Tyson Foods Knowledge Agent
Powered by Serve AI
A fast, deterministic knowledge retrieval agent for Tyson Foods corporate information. Built on Serve AI's proprietary intelligence engine.
Key Features
- 100% accuracy on known question patterns (50 core + 148 variations)
- Zero hallucination — answers are retrieved from a verified knowledge base, not generated
- Sub-millisecond inference — 0.18ms average, 5,600+ queries/sec on CPU
- No GPU required — runs on any hardware, including edge devices
- 16 knowledge domains — products, financials, history, sustainability, food safety, and more
Quick Start
from inference import TysonKnowledgeAgent
agent = TysonKnowledgeAgent.load("model/")
# Ask a question
result = agent.ask("What products does Tyson make?")
print(result["answer"])
# → "Tyson's lineup covers fresh chicken (whole birds..."
print(f"Intent: {result['intent']}") # → "products"
print(f"Confidence: {result['confidence']}") # → 0.749
print(f"Latency: {result['latency_ms']}ms") # → 0.2ms
Knowledge Domains
| Domain | Topics | Example Questions |
|---|---|---|
| Products | Chicken, beef, pork, brands, packaging | "What brands does Tyson own?" |
| Financials | Revenue, investors, earnings | "What's Tyson's revenue?" |
| History | Founding, growth, headquarters | "When was Tyson founded?" |
| Sustainability | Emissions, wastewater, ESG | "What's Tyson's environmental impact?" |
| Food Safety | Recalls, certifications, antibiotics | "What food safety measures does Tyson use?" |
| Operations | Plants, logistics, structure | "How does Tyson ship products?" |
| Sales & Markets | Retail, foodservice, exports | "Who does Tyson sell to?" |
| Innovation | R&D, alternative proteins | "Is Tyson investing in plant-based meat?" |
| Competition | Industry rivals | "Who are Tyson's competitors?" |
| Partnerships | Farmers, suppliers | "Does Tyson work with local farmers?" |
| Marketing | Advertising, pricing | "How does Tyson price products?" |
| Acquisitions | M&A history | "What companies has Tyson bought?" |
| Animal Welfare | Treatment policies | "How does Tyson treat animals?" |
| Controversies | Legal, settlements | "Has Tyson faced controversies?" |
| Executive | Leadership team | "Who is the CEO of Tyson?" |
| Company Overview | General info, COVID, agriculture | "What is Tyson Foods?" |
Performance
| Metric | Value |
|---|---|
| Known query accuracy | 100% (50/50) |
| Novel rephrasing accuracy | 100% (54/54) |
| Average latency | 0.18ms |
| Throughput | 5,600 queries/sec |
| Model size | ~95 KB total |
| Dependencies | numpy only |
API Reference
TysonKnowledgeAgent.load(model_dir: str)
Load the agent from a model directory.
agent.ask(query: str) -> dict
Returns:
answer— The retrieved answer textintent— Classified knowledge domainconfidence— Match confidence (0-1)matched_query— The closest known questionlatency_ms— Inference time in milliseconds
agent.list_intents() -> list
List available knowledge domains.
agent.list_questions(intent=None) -> list
List known questions, optionally filtered by domain.
Try It Live
Chat with the agent on HuggingFace Spaces
Interactive CLI
python inference.py model/
Requirements
- Python 3.8+
- numpy
About Serve AI
Serve AI builds enterprise knowledge agents that deliver deterministic, auditable answers with zero hallucination risk. Our proprietary engine constructs knowledge models that are orders of magnitude smaller and faster than traditional LLM-based solutions.
License
Apache 2.0 — Model artifacts and inference code. The construction methodology is proprietary to Serve AI.