# AI for Fujitsu GLOVIA G2 that stays inside your contract boundary > Source: https://netray.co/erp-ai/fujitsu-glovia-g2-ai-integration > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI on Fujitsu GLOVIA G2 works best as a private layer that reads order management, configurable BOM, and engineering change data through GLOVIA's own APIs, then answers questions and drafts routine transactions without any contract or technical data leaving the customer's network. For most GLOVIA G2 shops running government or prime-contractor work, that means a self-hosted model, not a vendor's shared cloud AI service. ## Kind platform ## Erp Family other ## Erp Products - Fujitsu GLOVIA G2 ## Industries - Aerospace - Defense ## Region Global ## Persona IT Director ## Eyebrow GLOVIA G2 + on-prem AI ## Intro GLOVIA G2 is not a mainstream ERP most consultants can quote from memory. Fujitsu Glovia built it for exactly the kind of manufacturer that ends up reading this page: engineer-to-order and configure-to-order aerospace and defense suppliers running multi-level BOMs, engineering change orders, and cost-plus or FFP government contracts side by side with commercial work. That combination of configuration complexity and contract accounting rigor is why GLOVIA G2 shops tend to be smaller, tighter IT teams than SAP or Oracle sites, and why every new system has to prove it will not put export-controlled or CUI data somewhere it should not be. The practical pain shows up as reporting and access, not as a lack of data. GLOVIA G2 holds accurate order status, configurable BOM structure, and engineering change history, but getting a plain-English answer out of it usually means a planner or program manager waiting on a report request, or opening several screens to reconstruct the current configuration of a make-to-order unit. Add DCAA-compliant job costing and the numbers most people actually want, current burden rate against a contract, unbilled WIP by job, sit behind finance-only views. Public AI assistants are a non-starter here. Most GLOVIA G2 customers carry ITAR technical data, DFARS 252.204-7012 obligations, or both, and a browser-based copilot that sends a BOM description or a contract line item to a third-party API is a compliance incident waiting to happen, regardless of the vendor's terms of service. The fix is architectural: keep the model, the retrieval layer, and the logs on hardware you control, and treat GLOVIA G2 as a read source through its own web services rather than as a place to bolt on a SaaS chatbot. This page is deliberately general about GLOVIA G2's internal object model. Fujitsu Glovia's implementation partners and your own DBAs know the exact table and API names for your version; we are not going to guess at specifics we cannot verify. What we can describe with confidence is the architecture that works for a system like GLOVIA G2, an Oracle-backed, API-addressable ERP with heavy configuration and government contract accounting, and where a private AI layer earns its keep fastest. ## Pain Points ### Configuration status is locked behind screens **Detail:** Reconstructing the as-configured BOM and current engineering revision for a specific sales order or work order takes navigating several GLOVIA G2 screens, and program managers end up asking engineering or planning instead of self-serving. ### Contract cost visibility is finance-only **Detail:** Unbilled WIP, burden rate variance, and funding remaining on a contract line live in cost accounting views that operations and program staff cannot query directly, so status updates travel by email instead of by report. ### Engineering change impact is manual **Detail:** When an ECO changes a component on a configurable BOM, tracing every open order, work order, and inventory lot affected is a manual cross-reference exercise, which is exactly the kind of task an LLM with real ERP access should shorten. ### Small IT teams, specialist system **Detail:** GLOVIA G2 shops rarely have a bench of in-house developers who know the platform deeply, so any AI project has to work with existing integration points rather than assume a large internal engineering capacity. ### Export control and CUI boundaries **Detail:** Technical data tied to defense programs cannot pass through a public AI API, which rules out most off-the-shelf copilots and pushes the requirement toward on-prem or sovereign-cloud deployment by default. ## Use Cases ### Plain-language order and configuration status **Description:** Program managers and customer service ask for the current status, configuration, and open engineering changes on a sales order in ordinary language instead of navigating multiple GLOVIA G2 screens. **Erp Objects:** Sales order and configurable BOM records, work order status, engineering change records **Outcome:** Cuts routine status lookups from a multi-screen search to a single answer with the source record cited ### Engineering change impact assistant **Description:** When an ECO is proposed, an agent traces every open order, work order, and on-hand inventory lot referencing the affected part or revision and drafts an impact summary for engineering