Reusable agent gateway, capability gates, evidence requirements, human approval, traces and replayable failures. The public cases are synthetic and positioned as pilot-ready engineering candidates.
Try the bounded workflow → · Inspect repo ↗Proof of work · Almedia Junior Engineer (AI)
Turn repeated data requests into self-service.
A stakeholder asks the same question again. The workflow finds the required sources, joins the data, checks the answer and returns it with provenance — then turns the repeated request into a reusable check.
Synthetic data fixtureReal browser-side join + checksFail closed when a source is missing
60-second self-service data proof
“Which campaigns need attention today?”
This is a synthetic UA-style case, not Almedia internal data. The mechanics are real and inspectable: separate JSON sources are loaded, joined by campaign ID and checked deterministically in the browser.
Request · Growth stakeholder
Flag campaigns below 1.0× ROAS or above €8 per verified user.
Normally this recurring question becomes another hand-off. Here the stakeholder gets a checked answer directly.
Self-service answer
No analyst ticket required.Run the request to load and reconcile the three source datasets.
Answer
This question is recurring. Turn the exact sources, joins, thresholds and failure policy into a reusable self-service check.
Campaign health checkReusable definition saved · demo
OwnerGrowth
Inputs3 governed sources
RuleROAS <1× or CPU >€8
Failure policyBlock if source missing
In production this definition could be exposed through a dashboard, Slack/agent tool or API and optionally scheduled — with the same permissions, provenance and failure behaviour.
Blocked · missing evidenceCannot calculate ROAS.
The revenue source is unavailable. The workflow does not guess a value or return a partial answer as complete.
Trust test: remove one required source and see whether the workflow still pretends it can answer.
Why this maps to the role
Repeated question → reusable data product.
The goal is not merely to answer one request faster. It is to turn common, well-bounded questions into reliable self-service interfaces so the data team stops being the human API.
Real PDF intake, structured extraction, evidence states and persistence — another example of turning fragmented inputs into reviewable workflow state.
Try the document workflow → · Inspect repo ↗If I joined Almedia
Start with the most repeated data request.
Build one self-service path in shadow mode first. Earn broader automation from evidence, not enthusiasm.
1 · Discover rank ad-hoc requests by frequency × time × business value.
2 · Build connect the minimum required sources and make every transformation observable.
3 · Productise save the recurring logic as a governed reusable check, not a one-off answer.
4 · Measure corrections, latency, failure recovery and self-service adoption.