Industrial AI solution

Factory-Floor AI Chatbot SOPs, Troubleshooting & Operator Knowledge

Instant answers for operators and technicians from SOPs, work instructions, equipment manuals and maintenance history. Multilingual, tablet-ready and MES-aware.

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Operator using a factory-floor AI chatbot on a tablet

Acceptance before scale

Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

Direct answer

NeoBram builds factory-floor AI chatbots that give operators and technicians instant answers from SOPs, work instructions, equipment manuals and maintenance history - multilingual, tablet- or HMI-ready, MES-aware, and deployed on-premise with full audit trail.

Private deployment available

Why evaluate it

Operator Knowledge Walks Out the Door Every Day.

The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

A factory-floor AI chatbot turns SOPs, equipment manuals, maintenance history and tribal knowledge into a single conversational interface. Operators ask in their native language and get the exact procedure, torque spec or troubleshooting tree in seconds.

We deploy on-premise or in your private cloud with full RAG over your documents, MES-aware context (current product, line state, recent alarms), and a feedback loop so unanswered questions become new SOP entries. No data leaves your network.

Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

Candidate capabilities

What a production solution may need to do.

Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

SOP & manual search

Conversational search over your full SOP library, equipment manuals, MSDS, change-over guides and quality procedures with citation back to the source document and page.

Cited answers - every response links to source SOP
Image and diagram retrieval (not just text)
Version-aware (always current approved revision)
PDF, DOCX, scanned and handwritten supported

Troubleshooting assistant

Step-by-step troubleshooting trees built from manuals plus past maintenance history. Asks operator clarifying questions, narrows root cause, recommends action.

Interactive decision trees from OEM manuals
Past-case retrieval from CMMS history
Step-by-step recommended actions with safety callouts
Auto-escalate to maintenance if outside operator scope

Multilingual & voice

Operators ask in their native language (Hindi, Marathi, Spanish, Vietnamese, Mandarin, Arabic and more). Voice input via Whisper for hands-free use in noisy areas.

30+ languages out of the box
Voice input via Whisper (offline)
Translation of source SOPs preserved with English original
Per-operator language preference

MES context & feedback loop

Chatbot knows the current product, line state, recent alarms and operator role - so the same question gets the right answer for the current context. Unanswered questions queue for SOP authors to address.

Current SKU and batch context from MES
Recent alarms and downtime context
Role-aware answers (operator vs maintenance vs QA)
Unanswered-question queue feeds SOP improvements

Architecture context

Select components after the boundary and test are clear.

The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

Foundation models

Llama 3.3 70B
On-premise open-weight LLM
Mistral / Mixtral
Efficient on-prem alternative
GPT-4o / Claude 3.5
Cloud option if approved

RAG stack

LlamaIndex / LangChain
RAG orchestration
Qdrant / Weaviate / pgvector
Vector store
BGE / E5 embeddings
Multilingual retrieval
Unstructured.io / LayoutLM
PDF & SOP parsing

Edge UI

PWA on Zebra / Honeywell tablets
Rugged shop-floor
Ignition Perspective embed
Inside HMI
Voice (Whisper) input
Hands-free in noisy areas

Integration

SAP S/4HANA / SAP PM
Work-order & batch context
Werum PAS-X / Siemens Opcenter
MES context
Maximo / SAP CMMS
Maintenance history
SharePoint / Documentum
SOP and manual source

Planning ranges

A standard path from decision to operation.

Discovery and qualification

1-2 weeks

Named workflow, owner, baseline, risks and go/no-go questions.

Readiness assessment

2-4 weeks

Data, integration, security, value and operating-readiness findings.

Technical proof of value

4-6 weeks

A bounded test on representative data with documented limitations.

Production pilot

8-12 weeks

One controlled workflow, integrated and evaluated with real users.

Enterprise or multi-site rollout

3-6+ months

Phased scale-out, monitoring, support and change management.

AI capability or CoE programme

3-6+ months

Governance, delivery methods, reusable assets and team enablement.

These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

Production safeguards

Private deployment is one control, not the whole control system.

Data boundary

Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
Document data flows, storage, deletion, backups and support access before production.
The customer approves every interface and any permitted external connection.

Model and application controls

Evaluate representative cases, uncertainty and harmful failure modes before use.
Use suitable access control, input handling and output guardrails for the selected risk.
Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

Traceability and operation

Define identity, roles, logs, monitoring, updates, backup and incident handling.
Keep the evidence needed to investigate outputs and reproduce important decisions.
Assign a named business and technical owner for production operation.

Human authority

Domain, quality, safety and regulatory owners retain decision authority.
Compliance depends on the implemented system and the customer's validated controls.
Escalation and safe fallback are part of the acceptance criteria.

Limitations to plan for

Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

Buyer questions

Direct answers before you plan a pilot.

Does this require sending shop-floor data to the cloud?+

No. We deploy on-premise with open-weight models (Llama 3.3 70B, Mixtral) on your GPU servers. No SOPs, operator questions or MES data leave your network. Cloud deployment with Azure OpenAI or AWS Bedrock is available if your security policy permits, but on-premise is the default for shop-floor use.

How do you keep answers grounded in our actual SOPs (no hallucinations)?+

Retrieval-Augmented Generation with strict grounding: the model is instructed to answer only from retrieved SOP passages and to refuse if context is insufficient. Every answer cites the source SOP and page. We tune the retrieval pipeline against a gold-standard test set of operator questions before go-live and monitor citation accuracy in production.

How do operators use it on the line - tablet, HMI, voice?+

Most deployments use rugged Zebra or Honeywell tablets running a Progressive Web App, mounted at the workstation or carried. For control rooms we embed inside the HMI (Ignition Perspective, Wonderware). Voice input via Whisper is offered for hands-free use in noisy or PPE-restricted areas.

What happens when SOPs are updated?+

Your SOP source (SharePoint, Documentum, Veeva Vault) is monitored. New approved revisions are re-indexed within minutes. Operators always get the current approved revision; old revisions are retained for audit but not surfaced by default.

Is this GxP-compliant for pharma shop floor?+

Yes - we deploy a GxP-validated variant with 21 CFR Part 11 audit trails on every operator interaction (question, retrieved sources, response, operator action). The chatbot is positioned as a decision-support tool, not a controlling system. Validation deliverables (URS, IQ/OQ/PQ) are included for regulated environments.

What's a realistic pilot scope?+

This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.

Bring one workflow

Define the evidence, boundary and acceptance test together.

Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

Plan the first project