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We build and run AI agents that forecast demand, rebalance inventory, source from suppliers and clear order exceptions inside your ERP and planning systems. Every agent works within the spend limits and supplier policies you set, and every decision it makes is traced.
Trusted on five-year contracts by a leading Indian bank and a Dubai government entity.
Supply chain teams use AI to forecast demand, set inventory levels, choose suppliers, spot supplier risk, plan routes and resolve order exceptions. AI agents now go further. They read signals across ERP and planning systems, apply your policies, propose an action such as a reorder or a reroute, and carry it out within spend and supplier limits.
Most supply chains already have forecasts. The gap is between the forecast and the action, where planners and buyers spend their days on exceptions. Agents close that gap, but only if each one knows the limits it can't cross and leaves a record of what it did.
Blend sales history, promotions and external signals into forecasts, and explain what changed and why.
Rebalance stock between locations and adjust safety stock and reorder points by service level.
Draft RFQs, compare bids, and place orders with approved suppliers, staying under each buyer's spend limit.
Watch suppliers' delivery performance, financial and geopolitical signals, and propose a fallback before a disruption hits.
Choose carriers and routes, rebook delayed shipments, and handle the paperwork for the change.
Resolve short shipments, price mismatches and failed deliveries, and update customers with the new date.
Prepare scenarios for the planning meeting, reconcile demand and supply plans, and record the decisions taken.
Check classifications, origin rules and restricted parties before goods move, and flag what needs a specialist.
Supply chain decisions move money and goods. A wrong reorder ties up working capital, and a wrong supplier can break a contract or a compliance rule. So every agent we build works through Context, Control and Action, with a decision trace behind each step.
Agents decide from live inventory, orders, lead times and supplier data, with the source of every input recorded.
Spend limits, approved-supplier lists, contract terms and compliance rules are checked before any action runs.
Agents act through approved tools in your ERP and planning systems, with approvals for orders above a set value.
Inputs, policy version, the option chosen, the options rejected and the approver, for every decision.
An agent starts by proposing reorders next to your planners. As its record builds, it earns the right to act on low-value orders, then higher ones.
SAP S/4HANA, Oracle SCM Cloud and other ERPs, through APIs, events or read replicas.
Blue Yonder, Kinaxis and other planning tools, where agents act on the plan instead of replacing it.
Coupa and other procurement suites, supplier portals, TMS and WMS, and data platforms such as Snowflake and Databricks.
| Dimension | Planning software | RPA | Governed AI agents |
|---|---|---|---|
| What it does | Produces forecasts and plans | Repeats fixed keystrokes | Turns signals into actions across systems |
| Handles exceptions | Flags them for planners | Breaks | Works through them within policy, or escalates |
| Explainability | Model outputs | Script logs | Reasons, options and policy recorded for every decision |
| Controls | User permissions | Access rights | Spend limits, supplier policy and approvals before every action |
| Audit evidence | Plan versions | Bot run logs | A full decision trace |
| Rollout | Go live | Go live | Shadow, supervised, bounded, then full |
Find the supply chain decisions worth automating first, with an AI readiness assessment.
One decision, such as replenishment or order exceptions, running in shadow mode on your data, with the evidence to move it up a level. Scope and timeline are agreed per engagement.
A portfolio of agents across planning, procurement and logistics, run under SLA. More on our procurement and supply chain agents.
They are AI systems that finish supply chain work end to end, such as a reorder or an exception, rather than only producing a forecast. They use your systems and data, follow your policies, and send decisions above set limits to a person.
Planning software tells you what should happen. Agents work on the gap between the plan and reality, handling the exceptions and actions your planners and buyers do by hand today.
Agents draft RFQs, compare supplier bids against your criteria, check contract terms and place orders with approved suppliers, staying within each buyer's spend authority.
Only within limits you set, such as order value, supplier list and category. Anything above those limits goes to a buyer for approval, and every order is recorded in the decision trace.
SAP S/4HANA, Oracle SCM, Blue Yonder, Kinaxis, Coupa, TMS and WMS platforms, and data platforms such as Snowflake and Databricks. Production-proven connectors for your stack are confirmed during scoping.
A pilot covers one decision running in shadow mode on your data, so you can measure its accuracy before it acts. Timeline depends on data access and policy clarity, and is set during scoping.
Start with a frequent decision that has a clear policy and a person reviewing it today, such as replenishment for one category or clearing order exceptions.
We'll choose a decision with real volume, map the systems and limits it needs, and show what shadow mode would prove.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
