Paperless Parts + private AI
AI for Paperless Parts: Faster RFQ Review Without Sending Your Customers' Drawings to a Public Model
Short answer
Paperless Parts is a quoting and order-management platform for custom manufacturers that builds cost-based quotes from CAD models and drawings. AI around it works best as a private layer that reads RFQ packages, past quotes and ERP actuals, flags risk on drawings and notes, and drafts review notes for the estimator, while the pricing engine and the human sign-off stay where they are.
- ERP
- Paperless Parts
- Industries
- Custom Manufacturing, Machine Shops, Aerospace, Defense
- Written for
- Estimating Manager
A job shop or build-to-print supplier lives or dies on quote turnaround and quote accuracy. Paperless Parts addresses the mechanics: it ingests 3D CAD and 2D drawings, lets estimators define operations, materials and pricing rules, and produces a quote package and customer-facing portal. What it does not remove is the judgment layer: reading a 14-page RFQ, noticing the flow-down that requires a special process, or recalling that the last three parts like this ran over on the second op.
That judgment currently lives in the heads of two or three senior estimators. Shops that have been quoting for 15 years also have a large archive of past quotes, shop floor actuals in the ERP (JobBOSS, ProShop, Epicor, Global Shop, E2 or similar), and customer correspondence. Almost none of it is searchable in a way an estimator can use mid-quote.
Because the work is often ITAR or CUI-bearing, many shops are rightly nervous about pasting drawings or customer purchase order terms into a consumer chatbot. An estimator who quietly uses a public AI tool on a controlled drawing creates a compliance problem that is far more expensive than the time it saved.
This page describes how a private AI layer fits around Paperless Parts: what it reads, where it runs, what it may and may not write, and where the platform's own features or a simple checklist are enough and you should not buy anything more.
What usually gets in the way
The problems we hear most from estimating manager teams running Paperless Parts.
RFQ packages hide the risky requirements
Customer POs, quality clauses, drawing notes and spec callouts arrive as PDFs and emails. Missing a required special process, a first article requirement or a source-inspection flow-down is how profitable-looking quotes become losses.
Past quotes and actuals are disconnected
What a part was quoted at, what it really cost on the floor and why it overran sits across Paperless Parts, the ERP job costing tables and someone's memory. Estimators rarely have time to reconcile them before the next similar quote.
Estimator knowledge is concentrated and fragile
The people who know that a certain titanium bracket family always needs an extra setup, or that a customer's tolerance callouts are routinely loose, are few. When one leaves, quote quality shifts quickly.
Quote volume outruns review capacity
Shops that win on speed accept more RFQs than they can review carefully. Quoting then drifts toward pattern matching on price per pound or per hour rather than reading each drawing.
Public AI tools are off the table for controlled data
Export-controlled drawings and customer proprietary data cannot be pasted into a general-purpose chatbot, which leaves estimators with no AI help at all or with unsanctioned use that nobody can audit.
Where AI earns its place in Paperless Parts
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
RFQ package triage
A model reads the customer RFQ, PO terms and quality clauses and produces a checklist of special processes, certifications, inspection requirements and delivery constraints for the estimator to confirm before pricing.
Touches: Paperless Parts RFQ, quote, attached documents; ERP customer quality requirements
Outcome: Reduces missed flow-downs by making the first read a structured checklist instead of a skim.
Drawing note and tolerance review
The assistant extracts notes, general tolerances, finish and material callouts from 2D drawings and highlights items that commonly drive cost, such as tight positional tolerances or unusual surface requirements.
Touches: Part, drawing files, operations and material fields
Outcome: Gives the estimator a shortlist of cost drivers to inspect instead of reading every sheet cold.
Similar-part and past-quote lookup
Natural-language search over past quotes and orders returns comparable parts, what was quoted and what the job actually cost, so the estimator sees the variance before setting a price.
Touches: Quotes, orders, ERP job cost, labor and material actuals
Outcome: Shortens the comparison step that estimators often skip under time pressure.
Quote-versus-actual variance explanation
After a job closes, the assistant compares the quoted operations and hours with ERP actuals and drafts a plain-language explanation of where the estimate missed.
Touches: Quote operations, ERP routing and labor transactions
Outcome: Turns a rarely done post-mortem into a routine feedback loop that improves future estimates.
Customer response and clarification drafting
The model drafts clarification questions to the customer where a drawing is ambiguous, for the estimator to edit and send.
Touches: RFQ messages, quote notes, part attachments
Outcome: Cuts the time spent composing routine clarification emails.
Order handoff checking
When a quote converts to an order, an agent compares the order package with the ERP job setup and flags mismatches in quantities, due dates, materials or special instructions before release.
Touches: Paperless Parts order, ERP sales order, job, routing
Outcome: Catches transcription errors at the handoff instead of on the shop floor.
Estimator onboarding knowledge base
A private retrieval layer over internal quoting rules, material notes and customer-specific guidance answers new estimators' questions with citations to the source document.
Touches: Internal quoting guides, material and process notes, customer files
Outcome: Reduces the time senior estimators spend answering repeat questions.
Reference architecture
Paperless Parts remains the system of record for quotes and orders. The AI layer runs in the shop's own environment, pulls quote and order data through the platform's API and exports, joins it with ERP actuals, and returns review notes and drafts to the estimator. It never changes prices on its own.
- 1
Platform connector
Reads quotes, orders, parts and attachments through Paperless Parts' API and webhooks using a scoped credential. Confirm the current API scope and rate limits with your Paperless Parts account team before design.
- 2
ERP join
Read-only connection to the shop ERP's job cost, routing and labor tables so past quotes can be compared with actual hours and material cost.
