminders before they lapse, and every exception kept with a reason, an owner and an end date. SBOMs when you ask.
Upgrade and code fixes arrive as pull requests, validated in your CI and repaired when the build fails.
Work goes to the right team, in its own tools, without a triage queue.
Code, cloud, agents, owners, business and threat intel in the Context Engine. Prevent, Fix and Operate all read it.
See the Context Engine →Rankings, fix versions, guardrail results and owners are decided by rules, not by an LLM. AI writes and repairs code where it helps.
See how Autotriage decides →Work lands in each team's own tickets and channels, on the timeframes you set. Nothing to add to your pipelines.
See setup →