Tulip + private AI
AI for Tulip: Grounded Answers From Operator Apps, Tables and Your ERP
Short answer
AI for Tulip reads operator-app data held in Tulip Tables and completion records through the Tulip API, joins it to ERP orders and materials, and lets engineers and supervisors ask plain-English questions with the source shown. Writes back to Tulip or the ERP go through Connectors and an approval step, and the model can run privately if data must stay on-prem.
- ERP
- Tulip, Tulip Tables, Tulip Apps
- Industries
- Medical Devices, Electronics, Automotive, Pharmaceuticals
- Written for
- Continuous Improvement Manager
Tulip is a no-code frontline operations platform. Engineers build apps that guide operators step by step, bind steps to machines and sensors through the Machine data source, store structured data in Tulip Tables, and visualize results in Analytics. Because apps are built by process engineers rather than IT developers, a Tulip deployment tends to grow quickly into dozens of apps, each with its own table schemas and variable names.
That speed is the strength and the governance challenge. A quality engineer who wants to know which operators were guided through a revised work instruction when a defect appeared has to find the right app, the right table and the right completion record, then reconcile with ERP work orders and lots. Tulip has built-in AI assistance for app creation and analysis, and for many teams that is the right starting point.
A separate private layer is useful in three situations: when data may not be sent to a vendor-hosted model, when you want one assistant across Tulip, ERP, MES and quality documents, and when you want an audited agent that can propose actions such as creating a nonconformance in the ERP, with a human approving before anything happens.
This page describes what that looks like in practice, which Tulip constructs it touches, and the limits worth stating up front, such as API rate limits, table design and the fact that AI is only as good as the data your apps capture.
What usually gets in the way
The problems we hear most from continuous improvement manager teams running Tulip.
App sprawl without a common data model
Each app creates its own tables and variables. Without conventions, cross-app questions require someone who knows every app, and analytics diverge by team.
Operator data does not reach the ERP cleanly
Completions, scrap and inspection results are captured in Tulip but reach the ERP through ad hoc Connectors or manual entry, leaving two versions of the truth.
Free-text and photo evidence goes unused
Operator comments and images are stored with completions but rarely analyzed, so recurring issues stay invisible until an audit or customer complaint.
Citizen-built apps need change control
Regulated plants must show which app version an operator used and who approved it. Reconstructing that history during an investigation is slow.
Hosted-model concerns
Customer contracts and export rules can prevent sending production data, drawings or photos to a vendor-hosted or public model.
Where AI earns its place in Tulip
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Natural-language questions over Tulip Tables
Engineers ask about cycle times, first-pass yield, or which stations have the most rework, and get answers based on table records and app completions with the query shown.
Touches: Tulip Tables, app completions, step timings, station and operator records
Outcome: Faster answers than building a new Analytics view; depends on consistent table design.
Work order and material context
Combine a Tulip completion with ERP work order, BOM and lot data, so the answer to a defect question includes material and supplier lots.
Touches: ERP production orders, BOM components, lot records, Tulip completions
Outcome: Reduces manual lookups when investigating a defect or deviation.
Operator comment and photo triage
A private multimodal or text model groups comments and flags images for review, proposing categories for an engineer to confirm.
Touches: Completion comments, attached images, defect tables
Outcome: Surfaces recurring issues; accuracy varies with image quality and requires review.
Nonconformance drafting
When an operator logs a failed inspection, the agent drafts a nonconformance with context (part, lot, station, instruction version) for approval before it is sent to the ERP or QMS.
Touches: Inspection tables, ERP quality notifications, QMS records
Outcome: Cuts duplicate data entry; a human always approves creation.
App usage and version audit
Reports which app versions were used on which orders, helping answer an auditor or complaint investigation.
Touches: App version history, completions, work orders
Outcome: Faster evidence assembly; relies on versioning discipline in Tulip.
Training and onboarding signals
Highlights steps where new operators take longer or make more errors and suggests where instructions need work.
Touches: Step timings, error logs, operator qualification tables
Outcome: Input to instruction improvement; individual performance data is scoped and governed.
Machine data context
Combines Tulip machine data with app events to explain stops and quality escapes at a station.
Touches: Machine data source tags, station events, defect records
Outcome: Better context for stops; limited to machines wired into Tulip.
Reference architecture
A private layer calls the Tulip API for tables and records with a scoped API key, optionally subscribes to events via Automations and webhooks, and uses read-only ERP access for context. Open-weight models run on local GPUs. Write-backs use Tulip Connectors or ERP APIs behind a human approval queue.
- 1
Tulip API access
Scoped API credentials to read Tables, records and app metadata, respecting Tulip rate limits and pulling incrementally.
