Mission Control ROOM
"Blindly" trusting AI in high-stakes projects is not an option.
That's why, behind the scenes, every project that uses Allocc is managed by our veteran team.
Our MCR team is comprised of top ex-EPC contractors (KBR, Technip, Técnicas Reunidas, Inelectra, Wood, Ayesa, and others). Engineers from every project discipline ensure Jonah's accuracy throughout the entire project lifecycle.
Mission Control Room
The Mission Control Room (MCR) is a digital and physical collaborative hub where selected industry veterans—the elite trainers of Jonah, Allocc’s AI project director—can observe, assess, and interact with Jonah’s decisions and recommendations in real time as projects unfold. This environment transforms Jonah’s training from static datasets and simulations into a living, dynamic process anchored in actual project execution.
The MCR acts as:
The MCR acts as:
A supervisory cockpit where veterans see live data streams from Allocc’s modules.
A training and intervention console where they can provide feedback, override Jonah’s suggestions if necessary,
or reinforce positive patterns.
or reinforce positive patterns.
A knowledge enrichment engine where Jonah continually refines its models using annotated,
context-rich lessons from veteran inputs.
context-rich lessons from veteran inputs.
Among others...
Core components
Unified Execution Dashboard
Hosts all the necessary information for decisions.
- Integrates outputs from all Allocc modules (Modules 1–17).
- Displays real-time status of flow KPIs, buffer conditions, delay causes, subsystem readiness, and contractor performance.
- Live Jonah suggestions and their adoption rate (via Animus Sentinella monitoring)
Veteran Feedback Layer
Allows our team to:
- Rate Jonah's decisions (approve, improve, reject).
- Provide context-rich annotations (e.g. "vendor X is unreliable under these conditions", "client Y usually escalates this type of issue").
- Propose alternative actions that Jonah can learn from.
Intervention Console
Emergency override function where our team can:
- Issue immediate corrective actions that are visible to project teams.
- Trigger escalation protocols (e.g., activate Quick Reaction Force).
- Adjust AI behavior weights temporarily in critical situations.
Learning Repository Feed
Interactions are logged, feeding:
- Jonah’s pattern recognition (what worked, what didn’t, under what conditions).
- Trigger Sentinella behavior tracking (how teams responded to interventions). protocols (e.g., activate Quick Reaction Force).
- Continuous retraining of Jonah’s algorithms with fresh, real-world data.
Scenario Playback
Allows our team to:
- Replay key decisions and flow events with veteran commentary for post-mortem reviews.
- Use of Jonah’s What-If Simulation Engine to explore alternate paths and outcomes.
Collaboration Hub
Provides the framework for our team to interact.
- Integrated chat, voice, and video for veterans to debate and agree on complex calls.
- Decision logging linked to Allocc's client-contractor log for traceability.
Our MCR in action
A delay emerges on a critical subsystem due to late vendor delivery. Jonah suggests fast-tracking a substitute component and reallocating crews.
Allocc's team in the MCR:
Spot a potential client spec risk in the substitute.
Override Jonah’s plan, activate the Quick Reaction Force, and initiate client consultation.
Annotate the case, so Jonah learns to check spec alignment before proposing
substitutions in similar scenarios.
substitutions in similar scenarios.
