Demand forecasting
Forecasting models that combine order history, promotions, seasonality and external signals, with planners able to see why a forecast changed and override it.
Agentic AI for supply chain that plans, acts and asks before it spends.
ap0t is a founder-led software engineering studio based in India that delivers worldwide, led by Vicky Paswan, a founding engineer specialising in Rust and distributed systems. We build agentic AI for supply chains: AI agents that forecast demand, monitor inventory and suppliers, draft purchase orders and resolve exceptions inside your ERP, with clear limits and human approval for decisions that matter.
Key facts: Agentic AI for supply chain at ap0t
- Engagement types
- Fixed-scope phases, dedicated engineering team on a monthly retainer, or a multi-year program partnership.
- Typical phases
- Paid discovery (2–4 weeks), pilot, phased delivery, then ongoing support and evolution.
- Tech stack
- Python for models and agent orchestration, Rust for high-throughput event processing, LLMs with tool calling, PostgreSQL, vector search, Kafka, Kubernetes on AWS, GCP or Azure.
- Delivery
- Remote and worldwide, with agreed overlap hours for North American, European, Middle Eastern and Asia-Pacific teams; on-site workshops for key milestones.
- IP ownership
- You own all source code, documentation and infrastructure, with no lock-in.
- NDA
- Signed before you share confidential details, on request.
When you need it
Why supply chains need AI agents, not more dashboards
Supply chain teams already have data and dashboards. What they lack is time: planners spend their days chasing late shipments, re-keying supplier confirmations and fixing stock positions by hand, while the decisions that move margin wait in a queue.
Agentic AI changes the unit of automation from a report to a task. An agent watches signals across ERP, warehouse, carrier and supplier systems, decides what needs to happen next, takes the action through the same APIs your team uses, and escalates to a person when the situation is ambiguous or the stakes are high.
It makes sense when you have repetitive, rules-plus-judgement work at volume: replenishment, expediting, supplier follow-ups, invoice and shipment exceptions, or demand plans that need constant adjustment.
What we build
AI agents for supply chain we design and build
Autonomous replenishment
Agents that monitor stock against forecasts and lead times, propose or place replenishment orders within the limits you set, and explain each decision.
Procurement agents
Drafting RFQs and purchase orders, comparing supplier quotes, chasing confirmations by email or portal, and flagging price or lead-time changes.
Logistics and expediting
Tracking shipments across carriers, predicting delays, rebooking or re-routing within policy, and notifying customers and teams automatically.
Exception handling
Triaging mismatches between orders, receipts and invoices, resolving the routine ones and preparing a clear case for a human on the rest.
Supply chain copilots
Natural-language assistants that answer questions over your live ERP and logistics data, with citations to the underlying records.
Our approach
How a agentic ai for supply chain engagement runs
Every program starts small and proves value before it grows.
Discovery
2–4 weeks · paidWe map the decisions your team makes, the data and systems involved, and where automation pays off, then design agent boundaries, approval rules and success metrics.
Pilot agent
6–12 weeksOne agent for one workflow runs in shadow mode first, proposing actions that people approve, until its decisions are measurably reliable.
Phased autonomy
Workflow by workflowApproval thresholds are relaxed step by step as trust is earned, and new agents are added for adjacent workflows.
Monitoring and improvement
OngoingEvaluation suites, drift monitoring, cost tracking and regular reviews of agent decisions with your team.
Architecture
Architecture of a reliable supply chain agent
Each agent is a deterministic workflow with well-defined tools, not an open-ended chatbot. Language models handle the parts that need judgement and language, such as reading supplier emails or explaining a recommendation, while forecasting and optimisation use proven statistical and machine-learning methods in Python.
Agents act only through typed, permissioned tools that call your ERP, WMS and carrier APIs. Every action is logged with its inputs, reasoning and outcome, and anything above a spend or risk threshold waits for human approval in the tools your team already uses.
High-volume event processing, such as consuming shipment and inventory streams, runs in Rust for throughput and reliability. The stack is model-agnostic, so you can switch LLM providers or run models in your own cloud.
Risk
Safety, control and trust in agentic AI
Human in the loopApproval thresholds by value, supplier and risk, with shadow mode before any agent acts on its own.
GuardrailsAgents can only use the tools and data they are granted, with spend limits, rate limits and policy checks enforced in code, not in prompts.
EvaluationTest suites built from your historical cases measure accuracy before every release, and live decisions are sampled and reviewed.
Data privacyYour data stays in your environment; we design for least-privilege access and can use models hosted in your own cloud.
Engagement and pricing
How agentic ai for supply chain is scoped and priced
Every engagement is scoped after a paid discovery, because the right size depends on your processes, systems and goals. Programs range from focused pilots to multi-year builds with dedicated teams.
Discovery is a fixed fee. Each delivery phase is then priced as a fixed scope or as a monthly dedicated-team retainer, with a written estimate, assumptions and risks agreed before work starts. You can pause or stop between phases, and you keep everything that has been built.
FAQ
Agentic AI for supply chain: questions from buyers
What is agentic AI in supply chain?
Agentic AI in supply chain means software agents that pursue a goal, such as keeping stock within target levels, by observing data, deciding on actions and carrying them out through business systems, rather than only producing reports. They combine forecasting and optimisation models with language models and act within limits set by people.
How is agentic AI different from traditional supply chain planning software?
Traditional planning tools recommend; people still execute. Agentic AI executes the routine actions itself, such as drafting purchase orders or chasing a late supplier, and escalates the exceptions, so planners spend their time on decisions instead of follow-ups.
Will AI agents place orders without our approval?
Only if and where you decide. Agents start in shadow mode, proposing actions for approval. You set thresholds by value, supplier or category, and anything above them always waits for a person.
Which systems can the agents work with?
Any system with an API or a structured export, including SAP, Oracle, Microsoft Dynamics, NetSuite, custom ERPs, warehouse management systems, carrier APIs, EDI and supplier portals. Email and documents can be handled where APIs don't exist.
How do you prevent AI hallucinations from causing bad decisions?
Agents act only through typed tools with validation, numeric decisions come from forecasting and optimisation models rather than free text, every action is logged with its evidence, and high-impact actions require approval. We measure accuracy on your historical data before going live.
How long until an AI agent is live?
A first agent typically runs in shadow mode within 6–12 weeks after discovery, then moves to approved autonomy as its decisions prove reliable. The timeline depends on data access and the workflow chosen.
Do we need clean data before starting?
No. Discovery includes a data assessment, and the first phase usually includes the data pipelines and quality checks the agent needs. Most supply chains have enough history to start with one well-chosen workflow.
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More ways we can help
Talk to the engineer who will build it
Tell us what you're planning. You'll hear back within one business day, and we can sign an NDA before you share details.