We design and build a custom AI layer above your ERP, MES, IoT, spreadsheets, and operational data. It connects fragmented information, detects risks across planning, procurement, warehouse, production, and quality, and turns escalations into structured decision options for your team.
Before describing what the Control Tower is and isn’t, here’s how it answers the problems above — directly.
Operational problem | AI Control Tower response | Business outcome |
|---|---|---|
Data is split across ERP, MES, IoT, Excel, planning tools | Connects sources into one operational context | Less time spent collecting facts |
Risks are noticed too late | AI agents monitor planning, procurement, warehouse, production, quality | Earlier intervention before disruption |
Escalations lack context | Creates a structured decision package | Faster, more traceable decisions |
Teams rely on informal rules | Turns process logic, historical cases, and instructions into escalation rules | More consistent response across teams |
AI Control Tower connects to the systems you already use, normalizes operational data, and applies specialized AI agents to analyze the manufacturing day across departments. It does not replace ERP, MES, or planning tools — it adds an intelligence and decision-support layer above them.
The Control Tower is an umbrella over what you already run. Your systems stay where they are; the AI layer reads from them, applies your rules, and surfaces decisions in one command view.
The AI layer pulls data from ERP, MES, IoT, spreadsheets, and other systems.
Specialized AI agents review planning, procurement, warehouse, production, and quality signals.
The system identifies shortages, delays, capacity conflicts, quality issues, and other operational risks.
Instead of a generic alert, the team gets structured options with pros, cons, cost, delay, and production impact.
High-impact decisions require human confirmation. Once approved, the decision is recorded and can influence future planning.
The final module set depends on your systems, workflows, and PoC scope.
KPIs, shortages, risks, production status, quality signals, and escalation load.
Natural-language questions grounded in operational data, not general AI answers.
Demand forecast against actual, S&OP view by production order, and capacity signals — so planning gaps surface before they become production stops.
Live PO tracker with delivery status and deviations, supplier scorecards flagging at-risk partners, and pending approvals for supplier risk alerts — before shortages reach the line.
Inspection results board with defect rate, disposition, and root-cause classification per order. Active holds and CAPA register linked to production context — not siloed in a separate QMS.
Structured decision workspace for issues that require action.
Maintenance calendar with PM, predictive, and reactive events overlaid. Equipment status by line — so unplanned downtime risk is visible to the operations team, not just maintenance.
A traceable view of what happened across the operating day.
When the system detects a high-impact issue, it does not silently automate the decision. It prepares a structured decision package for the responsible person: what happened, why it matters, affected entities, recommended options, and estimated business impact.
 | Issue summary |
| Affected order, line, supplier, material, or process |
| Cost / delay / production impact |
| 2–3 recommended options |
| Pros and cons for each option |
| Human confirmation before action is committed |
| Decision history for auditability |
Book a 30-minute session. We’ll show the escalation flow, the decision package, and how the system handles a real operational scenario.
AI Control Tower is designed for companies where operational decisions depend on many moving parts, multiple systems, and timely human judgment.
It is especially relevant for:
Production planning, material shortages, capacity conflicts, quality issues, supplier delays, and cross-department escalations.
Strict process control, quality signals, compliance-sensitive workflows, batch-related risks, and traceable decision-making.
Routing disruptions, warehouse bottlenecks, supplier risks, delivery delays, inventory issues, and time-sensitive escalation flows.
The solution is most valuable when missed signals are expensive, decisions require context from several systems, and teams need a faster way to understand what is happening and what to do next.
The model is rarely the blocker. The blocker is data spread across plants and countries, in different formats, with inconsistent naming and gaps in documentation. We close that part of the cycle too.
1 Different sites, different systems
Plants and branches run their own tools, often in different countries, rarely talking to each other.
2 Inconsistent nomenclature
The same material, supplier, or process is named three different ways across three systems.
3 Missing documentation
Not every site has clean process docs — and that’s normal. We can reconstruct rules from historical tickets and decisions, then hand them back for review.
4 Data lives somewhere
Oracle, PostgreSQL, MySQL, spreadsheets, sensors — we connect to the sources you have and prepare what’s needed.
Not sure your data is ready? Start here. A short engagement to map what you have, what’s missing, and what it takes to make the Control Tower work on your real data.
years in custom software
years in AI
based engineering team
of clients stay 5+ years
We design around your systems, data constraints, workflows, and operational rules.
We do not force a specific model, cloud, or platform. The solution is built for your ownership and flexibility.
We separate AI logic, business rules, integrations, and human approval flows so the system can be audited, adapted, and scaled.
AI Control Tower is designed as a layer above your existing systems. It can connect to ERP, MES, IoT data, databases, spreadsheets, planning tools, and other operational sources through custom connectors or available APIs.
The goal is not to replace your current stack, but to give your team one place to see operational risks, escalation logic, decision options, and audit history.
That is common. Many companies have useful data, but it is distributed across systems, sites, teams, or countries. Some data may be incomplete, inconsistently named, or poorly documented.
We can start with a data readiness assessment to understand what is available, what needs to be cleaned or normalized, and which PoC scenario is realistic for your current data maturity.
Escalation rules are configured around your actual workflows. They can be based on process documentation, operational instructions, thresholds, historical cases, resolved tickets, expert input, or decision traces.
The system can then distinguish between normal events, warnings, and situations that require human review.
Not by default. AI Control Tower is built around a human-in-the-loop approach.
The system can detect risks, prepare recommendations, and structure decision options. High-impact actions still require human confirmation. Automation can be introduced gradually only where the rules, risks, and approval logic are clear.
Yes. Access levels can be designed around roles, departments, sites, and responsibilities.
For example, a plant manager may need a full operational view, while a procurement manager may only need supplier and material-related risks. Sensitive data can be restricted according to the client’s security and governance requirements.
ERP AI features can be useful, especially inside a single vendor ecosystem. The difference is flexibility.
AI Control Tower can work across several systems, custom workflows, legacy tools, spreadsheets, and third-party AI capabilities. It can be adapted faster to your specific escalation logic, decision processes, and operational priorities instead of waiting for a vendor roadmap.
It is a custom AI solution built around a reusable concept and architecture.
We use the AI Control Tower approach as a starting point, then adapt the data sources, agents, workflows, escalation rules, access levels, and interface to your operation.
Start with one operational scenario. For example: material shortages, supplier delays, production bottlenecks, quality drift, capacity conflicts, or planning instability.
From there, we can map relevant data sources, define escalation logic, build a focused PoC, and validate whether the solution creates enough operational value to expand further.
We can start with a short working session to identify one high-value manufacturing scenario and discuss what a focused PoC could include.
