Incident investigation
in minutes, not hours.
Orions AI cross-references operational databases, source code and external logs to produce an evidence-backed Root Cause Analysis, with no human analyst, and strictly read-only.
Every incident waits for
the one person who knows.
When an order duplicates, an inventory count diverges or a payment fails silently, the investigation needs to know which systems to check, which logs tell the truth, and which code path triggered it, specialist knowledge that can’t be documented away.
So the specialist becomes the bottleneck. Every incident queues behind one person. As the system scales, that person does not, and diagnosis time only grows.
How an incident becomes a cited RCA.
Read-only end to end, the system investigates and cites; humans act on the result.
What lands on the screen.
A representative reconstruction, illustrative, built from the canon, not a live screenshot.
“Orders are duplicating in our StubHub Pro account.”
No analyst in the loop by design · read-only at the framework level
See it on real work.
Concrete scenarios, what goes in, what comes back.
“Orders are duplicating in our StubHub Pro account.”
Seat-overlap predicate run against BroadcastOrderQueue → cited RCA with the exact records.
“Inventory count diverges from the listing.”
Cross-references Ninja MongoDB and StubHub Pro audit logs → the divergence, traced to source.
“A payment failed and nobody knows why.”
Traces the code path and logs → an evidence chain, no human analyst in the loop.
The systems we build
for Operations & Logistics.
Each is a standalone engagement. Start with one, the systems compose over time.
Multi-Agent Systems
Specialist agents, one per data source, collaborating to a fully cited RCA. Read-only at the framework level. 55/55 stories, 0 analyst hours.
View solution →Agentic Pipelines
Operational process automation, reporting, reconciliation, compliance checks, with eval harnesses and CI regression gates as a service.
View solution →AI Data Intelligence
Plain-English access to operational databases, no SQL, no schema knowledge required.
View solution →Real work. Real clients.
Real eval scores.
Nothing here is a demo, each runs against live client data.
Orions AI
Incident diagnosis engine: no analyst in the loop by design, fully cited RCA.
- No analyst in the investigation loop, automated end to end by design
- Regression gate on 7 golden incidents, run manually today
- Read-only at the framework level, structural, not policy
- Shadow mode available: a new version can run beside production before any cutover
Operations & Logistics-specific
engineering decisions.
Domain knowledge baked into the architecture, not discovered during delivery.
Not sure which build
comes first?
Start with a scope call or the service that maps the problem, then we build the system that fixes it, on the model that suits its size.
Agentix Rapid
One targeted automation, 2-5 days from scope sign-off.
For Operations & LogisticsNone of this vertical's system types is sized for this model; the scope call confirms what fits.
See the automation portfolio →Engineering Program
Multi-week, custom architecture; scope sets the duration.
For Operations & LogisticsSee the programme phases →Tell us what you
want to solve.
A 30-minute call. We'll tell you which solution fits your situation, what the build involves, and the eval it would have to pass, engineering discussion from the first call, no sales pitch.