AI and machine learning services.
Custom models, chatbots, vision systems and GPT integrations — scoped on one measurable use case, built on your data, deployed on your infrastructure.
AI Development Services
End-to-end delivery — data audit, model build, deployment and monitoring.
AI Chatbot Development
Grounded chatbots for operators and customers — web, Teams and WhatsApp.
Natural Language Processing
Extraction, classification and search over reports, logs and documents.
Predictive Modelling
Quality, emissions and equipment-health prediction, validated against your lab.
Computer Vision
Detection and inspection on camera feeds and engineering drawings.
ChatGPT Integrations
Private GPT endpoints, retrieval, guardrails and audit logs.
AI and ML Solutions
Our project portfolio and the model families behind it.
AI and ML software, tailored to how your operation runs
SMHcoders builds custom models and intelligent automation — predictive analytics, natural language processing, computer vision and generative AI — that optimise processes and cut cost in heavy industry.
Every engagement is scoped to your industry and your data: scalable, secure, and deployed on the infrastructure you already run.
Looking for the plant-side stack — soft sensing, MPC, RTO, digital twin? Industrial AI solutions
One measurable use case, one owner, agreed success criteria.
Coverage, quality and gaps checked before any commitment.
Proof of concept on your historical data.
Production-grade, tested, versioned and documented.
Integrated, monitored and retrained on schedule.
Four steps from first contact to kickoff
Reach out with the basics of your project. A short brief helps us respond quickly, with the right engineer.
A no-obligation session to assess feasibility, explore the technical options and align on your goals.
A clear estimate and a tailored proposal — timeline, deliverables and scope — with full transparency from day one.
A multidisciplinary team starts work under agile practice, with open communication through to launch.
“One measurable use case, proven on your data, running on your infrastructure — that is the whole method.”
SMHcoders · method
Which service should we start with?
The one attached to your most expensive open problem. In the consultation we rank your candidate use cases by value and data readiness — that ranking picks the service.
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.