Company Expert AI / Private industrial knowledge
Keep your company’s expertise working. Even when experts move on.
Bring approved company documents, project experience and expert-reviewed know-how into a private AI assistant. Help every shift find useful guidance across planning, design, development, testing and service, entirely within your company’s infrastructure.
Company Expert AI is a tailored implementation around your business, your people and your approved knowledge. Start with one valuable workflow, then expand when the evidence supports it.
What is written
Approved wiki, documents, selected data
Why it matters
Worked examples, decisions, exceptions
Inside your approved infrastructure
Company Expert AI
Local model + approved-source retrieval
No internet dependency for the agreed answering workflow
Captured knowledge stays accessible. Important decisions stay human.
01 / Company Expert AI
A practical first scope
Start with the expertise one team repeatedly needs, such as a service procedure or the reasoning behind a design choice.
Possible output:
an agreed knowledge collection, captured expert explanations, source-linked guidance and reviewed evaluation results.
Your contribution:
a knowledge owner, subject-matter expert review time, approved sources and an IT operating contact.
Quality and value:
check source accuracy, important omissions and escalation, then compare searching, checking and correction time.
Next decision:
agree the scope and fee for the next stage using source readiness, reviewer capacity and operating costs.
02 / Company Expert AI
The real knowledge gap is between the documents.
A manual records the procedure. Your senior engineers, architects and subject-matter experts often know why it matters, which exceptions to watch for and when to stop and ask. When they retire, change roles or leave, that context can leave with them.
Company Expert AI helps preserve the knowledge your business can capture and validate, making it easier for colleagues to find and use after a handover. It cannot reconstruct undocumented experience that nobody has recorded or reproduce every expert’s judgment.
Repeated questions:
scarce specialists spend time explaining the same decisions to different teams.
Slow handovers:
new colleagues search scattered documents, project folders and conversations for the right context.
Avoidable rework:
teams repeat a past mistake, miss an exception or apply an outdated revision.
03 / Company Expert AI
Your company’s knowledge. Available to your people.
Teams ask questions in everyday language and receive source-linked guidance or useful first drafts drawn from the approved material in scope. The goal is less searching and repeated explanation, while qualified people retain authority over decisions.
Public AI models do not inherently know your private company history, internal design choices or operating context. A general-purpose assistant can become more useful when it is connected to approved private information. Company Expert AI brings that knowledge, access and operating model into an agreed company-controlled deployment.
Find relevant approved sources, with revision details where available, rather than rely on an answer without evidence.
Ask for missing context, flag conflicting records and escalate questions the available evidence cannot support.
Prepare a draft, checklist or explanation for the responsible person to review.
04 / Company Expert AI
Capture the documents. Preserve the reasoning behind them.
We work with your knowledge owners to select the company wiki, manuals, procedures, design records, project reports and relevant data that the system is allowed to use. Source ownership, revision, permitted audience and intended use are agreed before preparation.
Your specialists add the context between those records through structured interviews, worked examples and review sessions. We capture decision criteria, lessons learned, exceptions, trade-offs and escalation rules. Experts review the resulting material before it becomes guidance for others.
Written knowledge:
approved documents, wiki pages, selected data and recorded project decisions.
Expert reasoning:
why a decision was made, where an approach failed and which conditions change the answer.
Visible gaps:
identify missing or conflicting knowledge for expert capture, clarification or escalation.
05 / Company Expert AI
One knowledge foundation. Useful across the work.
The first deployment serves a defined team and workflow. These illustrative uses show how a reviewed knowledge foundation can support later stages without handing approval authority to the model.
Planning:
retrieve earlier assumptions, project lessons and constraints before preparing a work plan.
Design:
find applicable specifications and the documented reasons behind previous design choices.
Development:
prepare technical drafts and compare documented approaches, with engineers checking requirements and implementation.
Testing:
locate accepted methods, suggest a draft checklist and structure evidence for qualified review.
Service:
find product-specific guidance and known troubleshooting steps, asking for missing identifiers before proposing a response.
Guidance supports qualified people. It does not approve designs, release product or operate equipment.
06 / Company Expert AI
Make experience useful in real industrial work.
Illustrative applications to scope with your team. A useful answer depends on the available knowledge and the conditions covered in testing.
EPC and engineering:
before reusing a design, retrieve the project’s approved specifications and earlier review rationale. The engineer checks changed loads, operating conditions and client requirements.
Manufacturing and food:
help a new shift locate the current changeover instruction and captured exceptions. The supervisor follows approved procedures and verifies the line condition.
Oil and gas:
gather an equipment manual and reviewed maintenance history for an investigation. Authorized personnel decide the intervention under established permits and safety controls.
Pharmaceutical manufacturing:
retrieve controlled procedures and relevant investigation records for a qualified reviewer. The assistant does not make batch-release or compliance decisions.
Water and wastewater:
help service teams locate an approved maintenance procedure and lessons from similar equipment. Operators retain process-control decisions.
Gas chromatography and instrumentation:
in an illustrative application, organize approved instrument manuals, troubleshooting records and reviewed expert explanations into source-linked guidance. Reduce repeated evidence gathering while a specialist checks the instrument, configuration and current conditions, confirms applicability and authorizes any work.
07 / Your operating boundary
Private by design. Operated within your boundary.
The agreed offline workflow runs the user interface, model inference, document retrieval and required supporting services inside your approved infrastructure. Answering a question does not depend on internet access or an external AI service. Hardware, identity services, storage, licences and any integrations are assessed together before offline acceptance.
Offline operation does not automatically mean air-gapped or risk-free. We agree the deployment boundary, role-based access, source permissions, retained logs, backups and recovery with your IT owners. An answer must not expose material the user is not allowed to see.
