AI Orchestrator
AI agents. Calculate, alert, propose. You decide.
An orchestrator coordinates a team of specialised AI agents across demand, production, inventory, distribution, workforce and more. Every decision is traceable — none are final until your team approves.
What do the agents decide on their own?
Nothing. They read, compute and warn; an orchestrator runs them in one pass over demand, production, inventory, distribution and workforce, and what comes out is a proposal with its reasoning attached. Approving, editing or rejecting it is a person's act, and every one of those acts is recorded.
BakeWise calculates, alerts and proposes. It does not decide. Your team stays in command. Every AI decision is traceable and reversible.
Who does what in a single pass?
One agent per domain, each with a declared autonomy level. The ones marked Auto write derived data; the ones marked Alerts open a warning; the ones marked Proposes leave a proposal in the approval queue. None of them executes a business decision until a person has signed it.
| Agent | Autonomy | Scope |
|---|---|---|
| Data agent | Auto | Operational data ingest, cleanup and enrichment. |
| Forecast agent | Proposes | Demand forecasting with Prophet + weather + local events. |
| Insights agent | Alerts | Detects operational anomalies and opportunities. |
| Network plan agent | Proposes | Cross-tenant planning for Group networks. |
| Notification agent | Auto | Alert prioritisation and routing by impact. |
| Pricing agent | Proposes | Flags margin compression and recommends a price move; auto-applies only where the law allows. |
| Procurement agent | Proposes | Suggested purchase orders and supplier negotiation. |
| Production agent | Proposes | Daily production plan per bakery and baking sequence. |
| Sentinel agent | Alerts | Final fiscal-integrity pass per tax ID: numbering gaps, regime routing, waste records. |
| Transfer agent | Proposes | Cross-shop transfers in multi-location networks. |
| Validation agent | Auto | Checks plan coherence before execution. |
| Workflow agent | Auto | Orchestrates multi-step tasks across agents. |
| Workforce agent | Proposes | Suggests shifts, detects competency gaps. |
What does this cost today?
- The AI does odd things and nobody knows whyYour ERP recommended doubling the flour order. Nobody knows if the model was wrong or if it caught something. There's no trace. You either trust blindly or ignore it always.
- You don't know what the model will do tomorrowThe vendor says it learns. Learns what? With what data? What changes tomorrow? Without observability, every behaviour shift is a surprise.
- You can't override an AI decision without fighting the systemYou know the forecast is wrong for Saturday — there's a local event the model doesn't know. You try to override and the system asks you three times.
Before / After
And what changes on an ordinary Monday?
Black-box AI vs. traceable AI.
| Today | With BakeWise |
|---|---|
| The model recommends doubling flour — you don't know why. | Every proposal carries the why and the variables it weighed. |
| A behaviour shift catches you by surprise. | Drift monitoring tells you when the model changes. |
| Overriding a decision means fighting the system. | Overriding is one click — and it's logged for retraining. |
Which numbers can you check?
13
Specialised agents
0
third-party model APIs
3
Languages at parity
150ms
Typical response
What is underneath?
- Autonomy controlsYou decide which agents act in auto, which alert, which propose. Per agent, per tenant.
- Explainability endpointQuery why an agent recommended X: variables, weights, confidence. From Chain onwards.
- Traceable overrideOverride any decision with one click. Reason is logged and fed back to the model.
- Feedback learning loopEvery override feeds the nightly retraining. Drift monitoring spots shifts within 48h. From Chain onwards.
- One queue for what is waiting on youProduction volumes, purchases, prices and rotas arrive in a single work queue instead of four dashboard cards. Each row states the action, the evidence behind it, how long it has waited and what happens if you change your mind.
- Every run has an addressThe orchestration history is a page you can filter by shop, and each run opens on its own link: which agents ran, what they decided, what was approved and what it cost. You send the link, not a screenshot.
What else gets asked before deciding?
Can the AI "hallucinate" an operational decision?
No, because no decision is final without human override for agents that require it. The agent proposes or alerts; your team approves. Agents in auto mode (data, validation, notification, workflow) operate on deterministic pipelines, not on free-form generation.
Where are the models trained and hosted?
In the EU. European hosting. Your data does not leave the EU for training. We do not use your data to train other customers' models — each tenant has its own model.
How often does the model learn?
Nightly retraining of the forecast agent. Continuous drift monitoring on Chain+ spots distribution shifts within 48h and triggers additional retrainings if needed.
Can I override a decision after the fact?
Yes. Every decision is reversible. Overrides are logged with reason and fed back as training signal. On Chain+, the feedback learning loop uses overrides to tune the model.
How do you detect bias or model degradation?
Drift monitoring (Chain+) monitors the distribution of inputs and outputs. Alerts if it deviates from baseline. Quarterly manual review for agents in auto mode.