Purchase Intelligence
"Right item. Right quantity. Right time."
Synthesises demand signals, vendor reliability, inventory aging, and discount economics to generate procurement recommendations that balance service levels against carrying costs — continuously, not on a quarterly review cycle.
Key Applications
+ 4 Additional Industry Applications
Strategic Outputs
- SKU-level order recommendations
- Safety stock policies
- Vendor risk scores
- Coverage scenario simulator
- Seasonal buy calendar
Ecosystem Integration
Decision Framework
Managed intelligence layers that scale with your enterprise operations and data complexity.
Descriptive Analysis
Strategic parameters & pipeline architecture
Real implementation stack — Probabilistic Forecasting × Transformer-based LSTM × Neo4j graph layer
Ingest
Structured Time Series signals batched via async pipeline into staging layer
Transform
Graph relationships built — Transformer-based LSTM applied on entity nodes
Serve
Scored outputs streamed to Retail & Supply Chain endpoints in real-time
# Purchase Intelligence — ingestion pipeline
import asyncio
from neo4j import AsyncGraphDatabase
from pydantic import BaseModel
class Transformer-basedLSTMRecord(BaseModel):
entity_id: str
structured_time_series_score: float
metadata: dict[str, str]
async def run_pipeline(
records: list[Transformer-basedLSTMRecord],
uri: str,
auth: tuple[str, str],
) -> None:
driver = AsyncGraphDatabase.driver(uri, auth=auth)
async with driver.session(database="neo4j") as session:
await session.execute_write(
_merge_entities,
[r.model_dump() for r in records],
technique="Probabilistic Forecasting",
)
await driver.close()
async def _merge_entities(tx, batch, technique):
await tx.run("""
UNWIND $batch AS row
MERGE (e:Entity {id: row.entity_id})
ON CREATE SET e.created = datetime(),
e.technique = $technique
ON MATCH SET e.updated = datetime(),
e.score = row.structured_time_series_score
""", batch=batch, technique=technique)Strategic Implementation Kit
Access production-ready Python pipelines, optimized Cypher queries, and validated Pydantic schemas. Available after a technical discovery session.
- Full Neo4j schema + seed data
- Production Python pipeline
- FastAPI + Redis serve layer
- Docker Compose setup
Stack
Intelligence Engine Sandbox
See the engine in action
Strategic Perspective
Read our analysis of the operational philosophy and strategic metrics behind the Purchase Intelligence framework.
Transform your Supply Chain operations
Get a custom strategic roadmap, ROI projection, and delivery plan tailored to your enterprise landscape.