ChatGPT Integrations — SMHcoders
AI SERVICE // GPT-06

ChatGPT, inside your boundary

GPT connected to your systems safely — private endpoints, retrieval, guardrails and audit logs.

Free AI Consultation Talk to an engineer
PrivateAzure OpenAI or on-prem endpoints
ZeroTraining on your data
100%Prompts and answers logged
Role-basedAccess and usage metering
// Overview

Seamless communication with ChatGPT integration

As a ChatGPT integration service provider, we streamline support and lift engagement by bringing GPT models into your digital platforms — websites, applications and messaging channels — with intelligent, context-aware conversation.

We have helped organizations across sectors, including health tech, meet their goals with smart, automated communication. Scalable, results-driven integration, delivered safely inside your boundary.

>Natural language understanding that interprets intent, not keywords>Personalized responses, recommendations and content>Scales from startup volumes to enterprise traffic without losing quality
GPT INTEGRATION · PRIVATE ENDPOINTS
// What we offer

Inside this service

CHA-01
Private Azure OpenAI setup

Your tenancy, your region, your data boundaries.
CHA-02
Retrieval over your documents

Grounded answers from your engineering document store.
CHA-03
Report and summary generation

Drafts in your templates from your data.
CHA-04
Copilots inside your tools

GPT embedded in the systems your teams already use.
CHA-05
Function calling to your APIs

Read-only connections to CMMS, ERP and historians.
CHA-06
Governance and audit logging

Every prompt and answer logged and reviewable.
// How it works in practice

What you actually get

GPT-F1 · PRIVACY

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
DATA BOUNDARY
YOUR NETWORK // PRIVATE GPT ENDPOINT DOCUMENT STORE AUDIT LOG
PUBLIC INTERNET BLOCKED
NO TRAINING ON YOUR DATA
INTEGRATION MAP
GPT
COPILOT
DOCS — RAG
CMMS — READ ONLY
ERP — READ ONLY
HISTORIAN — READ ONLY
CONTROLLED CONNECTIONS ONLY
GPT-F2 · CONNECTION

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-F3 · GOVERNANCE

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
GOVERNANCE LOG // TODAY
09:41REPORT DRAFT — OPS TEAMLOGGED
10:02RAG QUERY — MAINTENANCELOGGED
10:15OUT-OF-SCOPE REQUESTBLOCKED
10:31WO SUMMARY — RELIABILITYLOGGED
// More capabilities

GPT services we also provide

GPT // EXTENDED SCOPE
Multilingual translationBreak language barriers with GPT-powered translation for users across countries and regions.
Personalized recommendationsProduct and content suggestions driven by user behaviour and preferences.
Prompt engineeringOptimized prompts that guide the model to high-quality, context-specific answers.
Foundation-model fine-tuningGPT-class models adapted to your documents, terminology and business rules.
// Tools and technology

Built with proven tooling

OpenAIOpenAI
LangChainLangChain
PineconePinecone
DjangoDjango
AZURE OPENAI OPENAI API LANGCHAIN PYTHON
// Process

How we deliver

01
Use-case scopingWhere GPT genuinely helps, ranked by value.
02
Endpoint and security setupPrivate deployment inside your boundary.
03
Retrieval buildYour documents indexed and connected.
04
Pilot groupMeasured trial with a real team.
05
Org rolloutAccess, training and governance scaled.
SMHCODERS // METHOD

GPT belongs inside your boundary: private endpoints, retrieval over your documents, every prompt logged.

Book a free consultation
// FAQ

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.