Astrolize deploys AI co-workers into discrete manufacturing processes, starting with one department and expanding only after measurable success. The platform builds on a company's own data—price lists, historical exceptions, and process manuals—so every AI answer is traceable to a source document and requires human sign-off before leaving the system. Its modular approach covers quoting, demand planning, sales, finance, HR, marketing, and surveyor functions.
Funding
Funding not disclosed
Founders
Product
Problem
Most AI initiatives inside manufacturing companies fail because employees are handed tools without context, generic models lack company-specific knowledge, and pilots have no clear path to broader deployment. This results in low adoption, incorrect answers on critical edge cases, and experiments that never translate into operational ROI.
Solution
Astrolize provides a structured method for embedding AI into manufacturing operations by pairing one digital co-worker with one real process, end to end. The system is built on the company's own data—including price lists, historical customer exceptions, and process constraints—so outputs are grounded in actual business context. Every AI-generated answer points back to the source document it came from, and a person must sign off before any output leaves the system. Once a process holds, the same method is replicated to the next department, with each subsequent co-worker costing a fraction of the first. The approach avoids hand-designed ontologies and instead uses the company's existing documentation as the knowledge base.
Target Audience
Manufacturing companies with complex operational processes—including quoting, demand planning, sales, finance, HR, and marketing—that have struggled with failed AI adoption and need a data-grounded, human-verified approach to digital transformation.
Features
- Modular AI co-workers for specific functions: quoting (Vega), demand (Lyra), sales (Rigel), finance (Mira), HR (Maia), marketing (Orion), and surveyor (Altair)
- End-to-end process integration that starts with one department and expands only after the method proves successful
- Every answer is traceable to the specific source document it originated from, ensuring auditability and trust
- Human-in-the-loop sign-off required before any AI output is finalized or transmitted
- No hand-designed ontologies; the system leverages existing company manuals, price lists, and historical exception data
- Scalable deployment model where the second co-worker costs a fraction of the first, enabling cost-effective expansion across departments