sign-off. **Erp Objects:** Engineering change records, BOM structure, open order and work order lines, inventory lots **Outcome:** Turns a multi-hour manual cross-reference into a reviewed draft in minutes, with a human approving before anything is posted ### Contract cost and WIP narration for program reviews **Description:** Ahead of a program review, an agent pulls unbilled WIP, burden variance, and funding remaining by contract line and drafts plain-English commentary for the program manager to check and present. **Erp Objects:** Job costing and contract accounting records, burden rate tables **Outcome:** Shortens program review prep from a finance data pull plus manual write-up to a reviewed first draft ### Purchase order and supplier follow-up drafting **Description:** For routine POs approaching their promise date, an agent drafts a status inquiry email to the supplier referencing the PO line, quantity, and need date, for the buyer to review and send. **Erp Objects:** Purchase order lines, vendor records, receiving history **Outcome:** Reduces time buyers spend writing routine follow-up emails on non-critical lines ### Quality and non-conformance drafting **Description:** For a logged non-conformance, an agent assembles the affected part, lot, order history, and prior similar findings into a draft disposition memo aligned to AS9100 documentation expectations. **Erp Objects:** Quality and non-conformance records, part and lot history, prior corrective actions **Outcome:** Cuts first-draft time on routine non-conformance documentation while keeping the quality engineer as the final author ### Government contract compliance question answering **Description:** Contracts and finance staff ask an agent to explain how a specific FAR or DFARS clause maps to current GLOVIA G2 cost accounting setup, grounded in the actual chart of accounts and cost pools rather than generic web answers. **Erp Objects:** Cost accounting configuration, contract records, indirect rate pools **Outcome:** Gives contracts staff a faster, source-grounded starting point instead of relying on institutional memory alone ### New-hire and cross-training assistant **Description:** Because GLOVIA G2 knowledge is concentrated in a few long-tenured staff, an agent trained on internal procedures and screen flows answers how-to questions for new planners and buyers. **Erp Objects:** Internal SOP documents plus read access to relevant transaction screens for context **Outcome:** Shortens ramp time for new staff on a system with a thin external talent pool ## Architecture **Overview:** A private layer sits beside GLOVIA G2, reading order, BOM, engineering, and cost data through its web services and database views, grounding a locally hosted model, and writing back only through approved, human-reviewed transactions. **Layers:** #### GLOVIA G2 connector **Detail:** Reads order management, configurable BOM, engineering change, and cost accounting data through GLOVIA's web services and, where authorized, read-only database views. #### Data and semantic layer **Detail:** Normalizes GLOVIA objects, BOM configurations, and cost pool structures into a consistent schema the model can query and cite accurately. #### Model serving **Detail:** An open-weight model (Llama, Qwen, or Mistral class) served on customer-owned or customer-controlled GPUs, sized to concurrent user load. #### Retrieval and agents **Detail:** Retrieval-augmented answers grounded in live GLOVIA data and engineering or contract documents, with agent actions scoped to specific, pre-approved transaction types. #### Governance and audit **Detail:** Every query and draft action is logged with the source records used, role-based access mirrors GLOVIA's own security, and no write-back happens without human approval. ## Deployment Options ### Air-gapped on-prem **Fit:** Programs carrying ITAR technical data or classified-adjacent CUI **Detail:** Model, retrieval index, and logs run entirely on customer hardware with no outbound network path, matching how the GLOVIA G2 database itself is typically isolated. ### Private or sovereign cloud **Fit:** Commercial A&D suppliers without a hard air-gap requirement **Detail:** Deployed in a dedicated tenant or the customer's own cloud account, keeping data under contractual and geographic control without the overhead of physical isolation. ### Hybrid **Fit:** Sites that want to pilot before committing hardware budget **Detail:** Start on a rented GPU instance for evaluation, then move the same stack on-prem once usage patterns and value are proven. ## Compliance ### Technical data referenced by the model stays on infrastructure the customer controls, avoiding any deemed-export exposure from a public AI API. **Framework:** ITAR ### AI infrastructure is designed to sit inside the same controlled environment as GLOVIA G2 itself, subject to the same access and logging controls. **Framework:** DFARS 252.204-7012 / NIST SP 800-171 ### AI-drafted cost commentary is clearly labeled as a draft citing source records, never posted or submitted without a human reviewer in the loop. **Framework:** DCAA cost accounting ### Quality and