GREENLIGHT - ENGINEER · GOVERNED AI DELIVERY
Move at AI speed, and keep the human greenlight. Greenlight - Engineer runs your software lifecycle as governed AI agents, so your team moves faster without ever wondering what the AI actually shipped.
The stage agent drafts the change from verified context.
DRAFTINGPROPOSEDAn independent twin rechecks it and can block.
CHECKINGVERIFIEDAutomated checks test the change against agreed correctness, security and regression criteria.
RUNNINGPASSEDAn engineer owns the go / no-go before anything is trusted.
AWAITINGGREENLITEvery decision and its evidence is recorded, and replayable on demand.
More code arrives every day, written quicker than anyone can review it, and the gap between "generated" and "verified" is where risk now lives.
Most of that output ships on trust. A suggestion gets accepted, it passes whatever tests happen to exist, and it goes out. Nobody independently checked whether it does what the requirement asked, or whether it quietly broke something six months of decisions depended on.
Meanwhile the reasoning behind it is stranded in a chat history someone will close and never reopen, and the token bill climbs every month as every developer runs everything on the most expensive model available.
Code arrives faster than anyone can read it.
It ships on trust, not on an independent check.
The why lives in a chat history no one reopens.
Everything runs on the most expensive model available.
Speed without control isn't an advantage. It's exposure.
Every task passes through Context, Blueprint and Map, and a human authorises each gate. A human greenlight authorises the transition between every layer.
Work starts from verified truth, not copy-pasted prompts.
Context pulls verified, source-linked truth from the systems you already work in, so every task starts from what's real. Shared context reduces repeated briefing. Missing information is flagged for resolution before the affected work proceeds.
The whole lifecycle runs as agents, each independently rechecked.
Agents cover the full lifecycle, from requirements through architecture, code, test, deploy and docs. Each one is independently rechecked before its work is trusted, to help catch errors before they reach production. Your engineering standards are enforced by the system, not left to whoever remembers them.
You see a change's blast radius before you write it.
Map shows what a change will touch across code, data, APIs and tests before the work starts, so review effort lands where the real risk is instead of on a partial picture.
An agent proposes the change for its stage of the lifecycle.
An independent twin, running on a different model family, verifies it against the requirement and the existing code.
Automated checks test correctness, security and regressions against the agreed requirements and test coverage.
Then a human greenlights it before it's trusted.
Written down, referenced in an audit, applied by whoever remembers it.
The intent is real. The enforcement depends on individual diligence at the moment of the merge.
The AI does the volume. The verification is independent. The final call stays with your engineers.
Every decision is replayable if an auditor ever asks why.
For every unit of work the loop is the same. Propose, verify, greenlight, at every stage of the lifecycle, not just at the pull request. Reproducibility: every decision is logged and replayable. If a client or an auditor asks why a decision was made, the answer is reconstructable rather than remembered.
Every developer with their own AI coding assistant, and no governance in between.
Every change clears an independent recheck, automated gates, and a human sign-off before it's trusted. Your engineers retain the approval decision.
On day one the story is speed: agents run in parallel and failed attempts get caught early. Over time the story is cost, as the platform gets more efficient at how work runs and cost-per-delivered-task bends down. The calculator lets you test it against your own numbers.
You bring your own tokens: inference runs on your accounts under your terms. We license the platform, not the usage, so we have no incentive to keep your consumption high. Every model call runs through one governed gateway, so spend is controlled and visible, with no shadow AI and no scattered subscriptions.
Requirements, decisions, diffs, and test results live in one versioned memory, not in someone's head or a closed chat. A new engineer can use that history during onboarding instead of reconstructing earlier decisions.
Greenlight works alongside GitHub Copilot and Cursor. It doesn't replace them. It puts verification and sign-off around the tools your team already reaches for.
Softobiz configures Greenlight - Engineer to your standards and runs it as a managed engine, so you get the output and the governance without building or operating the platform yourself.
Your architecture principles, security rules and conventions become the standards the system enforces.
Greenfield first, where value proves fastest.
At every gate. That is what makes the speed safe to keep.
Not our benchmark. Yours, measured before and after.
Governed acceleration first, then autonomy, never the reverse. Earn trust and results with the human greenlight in place, and extend autonomy only where the evidence supports it.
ONE ENGAGEMENT, MEASURED AGAINST YOUR OWN BASELINENo. We configure Greenlight around the tools and engineering standards your team already uses, adding independent checks and human approval to the delivery workflow.
We configure and run Greenlight as a managed engine. Your engineers retain the go or no-go decision at the agreed approval gates. Our agentic AI governance approach explains how responsibilities and escalation boundaries are set.
No check can guarantee that every defect will be found. Independent verification, automated tests and human review provide separate checks against agreed requirements. We measure escaped defects and rework alongside delivery speed.
We start with one engagement and compare cycle time, escaped defects and cost per delivered task against your baseline. The approximately fivefold cost reduction shown here is an internal modelled target, not a proven result or a guaranteed saving.
See the AI-led delivery lifecycle.
Review the stages, approval gates and controls that keep engineers accountable for what reaches production.
Cost per delivered task, on your inputs.
Model the economics against your own team size, throughput, and rework rate, so the number is yours, not ours.