- 3
Document understanding
Extraction of notes, tolerances and callouts from PDFs and drawings, with the source page and region shown so the estimator can verify each finding.
- 4
Model serving
An open-weight model served with vLLM or Ollama on the shop's own GPU workstation or server, sized for a small team's daily RFQ volume.
- 5
Governance
Every extraction and suggestion is logged against the user and the RFQ, controlled files are partitioned by program, and estimators approve every output before it touches a quote.
Integration notes for your ERP team
- Use a dedicated service credential for Paperless Parts API access, scoped to read where possible, and rotate it like any integration key.
- Use webhooks for new RFQ or order events where available, so triage starts when the package arrives rather than on a nightly sync.
- Join to ERP actuals by part number and customer, and handle revision differences explicitly so a rev C quote is not compared against rev A actuals without a note.
- Keep controlled drawings in a separate index per program, and enforce who can query which index.
- Treat extracted tolerances as suggestions with a visible source region, never as authoritative values written into the quote.
- Write-back is limited to internal notes and draft messages; pricing and operations changes stay with the estimator.
Deployment options
Single-server on-prem
Machine shops and build-to-print suppliers handling ITAR or CUI drawings.
One GPU server inside the shop network reads exported quote data and ERP tables and serves a browser-based assistant. Controlled files never leave the premises.
Private cloud tenant
Shops without server space that still have a contract barring public AI services.
A dedicated cloud tenant under the shop's own agreement, with US-person-only administration where the contract requires it.
Advisory-only, no live connection
Shops starting cautiously.
Estimators upload an RFQ package into a standalone private tool for triage, with no automated link to Paperless Parts or the ERP until trust is established.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR and EAR
Running the model on infrastructure you control, with access limited to US persons where your program requires it, keeps technical data out of third-party AI services. Your empowered official remains responsible for the determination.
CUI and NIST SP 800-171
Where drawings are CUI, the AI environment is placed inside the same enclave and access controls as the rest of your CUI systems, and its logs are included in your evidence set.
AS9100 and customer quality clauses
Review checklists are drafted from the customer's own documents and confirmed by a person, so contract review records remain a human decision.
Customer NDAs
Many OEM terms prohibit sending drawings to external services. A private deployment lets you demonstrate where data is processed.
Where Netray fits
Custom build
RFQ triage, drawing review and quote variance explanation are bespoke workflows built around your quoting rules and the Paperless Parts API rather than a packaged product.
DataRay
For shops that mainly want to search past quotes, drawings, work instructions and ERP data together, a private chat over those sources is a smaller first step.
How an engagement runs
Phase 1 . 2 weeks
Discovery
- -Review of current quoting workflow and Paperless Parts configuration
- -Inventory of controlled versus uncontrolled data
- -ERP actuals availability check
- -Ranked use cases
Phase 2 . 5-7 weeks
Pilot
- -RFQ triage checklist on historical packages
- -Accuracy review with senior estimators
- -Past-quote lookup over a sample of closed jobs
Phase 3 . 4-6 weeks
Production
- -Deployment on shop infrastructure
- -Access controls and audit logging
- -Webhook-driven intake
- -Estimator training
Phase 4 . Ongoing
Expansion
- -Quote-versus-actual feedback loop
- -Order handoff checks
- -Model and prompt refresh
- -Quarterly accuracy review
Questions to ask any vendor, including us
A short list that separates real Paperless Parts AI work from a chatbot demo.
- Where does the model run, and can you show the network path for a controlled drawing from upload to answer?
- Does the system write anything into Paperless Parts, or is it advisory only?
- How are extracted tolerances and notes verified against the source drawing?
- How do you keep one customer's drawings from influencing answers about another customer's parts?
- What happens when the Paperless Parts API changes or a field is renamed?
- Can we measure whether triage actually reduces missed requirements before rolling it out?
- Which ERP do you support for actuals, and how do you handle part revisions?
- What do we keep if we stop using the service?
Frequently asked questions
Does Paperless Parts already include AI?
The platform has been adding automation around part intake and quoting, and you should review its current feature set first. A private layer is relevant when you need review of customer RFQ documents, comparison with your own ERP actuals, or processing under ITAR or CUI controls that you cannot satisfy with a shared service.
Can AI price the quote?
It can inform pricing by surfacing comparable jobs and variance history, but we recommend the cost engine and the estimator keep authority over price. An unexplained model-generated price is hard to defend to a customer or an auditor.
Is this suitable for ITAR work?
The architecture lets you keep technical data on infrastructure you control and restrict access to authorized persons. Whether a given use is compliant is your determination, and Netray does not claim ITAR registration or certification.
How accurate is drawing note extraction?
It varies with drawing quality. Clean vector PDFs do well; scanned legacy drawings with handwritten notes do worse. That is why every finding shows its source region and why we measure accuracy on your own historical drawings before go-live.
Do we need a GPU server?
For a small team, a single workstation-class or server GPU is usually enough with a mid-sized open-weight model. Sizing depends on RFQ volume, drawing page counts and how many estimators query at once, which we estimate during discovery.
What if we use a different quoting tool later?
The retrieval and extraction layers are not tied to one vendor. Only the connector would change, which is a modest piece of work compared with rebuilding the knowledge base.
Is a simple checklist enough?
For low-volume shops with two estimators, a disciplined RFQ review checklist and good use of Paperless Parts' own features may be enough. AI becomes worthwhile when volume exceeds careful human review capacity or when historical data is rich but unused.
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Talk it through with an engineer who knows Paperless Parts
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.