Agentic Intelligence

AI-Agentic Sourcing
Purpose Built for Manufacturing

Optimize spend and protect margins with an AI-agentic sourcing platform - Stozia reads engineering drawings, runs sourcing workflows, benchmarks supplier quotes, and generates should-cost and offers negotiation guidance. It works across direct, indirect, CAPEX, and finished goods procurement categories.

10–15%
Savings in Indirect Spend

Captured across tail spend categories.

3–5%
Savings in Direct Materials

Driven by techno-commercial normalization.

60–70%
Reduction in Buyers Efforts

With agents handling repetitive sourcing steps end-to-end.

40–60%
Faster Sourcing Cycles

With structured RFQs, comparisons, and negotiation packs.

Why Sourcing in Manufacturing is so hard?

Sourcing in manufacturing is uniquely complex - messy specifications, inconsistent master data, and critical requirements buried inside engineering drawings and long PDFs, leading to slow decisions and hidden margin impact.

RFQ's on Email and Excel:

Slow, manual, error-prone processes that drain engineering and procurement time, and make every RFQ harder to track, compare, and audit.

Complex Engineering Drawings, Hidden Sourcing Intelligence:

Critical technical details remain invisible, locked inside engineering drawings and long PDFs - weakening standardisation and negotiation leverage.

Lack of Real Price Benchmarks:

Little to no real-time price benchmarks or reliable should-cost references before negotiations, so "fair price" is mostly guesswork.

Unstructured, Gut Driven Negotiations:

Negotiations are driven by instinct and fragmented data rather than should-costs, cross-supplier comparisons, and market realities.

Execution → Intelligence

AI Agents
Transforming Procurement

Meet the autonomous agents behind every sourcing decision.

Live · Running

AI Sourcing Analyst

Autonomously manages the end-to-end RFQ lifecycle — from vendor outreach to techno-commercial evaluation — so your team focuses on decisions, not admin.

  • Automates RFQs, follow-ups & quote collection
  • Performs techno-commercial evaluations
  • Benchmarks quotes & flags overpriced bids
RFQ Pipeline — Live
RFQ-2024-0847 · Valve Assembly
Sent to 6 vendors · 4 responded
Evaluating
RFQ-2024-0848 · Bearing Kit
Follow-up scheduled · 2 pending
Follow-up
RFQ-2024-0845 · CNC Parts
Best bid: $2.9k · Savings: 12%
Complete
RFQ-2024-0846 · Sheet Metal
Overpriced bid detected · +23% above benchmark
Flagged
Auto follow-up sent to 3 vendors
RFQ-2024-0849 · Hydraulic Seals · just now
Sent
Live · Running

AI Supplier Search

Discovers and validates alternate suppliers beyond your legacy list, expanding the competitive landscape and improving your negotiation leverage automatically.

  • Discovers and validates alternate suppliers
  • Expands competition beyond legacy lists
  • Improves negotiation leverage significantly
Supplier Discovery — Results
IN
Apex Precision Engineering
ISO 9001CNCVerified
94
IN
Vega Metal Works
CastingVerified
88
IN
KPL Fabricators
Sheet MetalAI Found
82
IN
Meridian Forge & Tools
ForgingAI Found
79
IN
Srishti Machining Solutions
PrecisionAI Found
76
Live · Running

AI Category Analyst

Reads engineering drawings and BOMs to compute accurate should-cost references, applying category-specific cost drivers and material indices for precise benchmarking.

  • Applies category-specific cost drivers & indices
  • Reads drawings & BOMs to calculate costs
  • Guides sourcing & negotiation strategy
Should-Cost Analysis
Should Cost Analysis: Baseframe
Date: 2026-02-23 · $ Baseline Version
Engineering
Table 1 – Metadata
Table 2 – Dims
Table 3 – GD&T
Extracted T1
BOM
Machine
Labor
Commercial
QA
Drawing Reader
Cost Calc
Material Idx
BOM Parser
Should-Cost Breakdown · BASE FRAME
Component Cost Share
Raw Material $1.5k
Machining $0.6k
Overhead $0.2k
Margin $0.1k
Cost Distribution vs Vendor
Should-Cost Vendor
$1.5k
Mat.
$0.6k
Mach.
$0.2k
OH
$0.1k
Mgn.
$2.9k
Vendor
Should-Cost Target $2.4k
Vendor quote 21% above target — recommend negotiating down
Live · Running

AI Negotiation Analyst

Sets data-backed negotiation targets, identifies gaps and risks in vendor proposals, and provides real-time pricing and leverage intelligence throughout the cycle.

  • Benchmark should costs to set negotiation targets
  • Identify negotiation gap & risks
  • Provide real-time pricing & leverage intelligence
Negotiation Intelligence
Vendor Quote
$2.9k
Should-Cost
$2.4k
Walk-Away
$2.2k
3 alternate suppliers identified by AI Search
Apex Precision · Vega Metal · KPL Fabricators
Active
Market price trending ↓ — strong leverage available
Steel index −4.2% this quarter · Buyer's market
↓ 4.2%
Negotiation Gap $0.5k  (20.8%)
Vendor quote 21% above should-cost target

One platform for
every sourcing need

From complex strategic sourcing to high-volume tactical events, powered by a single manufacturing-first workflow.

Engineering Drawing Intelligence

Reads engineering drawings, specifications, and requirement documents to extract sourcing-ready technical context.

Automatic Should-Cost

Generates should-cost references automatically so teams can benchmark quotes before negotiation and award decisions.

All Procurement Types

Supports Direct, Capex, and Indirect buying, including made-to-order parts and non-catalog requirements.

Context-Aware Comparison

Interprets technical + commercial terms and compares suppliers in real context, not just by headline unit price.

Techno-Commercial Normalization

Adjusts for freight, payment terms, risk, and compliance to compare on true landed cost and practical fit.

Manufacturing-Aware AI

Purpose-built intelligence trained around sourcing realities in manufacturing, not a generic conversational layer.

Key Success Stories

Real impact delivered across manufacturing leaders.

Tier 2 Auto Components Manufacturer

Challenge: Decentralised sourcing, compliance gaps, and reliance on a limited set of known vendors.

Impact:

  • 12%+ improvement in part-level cost outcomes through better supplier discovery and quote benchmarking
  • 50%+ faster RFQ cycles with agents preparing RFQs, comparisons, and negotiation packs, cutting day-to-day buyer effort per RFQ
  • Audit-ready sourcing trails across plants, reducing time spent on follow-ups, reconciliations, and documentation

Leading Brake & Pump Manufacturer

Challenge: Unmanaged indirect spend, ad hoc sourcing, and weak audit trails across plants.

Impact:

  • 15% savings on operational purchases by normalising quotes
  • Broader, healthier supplier base with AI-led supplier discovery and structured evaluations, without adding to buyer workload
  • Standardised, searchable sourcing records that strengthened audit and compliance reporting and reduced effort spent reconstructing past decisions

Large Industrial Manufacturer

Challenge: Bypassed procurement, over-reliance on legacy suppliers, and limited visibility into who is buying what, from whom, and at what price.

Impact:

  • 60% reduction in manual sourcing workload as agents handled RFQs, comparisons, and negotiation packs, freeing buyers for more strategic work
  • Rapid ROI with days-to-payback, driven by better cost outcomes and time savings
  • Procurement elevated to a strategic cost & risk function, with clear visibility across categories and plants

Why Stozia Is Different

"Automation saves time. Intelligence creates value."

Works over Email / ERP
No Supplier Portals
No Heavy Change Mgmt
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Optimize your spend today

Request a personalized demo and see how Stozia's agentic intelligence can transform your procurement workflows.