01Tailored AI solutions
Models designed around your business goals — automating tasks and improving the experience of the people who use them.
AI serviceEnd-to-end delivery · ADS-01
From data audit to a model that stays right.
End-to-end AI software development — from data audit to deployed, monitored models on your infrastructure.
ScopeOne KPI, one owner
PoCOn your historical data
BuildProduction-grade, versioned
LiveMonitored and retrained
01/ 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, optimised 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, optimised workflows, ongoing improvement
02/ LiveInteractive · runs in your browser
A model that is not watched decays quietly.
A deployed model's error against lab results, day by day. Change the plant — a new crude slate, a recalibrated analyser — and watch the drift alarm catch it, the retrain get scheduled, and the error come back down after sign-off.
- Rolling error against the reference, with an agreed limit
- Drift alarm before the error breaches
- Retrain needs your sign-off — nothing changes silently
03/ What we offer
Inside this service
ADS·01
Data engineering and pipelines
Historian, LIMS, CMMS and document data made model-ready.
ADS·02
Model development and validation
Built against an agreed KPI, validated before go-live.
ADS·03
MLOps and deployment
Versioned, containerised, monitored models — on-prem or cloud.
ADS·04
Plant-system integration
OPC UA, Modbus and REST integration with your existing systems.
ADS·05
Monitoring and retraining
Performance dashboards, drift alarms and scheduled retraining.
ADS·06
AI strategy and scoping
Use-case shortlists ranked by value and data readiness.
04/ How it works in practice
What you actually get
ADS-01·F1
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
ADS-01·F2
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
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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 the team that built it
05/ Key features
What every engagement includes
Drag or scroll the strip.
02End-to-end integration
Full-cycle delivery, from ideation and model development to deployment inside your existing systems.
03Data-driven intelligence
Machine learning and deep learning that surfaces patterns and actionable insight from your operational data.
04Scalable and secure architecture
Systems that grow with your business while protecting your data with enterprise-grade controls.
06/ Tools and technologyBuilt with proven tooling
PyTorch
TensorFlow
scikit-learn
NumPy
pandas
DjangoStack
PythonPyTorchTensorFlowscikit-learnAzureAWSDocker
07/ ProcessHow we deliver
Step 01Scope the use case
One target KPI, one owner, agreed success criteria.
Step 02Audit the data
Historian, lab and document coverage checked for gaps.
Step 03Prove it
Proof of concept on your historical data.
Step 04Build for production
Hardened, tested, versioned and documented.
Step 05Deploy and monitor
Integrated with your systems, watched after go-live.
08/ MethodSMHcoders
“One measurable use case, proven on your data, running on your infrastructure — that is the whole method.”
SMHcoders · method
Free 45-minute consultation with an engineer: bring one problem, leave with a feasibility read.
09/ 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.
10/ Related services
Proof of concept · 4–8 weeks · your data