FortLock reads your live environment, maps every dependency across Kubernetes and cloud, and turns every pull request, plan and deploy into a known consequence — woven through the tools your teams already run.
As AI ramps change faster than any human can review, FortLock is the consequence layer that keeps it safe — for engineers and AI agents alike — and gets smarter with every change.
The map of what depends on what lives in senior engineers' heads — never written down, never current. So a one-line config edit takes down checkout, and a 2am page becomes an hour of log archaeology.
A PR diff shows the lines that changed — never the eleven services that read that config, or that one of them is a single-replica payments path.
"Who uses this?" is answered in Slack, by the few people who remember. They leave. The dependency graph drifts the moment it's written down.
When something breaks, finding the change that caused it is manual correlation across five tools — the single most expensive minutes in the business.
FortLock connects read-only to the systems you already run, builds a content-addressed dependency graph of the live environment — across Kubernetes, cloud, config and runtime traffic — and follows a single change record through its entire lifecycle.
read-only by construction · self-hostable · never reads a secret's value
Seven capabilities, one engine. Each answers a question about a change — never the steady state, which your incumbents already own.
Every transitive dependent of a change, weighted by confidence and criticality — including the k8s↔cloud seam a Terraform plan crosses, before apply.
A semantic read of what field changed, propagated through a simulation that attenuates for redundancy — a 3-replica service reads differently than a single-replica SPOF.
Errors, SLO/latency, availability, data, security, capacity — each with the exact signals to verify after you ship. Not "73% risky," but "check these."
A symptom asset, run backwards: the ranked changes whose blast hit it, the likely cause, and the one to roll back. MTTR from an hour to minutes.
Not just what breaks — how to ship it: canary, add a replica, split the fan-out, flag it, rotate with overlap. Each tied to the risk it removes.
The change ledger as institutional memory: which change types and teams carry risk, which assets keep landing in the blast, auto-assembled postmortems.
Collisions between in-flight changes, ship-before ordering, and a tamper-evident evidence pack — the artifact a regulated audit asks for. An expansion motion that opens once SOC 2 Type II lands.
The same intelligence in the CLI, the PR check, the CI gate, the API and a live interactive map. No new dashboard to adopt.
Built read-only by construction; secret values are never read, only references. Runs air-gapped. Never depends on a competitor's API to function.
Incumbents own state — inventory, posture, monitoring. FortLock owns change-over-state, and that's where the defensible asset will accrue: every prediction gets labeled against what actually happened, so the model calibrates to your environment. The corpus is empty today — it will compound per design partner as it fills, and it can't be bolted on after the fact without the history.
How calibration works: predicted probability gets corrected against labeled outcomes, so the number becomes measured, not asserted. Figures shown are representative on the sample environment.
isotonic reliability curve · the corpus accrues after landing with design partners
Every safety mechanism — code review, change boards, "ask the engineer who knows this system" — was built for human-paced change. As agents generate and ship changes at machine speed, that assumption breaks. The dependency knowledge that kept you safe lives in a few heads and evaporates with every departure.
The same calibrated consequence API an engineer reads in a PR is the check an autonomous agent calls before it ships — at machine speed, with an audit trail. The admission control layer for AI-driven change.
Operational understanding stops walking out the door. Every change — human or agent — is predicted, then checked against what happened, so the model of how your estate behaves only sharpens. A system of record, not a wiki that goes stale.
It threads the developer pipeline (GitHub/GitLab) and the enterprise change chain (ServiceNow CR/CAB) — tying every change from intent → predicted consequence → real outcome → learning, and moving CFR and MTTR while it does.
FortLock doesn't replace a step in your change process. It's the connective intelligence between the steps no single tool owns — landing context exactly where the work already happens.
Blast radius + owner routing posted on the pull request. Reviewers get context instead of rubber-stamping.
Cross-seam consequence before apply — the one thing no other tool shows when cloud cascades into Kubernetes.
Advisory check in the pipeline you already run. Shift-left, no new dashboard.
Watch the predicted blast as it rolls out; diverge → flag or roll back in the act.
Symptom → the change that caused it, plus the rollback. The painkiller that moves MTTR.
Tamper-evident evidence pack: every change, blast, approval and outcome — the expansion motion for regulated teams, gated on SOC 2.
A deliberate sequence: land bottoms-up on visibility, earn trust on the painkillers, then let the calibration corpus build the moat. The engine below is built today — the path ahead is adoption and depth.
Click into the interactive map, pick an asset, and watch the blast radius, consequence and safe-ship guidance light up — the way your engineers will.
Design partnership, a pilot, security questions, or just curious — send a note and we'll get back to you.
The consequence layer for AI-driven change is moving fast. Get the occasional update on the engine, the thesis, and design-partner openings.
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