Supply chain management runs on documents, sensor feeds, emails, and legacy EDI messages that rarely fit neatly into a relational schema. Large language models can now extract structured insight from this noise, reason across multimodal inputs, and execute function calls against ERP and WMS systems. The result is not just chatbot assistance, but autonomous agentic loops that handle exceptions, reorders, and compliance checks with minimal human intervention. Oxlo.ai provides the inference backbone for these workloads, offering request-based pricing and a fully OpenAI-compatible API that drops into existing Python and Node.js stacks without friction.
Unlocking Unstructured Supply Chain Data
Procurement contracts, customs declarations, and carrier invoices arrive as PDFs, scans, and unstructured emails. Processing them with traditional OCR plus regex requires brittle pipelines that break when a supplier changes a template. Modern LLMs with vision and long-context capabilities can ingest entire documents in a single request, extract key-value pairs, and return normalized JSON.
For multilingual operations, Qwen 3 32B handles mixed-language supplier communications and agent workflows across regions. When the input is a dense 50-page contract or a threaded email chain, token-based costs scale linearly with page count. Oxlo.ai flips this model with flat per-request pricing, so analyzing a long customs manifest costs the same as a short status query. See https://oxlo.ai/pricing for plan details.
Agentic Automation for Inventory and Logistics
Reactive supply chain management is giving way to agentic systems that observe, reason, and act. An LLM agent can monitor stock levels, parse incoming shipment notices, and call downstream tools to create purchase orders or reroute freight. This requires reliable function calling, multi-turn context, and fast inference.
Models like DeepSeek R1 671B MoE and Kimi K2.6 excel at deep reasoning and agentic coding. You can chain them into workflows where one model evaluates risk while another generates SQL to update inventory. Because these agents often carry long conversation histories and large tool schemas, token-based billing inflates quickly. Oxlo.ai charges per request, making it practical to run persistent agent loops that inspect hundreds of SKUs without cost surprises.
Implementation: OpenAI SDK and Function Calling
Oxlo.ai is a drop-in replacement for the OpenAI SDK. Point your client at https://api.oxlo.ai/v1 and use the same chat.completions interface with function calling and JSON mode.
Example: an inventory agent that decides whether to reorder based on current stock and lead time.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
tools = [
{
"type": "function",
"function": {
"name": "get_stock_level",
"description": "Get current stock for a SKU",
"parameters": {
"type": "object",
"properties": {
"sku": {"type": "string"}
},
"required": ["sku"]
}
}
},
{
"type": "function",
"function": {
"name": "create_purchase_order",
"description": "Create a PO for a supplier",
"parameters": {
"type": "object",
"properties": {
"sku": {"type": "string"},
"qty": {"type": "integer"},
"supplier_id": {"type": "string"}
},
"required": ["sku", "qty", "supplier_id"]
}
}
}
]
messages = [
{"role": "system", "content": "You are a supply chain agent. Use tools to check stock and reorder when below safety level."},
{"role": "user", "content": "SKU-9941 has a safety stock of 500 and supplier S-88. Should we reorder?"}
]
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages,
tools=tools,
tool_choice="auto"
)
print(response.choices[0].message.tool_calls)
Because Oxlo.ai offers no cold starts on popular models, this agent responds consistently even during irregular demand spikes.
Vision Workflows for Freight and Compliance
Not all supply chain data is text. Damage claims, container labels, and customs seals arrive as images. Vision-capable models like Kimi VL A3B and Gemma 3 27B can inspect these visuals and feed structured outputs into your TMS or compliance dashboard.
Using Oxlo.ai’s chat/completions endpoint with image input, you can build a pipeline where a driver photo of a bill of lading is instantly converted into structured JSON and cross-referenced against the ASN. The same request-based pricing applies whether the payload is a short text prompt or a high-resolution image with a detailed system instruction.
The Oxlo.ai Advantage for Operations Teams
Supply chain LLM workloads are inherently long-context and agentic. A single supplier risk assessment might consume a 10-K report, recent news articles, and internal quality logs. Token-based inference penalizes this depth. Oxlo.ai’s flat per-request pricing removes that penalty, so teams can send full context windows without budget anxiety.
With 45+ models across chat, reasoning, code, vision, and embeddings, Oxlo.ai covers the entire stack. Prototyping starts on the free tier with 60 requests per day and 16+ free models, including DeepSeek V3.2. Scale to Pro or Premium as agent traffic grows, or contact the team for Enterprise dedicated GPUs and unlimited volume.
If you are already using the OpenAI SDK, migrating to Oxlo.ai requires only a base_url change. Start building resilient supply chain intelligence today at https://oxlo.ai/pricing.
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