01Sentiment analysis
Decode opinion and tone from feedback, reviews and social channels to guide product and service decisions.
AI serviceText into data · NLP-03
Your shift logs already hold the answers. Make them queryable.
Extraction, classification and search over the documents your teams do not have time to read.
InputsShift logs · work orders · incident reports
FormatsPDF · scans (OCR) · email · CMMS exports
LanguagesEnglish · Arabic
OutputsEntities · classes · search index · summaries
DeploymentYour network · API into document systems
01/ Overview
Smarter communication and deeper insight from text
We improve how machines understand text, analyse language and generate natural responses — enabling precise, intelligent automation over the documents an operation produces every shift.
From extraction and classification to search, translation and sentiment, our NLP work integrates cleanly into the platforms you already run — chatbots, CMMS, document stores and more.
- Tailored NLP integrations for your specific goals and languages
- Automated analysis of large-scale text, replacing manual processing
- Proven in industrial, healthcare, finance and education settings
02/ LiveInteractive · runs in your browser
Paste a shift log. Get structure back.
Edit the log entry, or pick another. Equipment tags, faults, actions and times are pulled out, the entry is classified and routed — with human review when confidence is low. Then search the small archive in plain English: it knows feed pump A is P-101A.
- Entities: equipment, fault, action, time, shift
- Classification with a confidence and a routing rule
- Synonym-aware search — browser demo; the service uses trained transformer models
03/ What we offer
Inside this service
NLP·01
Entity extraction
Equipment tags, dates, actions and faults pulled from free text.
NLP·02
Document classification
Shift logs, work orders and reports sorted automatically.
NLP·03
Semantic search
Plain-English queries across your document store.
NLP·04
Report summarisation
Long reports condensed with references intact.
NLP·05
English and Arabic models
Bilingual pipelines for Gulf operations.
NLP·06
OCR and document parsing
Scans and PDFs turned into structured, searchable text.
04/ How it works in practice
What you actually get
NLP-03·F1
Structure from unstructured text
Maintenance records and shift logs hold equipment history nobody can query. Extraction models turn them into structured data.
- Tags, faults and actions extracted
- Works on scans via OCR
- Validated against your taxonomy
NLP-03·F2
Search that understands engineering
Search that knows P-101A and "feed pump A" are the same thing, and ranks results with their sources.
- Plain-English queries
- Tag-code and synonym aware
- Results ranked with sources
NLP-03·F3
Classification and routing
Incoming reports classified and routed to the right team, with human review below a confidence threshold.
- Logs, work orders, incident reports
- Auto-routing to the right team
- Human review below threshold
05/ More capabilities
NLP services we also provide
Drag or scroll the strip.
02Language translation
Accurate, real-time translation across languages for international support and content localisation.
03Speech recognition
Transcribe calls, handovers, meetings and recordings into searchable, actionable text.
04NLP-driven chatbots
Conversational assistants that understand intent and resolve complex queries in context.
06/ Tools and technologyBuilt with proven tooling
Hugging Face
PyTorch
OpenAI
LangChain
NumPyStack
PythonHugging FacespaCyElasticsearchTesseract
07/ ProcessHow we deliver
Step 01Corpus audit
Document types, volumes and languages mapped.
Step 02Taxonomy
Entities and classes defined with your SMEs.
Step 03Model training
Trained and tuned on your corpus.
Step 04Validation
Accuracy reviewed with your engineers.
Step 05Deploy and integrate
APIs into your document systems.
08/ MethodSMHcoders
“Your shift logs and work orders already hold the answers. NLP just makes them queryable.”
SMHcoders · method
Free 45-minute consultation with an engineer: bring one problem, leave with a feasibility read.
09/ Common questions
Our documents are scans, in mixed English and Arabic. Is that a problem?
No. OCR and bilingual English–Arabic pipelines are standard parts of our NLP stack for Gulf operations.
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