Source owners + IT release process
Staged package + rollback version
Interface + retrieval + model
Local inference:
questions, retrieval and generation stay inside the agreed boundary for the offline workflow.
Controlled release:
an authorized owner approves source, software or model updates; packages enter through your approved transfer process, are checked and tested, then released with a rollback plan.
Across shifts:
users can access captured knowledge 24/7 when the local infrastructure is available. Uptime, capacity, maintenance windows, backup and support arrangements are scoped explicitly.
External connections:
remote support, external notifications or live cloud data are separate choices. They are not dependencies of the agreed offline answering workflow.
Optional technical detail
For your technical team: retrieval and tuning
Optional technical detail
For your technical team: retrieval and tuning
Retrieval works like an informed librarian: it finds relevant approved passages when a question is asked, so the answer can point back to evidence. Updating the source collection can refresh available facts without retraining the model, after your review and release checks.
Fine-tuning changes how the model behaves using prepared examples. It may help with specialist terminology, an output format or a repeatable task if evaluation shows a worthwhile improvement. It does not replace current sources, permission checks or expert review. Permission to use a document for search is not automatic permission to train on it.
A smaller language model may fit a bounded local workload. Model size alone does not establish quality, privacy or low cost. We assess languages, task difficulty, hardware, response time, concurrency and licence terms; training a model from scratch is not the default.
10 / Company Expert AI
You bring the expertise. We handle the AI work.
You do not need an in-house AI team for the engagement. NeoBram works with your business owner, subject-matter experts and IT team to scope, configure, evaluate and deploy the system. Your people still need time to explain the work, resolve conflicting knowledge and judge results.
1. Choose the first workflow:
identify repeated questions, affected users, business value and decisions that must remain human.
2. Prepare the knowledge:
inventory approved sources, capture expert reasoning and record the gaps.
3. Build and evaluate:
configure retrieval and the local model, test representative questions and review the errors with specialists.
4. Accept and hand over:
verify offline dependencies, access, recovery and user workflows against agreed acceptance criteria.
5. Keep it useful:
assign owners for new information, corrections, releases and periodic review before extending scope.
11 / Company Expert AI
A usable system, with clear ownership after handover.
The proposal defines the included knowledge collections, users, workflows, integrations and infrastructure responsibilities. It also identifies what needs further capture, engineering or validation.
Related manufacturing knowledge-assistant work is described in an anonymized engagement brief with its evidence limits. It provides adjacent experience to assess; it does not establish a deployment of this named product or its expert-capture and offline acceptance process.
Knowledge package:
an approved source inventory, captured expert explanations, glossary, known gaps and ownership record.
Configured deployment:
the agreed interface, local model and retrieval workflow, permissions and installation documentation.
Acceptance evidence:
a reserved evaluation set, reviewed results, important failure cases and operating boundaries.
Handover:
administrator and user training, operating instructions, backup and recovery procedures, update and rollback guidance.
Support choices:
scope a customer-operated handover, agreed support or an ongoing improvement engagement. Availability commitments, response times and any remote-access method are agreed separately.
12 / Company Expert AI
Measure better work, not more generated text.
Agree a baseline before the first workflow goes live. Compare time spent finding an answer, repeated SME questions, useful drafts accepted, substantive corrections and total review effort. Look for less searching, smoother handovers and fewer repeated explanations while keeping the quality threshold intact.
Count the full cost: expert preparation, data cleanup, hardware, operation, support and knowledge maintenance. Time recovered is not automatically cash saved. Any savings or quality improvement must be measured in your workflow.
Clear answers before you start
Questions and answers
Can it run entirely without internet access?
Yes, the agreed workflow can be deployed entirely inside your company’s infrastructure. We validate the model, retrieval, interface and required dependencies together, and agree how controlled updates reach the offline system. External notifications, live cloud feeds and remote support are separately scoped choices.
Does it preserve everything an expert knows?
No. It preserves the knowledge that can be captured, approved and tested. Structured interviews and worked examples help reveal unwritten reasoning, but missing experience and judgment remain gaps. The assistant should flag those gaps and escalate rather than pretend to know.
Does it replace our engineers or subject-matter experts?
No. It helps more people use approved knowledge. Specialists define good answers, resolve exceptions and retain approval for important decisions. It does not autonomously operate a factory, approve designs or replace safety systems.
Does uploading documents train a new model?
Not by itself. Retrieval searches approved material without retraining the model. Fine-tuning changes model behaviour using prepared examples and can be considered for a specific tested gap. It does not replace keeping source information current.
Can it give a wrong answer?
Yes. Source links, testing, boundaries and expert review help manage errors but cannot eliminate them. We test unsupported questions, outdated material and high-consequence mistakes, and agree when users must escalate.
Is it available 24/7?
It can support people across shifts without waiting for an individual specialist, subject to local infrastructure availability. Actual uptime depends on compute, network, storage, maintenance, recovery and the support arrangement agreed for your deployment.
Do we need our own AI team?
No in-house AI team is required for the engagement. Your business still needs a knowledge owner, access to subject-matter experts and an IT contact for deployment and operation. Their contribution is agreed during scoping.
Who owns the knowledge and the resulting system?
The contract defines rights to customer-specific knowledge, configuration, evaluation material and any model adapters, along with handover and export arrangements. The underlying model and software keep their own licence terms; exclusive ownership of a foundation model is not assumed.
What does an implementation cost?
A tailored proposal depends on the workflow, source condition, expert capture, languages, users, infrastructure, integrations, acceptance checks and support.
Start with one valuable knowledge workflow