non-conformance drafting follows existing documentation formats and requires quality engineer sign-off before any record is finalized. **Framework:** AS9100 ## Integration Notes - Read access goes through GLOVIA G2's supported web services and reporting views wherever available, rather than direct table writes. - Configurable BOM structures are normalized so the model can explain a specific unit's as-built configuration accurately, not just its base part number. - Cost accounting integration respects existing role-based security; users only see cost pool and rate detail they are already entitled to see in GLOVIA G2. - Any write-back, an approved engineering change note, a drafted PO follow-up, a filed non-conformance, goes through a staged approval queue, never a direct API write. - Document grounding includes engineering drawings, SOPs, and prior contract correspondence stored outside GLOVIA G2, indexed alongside the transactional data. - Model updates and retrieval index refreshes run on a schedule the customer controls, with no dependency on an external vendor's release cycle for data freshness. ## Product Fit ### Custom build **Why:** GLOVIA G2 is not one of ERPray's built-in connectors today, so the connector and agent layer is built to order using the same architecture pattern, and can be extended as your GLOVIA usage evolves. ## Engagement ### Discovery **Duration:** 2-3 weeks **Deliverables:** - GLOVIA G2 API and data access inventory - Prioritized use case list by pain and feasibility - Deployment boundary decision (air-gapped vs private cloud) ### Pilot **Duration:** 6-8 weeks **Deliverables:** - Working connector to order, BOM, and cost data - One or two use cases live for a pilot group - Accuracy and grounding review against source records ### Production **Duration:** 4-6 weeks **Deliverables:** - Role-based access matching GLOVIA G2 security groups - Approval workflow for any write-back actions - Audit logging and monitoring in place ### Scale **Duration:** Ongoing **Deliverables:** - Additional use cases added by priority - Model and hardware right-sizing as usage grows - Quarterly review of accuracy and coverage ## Buyer Questions - Does any part of this solution send GLOVIA G2 data to a shared, multi-tenant AI service? - Can the vendor name the specific GLOVIA G2 API or view each use case reads from? - Who owns the model weights and the retrieval index if we end the engagement? - What happens to a write-back action if the AI's draft is wrong, is there always a human approval step? - How is access scoped so a buyer cannot see cost pool detail they are not authorized to see today? - What is the realistic GPU sizing and cost for our expected concurrent user count? - How does the vendor handle a GLOVIA G2 version upgrade that changes the underlying API surface? ## Faq ### Is there an off-the-shelf AI copilot for Fujitsu GLOVIA G2? **Answer:** Not in the way SAP or Microsoft ship one for their own ERPs. GLOVIA G2 customers who want generative AI today are building or commissioning a private layer that reads GLOVIA's data through its APIs rather than waiting on a vendor feature. ### Can AI safely touch DCAA-relevant cost data? **Answer:** Yes, if it is read-only for reporting and any drafted commentary is clearly marked as a draft requiring human review before it feeds a contract deliverable. AI should never post or submit cost accounting entries on its own. ### Does this require replacing or upgrading GLOVIA G2? **Answer:** No. The AI layer reads from your current version through supported APIs and views; it does not require a version upgrade or a change to how GLOVIA G2 itself operates. ### What models are realistic for a shop this size? **Answer:** Open-weight models in the 7B to 70B parameter range, served on one to a few GPUs, are typically sufficient for question answering and drafting tasks grounded in GLOVIA data, without needing frontier-scale infrastructure. ### How do you handle export-controlled technical data in the BOM or engineering records? **Answer:** The model and its retrieval index run entirely on infrastructure the customer controls, with no outbound calls to a third-party API, so technical data never leaves the boundary it is already required to stay inside. ### Can engineering change impact analysis actually be trusted? **Answer:** The agent produces a draft impact list citing the specific orders, work orders, and lots it found; engineering still reviews and signs off. It replaces the manual search, not the engineering judgment. ### What does a pilot cost roughly? **Answer:** Costs vary by GPU sizing and integration scope, but a focused pilot on one or two use cases is typically the fastest way to get a real number for your environment rather than a generic estimate. ## Search terms glovia g2 ai, glovia g2 ai integration, fujitsu glovia ai, ai for glovia g2, glovia g2 copilot, on-prem ai for glovia g2, glovia g2 generative ai, glovia g2 aerospace ai, glovia g2 dcaa ai, glovia g2 quality management ai, glovia g2 engineering change ai, private llm glovia g2, fujitsu glovia g2 ai agents --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json