AI Development Services — SMHcoders
AI SERVICE // ADS-01

AI Development Services

End-to-end AI software development — from data audit to deployed, monitored models on your infrastructure.

Free AI Consultation Talk to an engineer
ScopeOne KPI, one owner
PoCOn your historical data
BuildProduction-grade, versioned
LiveMonitored and retrained
// Overview

Excel in emerging tech with end-to-end AI services

Begin your AI-powered transformation with SMHcoders as your software development partner. We cover the whole chain — strategic ideation, data engineering, model development and seamless integration into the systems you already run.

Our expertise spans custom model development and the fine-tuning of foundation models such as GPT to your own data and business rules. We do not just build AI tools; our AI-as-a-Service model keeps them running, optimized and improving long after go-live.

>Strategy and ideation through to integration and support>Custom models and fine-tuned foundation models (GPT)>AI-as-a-Service: automation, optimized workflows, ongoing improvement
AI DEVELOPMENT · STRATEGY TO INTEGRATION
// What we offer

Inside this service

AI-01
Data engineering and pipelines

Historian, LIMS, CMMS and document data made model-ready.
AI-02
Model development and validation

Built against an agreed KPI, validated before go-live.
AI-03
MLOps and deployment

Versioned, containerized, monitored models — on-prem or cloud.
AI-04
Plant-system integration

OPC UA, Modbus and REST integration with your existing systems.
AI-05
Monitoring and retraining

Performance dashboards, drift alarms and scheduled retraining.
AI-06
AI strategy and scoping

Use-case shortlists ranked by value and data readiness.
// How it works in practice

What you actually get

ADS-F1 · DELIVERY

From use case to production

We scope one measurable use case, prove it on your historical data, then take it to production — no open-ended research projects.

>Measurable KPI agreed up front >PoC on historical data first >Documented handover and training
DELIVERY PIPELINE
SCOPE — KPI AGREED POC — HISTORICAL DATA PRODUCTION BUILD LIVE + MONITORED ONE USE CASE AT A TIME
DEPLOYMENT BOUNDARY
YOUR NETWORK // PRIVATE PLANT-SIDE SERVER YOUR AZURE / AWS MODELS + DATA
PUBLIC INTERNET BLOCKED
NOTHING LEAVES WITHOUT SIGN-OFF
ADS-F2 · INFRASTRUCTURE

Built on your infrastructure

Models run where your data lives — on plant-side servers, your Azure or AWS tenancy, or an air-gapped network.

>On-prem, Azure or AWS >Data stays inside your network >Role-based access and audit logs
ADS-F3 · SUPPORT

Maintained after go-live

A model that is not monitored decays quietly. We watch performance, catch drift and retrain on a schedule you approve.

>Live performance dashboards >Drift detection and retraining >Support from UAE and Pakistan teams
MODEL PERFORMANCE // LIVE
DRIFT WATCH — ARMEDRETRAIN — SCHEDULED
// Key features

What every engagement includes

ADS // ENGAGEMENT CHECKLIST
Tailored AI solutionsModels designed around your business goals — automating tasks and improving the experience of the people who use them.
End-to-end integrationFull-cycle delivery, from ideation and model development to deployment inside your existing systems.
Data-driven intelligenceMachine learning and deep learning that surfaces patterns and actionable insight from your operational data.
Scalable and secure architectureSystems that grow with your business while protecting your data with enterprise-grade controls.
// Tools and technology

Built with proven tooling

PyTorchPyTorch
TensorFlowTensorFlow
scikit-learnscikit-learn
NumPyNumPy
pandaspandas
DjangoDjango
PYTHON PYTORCH TENSORFLOW SCIKIT-LEARN AZURE AWS DOCKER
// Process

How we deliver

No open-ended research projects: each stage has a deliverable and a sign-off.

01
Scope the use case

One target KPI, one owner, agreed success criteria.

02
Audit the data

Historian, lab and document coverage checked for gaps.

03
Prove it

Proof of concept on your historical data.

04
Build for production

Hardened, tested, versioned and documented.

05
Deploy and monitor

Integrated with your systems, watched after go-live.

SMHCODERS // METHOD

One measurable use case, proven on your data, running on your infrastructure — that is the whole method.

Book a free consultation
// FAQ

Common questions

We have no AI team — can we still do this?+

Yes. We deliver the full chain — data engineering, modelling, deployment — and train your engineers to run and extend it after handover.

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.