Industrial & Manufacturing Tech
Reconstruct complete context across engineering, sales, compliance, and supply chain systems so AI can execute industrial workflows without risk, rework, or guesswork.
What this breaks in the business
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Incorrect Pricing Missed technical constraints lead to margin loss.
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Lost Tenders & Delayed Incomplete compliance context slows approval
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Engineering Rework Outdated specifications trigger avoidable redesign
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Slower Sales Cycles Manual verification creates friction and drag
Nand AI Approach
Reconstructing the Industrial Digital Thread
Nand AI doesn’t retrieve information. It reconstructs meaning.
Our Context Engine builds a graph-first architecture that becomes the system of record for your enterprise’s collective intelligence.
- Connects technical P&IDs to active sales proposals
- Links BOMs to engineering revisions and approvals
- Maps supplier specs to compliance commitments
- Traces change orders to downstream production impact
What this enables
Engineering to Sales Alignment
Link technical specifications directly to commercial bids so feasibility is validated before contracts are signed.
Timeline & Version Intelligence
Automatically track how specs evolve so agents always use the latest approved data, preventing rework.
Cross-System Dependency Awareness
Understand how a change in a supplier’s spec affects your ability to meet buyer-added conditions in complex tenders.
This is the digital thread, rebuilt as living context.
OneSearch for Industrial & Manufacturing Teams
OneSearch gives pre-sales, engineering, and operations teams a unified search layer that understands industrial nuance. It doesn’t return links. It synthesizes answers.
KEY CAPABILITIES
- Synthesize Answers Across Systems
Pull context from Salesforce, Slack, and Google Drive into a single, grounded response. - Authorship and Provenance
See exactly who approved a spec and when. - Eliminate Hallucinations
Claims without a traceable source or timestamp are rejected before delivery.
Commercial to Operational
Agentic Workflows
The power of an agent is only as good as its data. By using Nand AI as your central Context Engine, you can deploy agents that execute high-stakes, multi-step workflows.
Agent 1
Proposal Agent
Automates complex technical proposals without risking feasibility or margin.
- Matches buyer-added conditions against live engineering specs and BOMs
- Validates technical feasibility before submission
- Eliminates contradictions across proposal sections
Agent 2
Security & Compliance Agent
Answers ISO, NIST, SOC2, and customer security questionnaires with audit-ready accuracy.
- Pulls evidence from internal audits, policies, and control documents
- Preserves authorship, approval trails, and version history
- Maintains consistency across all customer and regulatory submissions
Agent 3
Supply Chain Resilience Agent
Anticipates disruptions before they impact production.
- Monitors supplier performance, logistics signals, and external risk factors
- Connects external events to internal inventory and production timelines
- Recommends alternate sourcing or routing paths proactively
Agent 4
Predictive Maintenance Orchestrator
Prevents downtime by turning machine data into action.
- Analyzes IoT logs, service manuals, and historical maintenance records
- Predicts failure windows with contextual understanding
- Automatically schedules service and orders parts
Agent 5
Adaptive Quality Control Agent
Finds defects and traces root cause across the production chain.
- Links vision data, shift logs, and supplier batches
- Identifies systemic quality issues, not just symptoms
- Triggers corrective action workflows with full traceability
Agent 6
Autonomous Sourcing Agent
Manages RFQs and supplier selection using real demand context.
- Issues RFQs based on live production requirements
- Compares suppliers using performance history and contract terms
- Grounds negotiation strategies in ERP and demand data
How Nand AI Delivers Impact Over Time
Measurable improvements across sales velocity, compliance reliability, and supply chain resilience.
IMMEDIATE IMPACT
Shorten your quote-to-cash cycle
Link technical specifications directly to commercial bids so feasibility is validated before contracts are signed.
Target Outcome
50% Faster Bids
THIS QUARTER
Reduce compliance friction and audit risks
Automate feasibility checks and BOM generation with zero engineering bottlenecks.
Target Outcome
Zero Audit Findings
FUTURE STATE
Build a self-healing supply chain
Orchestrate autonomous sourcing, predictive maintenance, and adaptive quality control. Let the system optimize itself.
Target Outcome
Autonomous Ops
Security & Compliance
Enterprise Security by Design
Security is not an afterthought. Our platform is built for the most stringent regulatory environments, ensuring your data never leaves your perimeter and access is strictly audited.
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Tenant-Isolated Architecture
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Zero-Training Guarantee
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(our models don't train on customer data)
Help Center
Frequently asked questions
Quick answers to questions you may have. Can't find what you're looking for? Reach us out
Magus is the execution layer powered by the Nand AI Context Engine. It securely connects to your trusted enterprise systems such as Google Drive, Salesforce, Slack, and ServiceNow, reconstructs full context across documents, authors, and timelines, and generates accurate, evidence-backed responses for workflows like RFPs, security questionnaires, and real-time sales support.
Magus is built for enterprise sales, pre-sales, proposal, legal, and procurement teams operating in complex, high-stakes environments. It is used by teams that require accurate, traceable answers across large and evolving knowledge bases. The Universal Search capability can be used across the organization to retrieve precise, context-aware answers.
Most customers are production-ready in hours, not weeks. Initial deployment is typically completed the same day, depending on the number of systems being connected.
General-purpose AI is designed for broad knowledge and open-ended conversation. It is not built for enterprise accuracy, governance, or traceability.
Magus is different in three critical ways:
- It operates only on your verified enterprise data
- Every answer is grounded with source citations
- Each response is confidence-scored to surface uncertainty
This makes Magus suitable for high-stakes enterprise workflows where correctness matters.
Accuracy is enforced through the Nand AI Context Engine and validation layer.
Every response is:
- derived from your verified source material
- checked against the knowledge graph for consistency
- rejected if it cannot be traced to a specific source or timestamp
This eliminates black-box behavior and ensures outputs are auditable.
Security is foundational to the Nand AI platform.
- All data is encrypted in transit and at rest
- Customer data is tenant-isolated
- Your data is never used to train third-party models
- Full audit trails are maintained for all access and activity
Nand AI is built for enterprise-grade security and compliance from the ground up.
Magus integrates with the systems where your enterprise knowledge already lives, including:
Google Drive, Salesforce, ServiceNow, Microsoft Teams, HubSpot, OneDrive, Jira, Zendesk, Slack, Notion, Confluence, and custom enterprise systems.
New connectors can be added without disrupting existing workflows.
Ready to fix your industrial context challenge?
The next generation of industrial AI won’t be built on prompts and patches.