01Multilingual translation
Break language barriers with GPT-powered translation for users across countries and regions.
AI serviceGPT inside your boundary · GPT-06
ChatGPT, inside your boundary.
GPT connected to your systems safely — private endpoints, retrieval, guardrails and audit logs.
EndpointsPrivate — Azure OpenAI or on-prem
Training on your dataZero
Prompts and answers100% logged
AccessRole-based, usage metered
01/ Overview
Seamless communication with ChatGPT integration
We bring GPT models into your digital platforms — websites, applications, messaging channels and internal tools — with intelligent, context-aware conversation that streamlines support and lifts engagement.
We have helped organisations across sectors meet their goals with smart, automated communication: scalable, results-driven integration, delivered safely inside your boundary.
- Natural language understanding that interprets intent, not keywords
- Personalised responses, recommendations and content
- Scales from startup volumes to enterprise traffic without losing quality
02/ LiveInteractive · runs in your browser
Every request through the same gate.
Pick who is asking and what they want. Each request passes the role check, retrieval and tool policy before it reaches the model; connections to CMMS, ERP and the historian are read-only. Watch the governance log — including what gets blocked, and why.
- Role-based access before anything else
- Tools are read-only; writes are refused
- Every prompt and answer logged, blocked ones too
03/ What we offer
Inside this service
GPT·01
Private Azure OpenAI setup
Your tenancy, your region, your data boundaries.
GPT·02
Retrieval over your documents
Grounded answers from your engineering document store.
GPT·03
Report and summary generation
Drafts in your templates from your data.
GPT·04
Copilots inside your tools
GPT embedded in the systems your teams already use.
GPT·05
Function calling to your APIs
Read-only connections to CMMS, ERP and historians.
GPT·06
Governance and audit logging
Every prompt and answer logged and reviewable.
04/ How it works in practice
What you actually get
GPT-06·F1
Private by default
Endpoints live in your Azure tenancy or on-prem. Nothing is used to train the model, and residency rules are respected.
- Azure OpenAI or on-prem endpoints
- No training on your data
- Data residency respected
GPT-06·F2
Connected to your work
GPT is only useful when it can see your context — documents, work orders, historian trends — through controlled connections.
- Retrieval over your document store
- Read-only calls to CMMS and ERP
- Drafts in your report templates
GPT-06·F3
Governed from day one
Access is role-based, usage is metered, and every interaction is logged for compliance review.
- Role-based access
- Every prompt and answer logged
- Cost and usage dashboards
05/ More capabilities
GPT services we also provide
Drag or scroll the strip.
02Personalised recommendations
Product and content suggestions driven by user behaviour and preferences.
03Prompt engineering
Optimised prompts that guide the model to high-quality, context-specific answers.
04Foundation-model fine-tuning
GPT-class models adapted to your documents, terminology and business rules.
06/ Tools and technologyBuilt with proven tooling
OpenAI
LangChain
Pinecone
DjangoStack
Azure OpenAIOpenAI APILangChainPython
07/ ProcessHow we deliver
Step 01Use-case scoping
Where GPT genuinely helps, ranked by value.
Step 02Endpoint and security setup
Private deployment inside your boundary.
Step 03Retrieval build
Your documents indexed and connected.
Step 04Pilot group
Measured trial with a real team.
Step 05Org rollout
Access, training and governance scaled.
08/ MethodSMHcoders
“GPT belongs inside your boundary: private endpoints, retrieval over your documents, every prompt logged.”
SMHcoders · method
Free 45-minute consultation with an engineer: bring one problem, leave with a feasibility read.
09/ Common questions
Is ChatGPT safe for plant data?
With the right setup, yes. Endpoints live in your Azure tenancy or on-prem, nothing trains on your data, and every interaction is audit-logged.
What data do we need to start?
Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.
How long until we see results?
A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.
Where does it run, and who sees our data?
On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.
Who owns the result?
You do. Handover includes source code, documentation, training sessions and a retraining plan.
10/ Related services
Proof of concept · 4–8 weeks · your data