Private LLM assistants inside the enterprise perimeter
Local language models on the customer's own infrastructure: search across technical documentation and procedures, an assistant for engineers and dispatchers, reporting automation. No data is sent to external clouds, which meets the information-security requirements of national companies. Not a single token leaves the enterprise perimeter, and every answer comes with a citation down to the clause and revision of the source document.
What problems it solves
Pain
An engineer spends 20–30% of their time searching through hundreds of standards, procedures, P&IDs and equipment data sheets
Solution
A RAG assistant on a local LLM: answers cited down to the clause and revision of the document, parsing of tables and drawings
0.5–1 hour a day per engineer × hundreds of engineering staff
Pain
Errors caused by outdated standard revisions — from project rework to an incident
Solution
A single corpus of normative documents (ST RK, GOST, API, ASME, internal company standards) with revision-currency control and role-based access rights
Lower risk of errors from outdated documents
Pain
Cloud assistants (Copilot, ChatGPT) fail information-security review: corporate data leaves the perimeter
Solution
Deployment of open LLMs (Qwen/Llama class) on the customer's GPU infrastructure, SSO and a role-based access model
Full compliance with information-security and data-localization requirements
What data we work with
- Normative and technical documentation (ST RK, GOST, API, ASME, internal company standards)
- Design documentation, equipment data sheets, P&IDs
- Procedures, acts and reports, knowledge bases
- Document management and maintenance (CMMS) systems
What the customer gets
- An LLM with a RAG pipeline deployed inside the customer's perimeter
- An indexed document corpus for one domain (300–1,000 documents in the pilot)
- A web interface for the assistant with source citations, SSO and access rights
- Answer-quality metrics on a control set of engineers' questions
What problem a local LLM assistant solves
An engineer at a large plant spends 20–30% of their time searching through hundreds of standards (ST RK, GOST, API, ASME), internal company standards, design documentation and equipment data sheets. The cost of an error caused by an outdated standard revision ranges from project rework to an incident. Tengizchevroil confirmed the value of the approach by building its own LLM assistant for engineers over 300+ technical specifications; Kazakhstan Temir Zholy (KTZ) processes 5,000+ normative documents with AI assistants. Most companies need the same result — without spending years building their own data-science team.
Why on-premise rather than ChatGPT/Copilot
| Criterion | Cloud LLMs | Local LLM inside the perimeter |
|---|---|---|
| Where the data goes | External data centers outside Kazakhstan | Nowhere: the customer's servers |
| National companies' information-security requirements | Usually fails review | The default architecture |
| Kazakhstan's personal data law | Risk of violating database localization | Compliant |
| Access to internal documentation | Requires export outside the perimeter | RAG inside the perimeter |
| Document version control | None | Citation down to the clause and revision |
| Cost at scale | Grows with every query | Fixed infrastructure |
- Criterion
- Where the data goes
- Cloud LLMs
- External data centers outside Kazakhstan
- Local LLM inside the perimeter
- Nowhere: the customer's servers
- Criterion
- National companies' information-security requirements
- Cloud LLMs
- Usually fails review
- Local LLM inside the perimeter
- The default architecture
- Criterion
- Kazakhstan's personal data law
- Cloud LLMs
- Risk of violating database localization
- Local LLM inside the perimeter
- Compliant
- Criterion
- Access to internal documentation
- Cloud LLMs
- Requires export outside the perimeter
- Local LLM inside the perimeter
- RAG inside the perimeter
- Criterion
- Document version control
- Cloud LLMs
- None
- Local LLM inside the perimeter
- Citation down to the clause and revision
- Criterion
- Cost at scale
- Cloud LLMs
- Grows with every query
- Local LLM inside the perimeter
- Fixed infrastructure
What the assistant can do
- Search across standards and technical documentation with citations: an answer with a reference to the clause and revision of the document
- Engineer's and dispatcher's assistant: answers based on procedures, instructions and logs
- Parsing of tables and drawings: vision models for P&IDs, data sheets and scans
- Reporting automation: drafts of acts, protocols and reports from the plant's templates
- Access control: SSO, role-based rights — the assistant answers only from documents the user is allowed to see
What effect it delivers
Saving 0.5–1 hour a day per engineer: with 200–500 engineering staff and a fully loaded hourly cost of $15–25, that is $1–4 million a year, plus a lower risk of errors from outdated document revisions. The effect is “soft”, so projects are structured at a fixed price with measurable answer-quality criteria.
FAQ
Frequently asked questions
What chief engineers, security and procurement teams usually ask — with straight answers.
Next step
Request a demo on your documents
Tell us about your task — we'll come back with a data audit plan and an effect estimate within two business days.
- 1A 30-minute call: the task, the data, who signs off on security
- 2Data audit or a retrospective analysis on an anonymised export
- 3An 8–12 week pilot with success criteria agreed before the start
NDA from the first contact
Reply within one business day