- 2
Event ingestion
Optional Automations that send completion or inspection events to the AI layer for near-real-time triage.
- 3
Cross-system join
A mapping between Tulip station, order and lot identifiers and ERP keys, with read-only ERP connectors.
- 4
Private model serving
Open-weight text and vision models via vLLM or Ollama on local GPUs, with no external inference.
- 5
Approval and audit
Proposed records are queued for human approval; all prompts, queries and outputs are logged with user identity.
Integration notes for your ERP team
- Use Tulip API keys scoped to the workspace and needed tables; rotate them and store them in your secrets manager.
- Pull incrementally using timestamps or record updated fields, and respect Tulip's API rate limits instead of bulk-reading tables.
- Standardize table naming and key fields (order, lot, station, operator ID) before building the join to the ERP.
- Use Tulip Connectors for governed outbound calls to ERP APIs; keep the AI layer's proposals separate from the approved writes.
- Images and attachments should be fetched through the API on demand and cached on-prem, not copied to external services.
- Track app versions in your index so answers can state which instruction version applied.
Deployment options
Private cloud tenant
Most Tulip customers, since Tulip itself is cloud-based
The AI layer runs in your cloud account or data center and pulls via API, keeping inference private.
On-prem with Edge connectivity
Plants with strict OT/IT segmentation
Station-level data stays local; only selected records are synced for analysis.
Air-gapped analysis environment
Defense and export-controlled work
Periodic governed exports into an isolated environment for analysis, with no return path.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
FDA QMSR (21 CFR 820) and ISO 13485
AI drafts are labeled and approved by a person; records of record stay in Tulip, the ERP or QMS. Document the intended use for your quality system.
21 CFR Part 11
No e-signature or record creation by the model. Signatures remain inside validated systems.
IATF 16949
Process-control and traceability answers cite underlying records for audit.
GDPR and works council rules
Operator-level data is scoped, minimized and excluded from performance profiling unless agreed.
ITAR / export control
Private or air-gapped deployment keeps controlled technical data within your boundary.
How an engagement runs
Phase 1 . 2 weeks
App and table review
- -Inventory of apps, tables and Connectors
- -Priority questions from engineers and quality
- -Data classification and policy check
Phase 2 . 3-4 weeks
Data model and join
- -Standard keys across Tulip and ERP
- -Scoped API credentials and ingestion
- -Evaluation set
Phase 3 . 4-6 weeks
Pilot
- -Natural-language Q&A over Tulip data
- -Nonconformance drafting with approval
- -Accuracy and usage report
Phase 4 . 4-6 weeks
Scale
- -More apps and sites
- -Version audit reporting
- -Runbook and training
- -Support plan
Questions to ask any vendor, including us
A short list that separates real Tulip AI work from a chatbot demo.
- Which Tulip data leaves our environment, and where is inference performed?
- How are API keys scoped and rotated?
- How do you handle inconsistent table design across apps?
- Can the assistant create records, and what approval is required?
- How do you treat operator-level data and works council constraints?
- What does Tulip's own AI cover that we should use first?
- How are images handled, stored and deleted?
Frequently asked questions
Does Tulip already have AI, and why add another layer?
Tulip provides AI features for building apps and analyzing data, and you should evaluate them first. A separate layer helps when data must stay in your environment, when you need one assistant across Tulip, ERP, MES and documents, or when you want governed agents that draft records for approval.
How does the AI read data from Tulip?
Through the Tulip API using a scoped key to read Tables and records, optionally supplemented by Automations that push events. Reads are incremental and rate-limit aware. The AI layer does not run inside Tulip.
Can it write to Tulip Tables or the ERP?
It can propose records, such as a nonconformance, but writes go through an approval step and use Tulip Connectors or ERP APIs under a named user or service account. Read-only is the default, and many sites stay read-only for the first phase.
What if our apps are inconsistent?
That is common. We start with the highest-value apps, agree on key fields such as order, lot and station, and build a mapping layer. Accuracy improves with better table design, and we will tell you where data capture, not AI, is the limit.
Is operator data handled differently?
Yes. Operator identifiers can be pseudonymized, performance profiling is excluded by default, and scope is agreed with HR and works councils where relevant. The goal is process insight, not individual surveillance.
Can it analyze photos from inspections?
A local vision model can classify or describe images and flag candidates for review. Treat it as triage, not a replacement for an inspector, and measure accuracy on your own images before relying on it.
How long does a pilot take?
Typically 8 to 12 weeks for one product line or workflow: two weeks of review, a few weeks to standardize keys and ingest data, then a measured pilot. Timelines depend on table consistency and security approval.
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Talk it through with an engineer who knows Tulip
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.