AI agents and agentic workflows
Agents that plan and carry out multi-step tasks across your tools, then hand off to a person when judgement is needed.
- Tool and API calling
- Multi-agent orchestration
- Human approval steps
- Memory and context
AI agents and machine learning that do real work
We design, build and run AI that answers customers, reads documents, sees images and makes routine decisions for you, connected to your own data and systems and checked by your team where it matters.
24 AI and ML projects delivered9 agentic AI suites built24/7 work that continues outside office hours
What we deliver
Answer customers, read documents and make routine decisions automatically, around the clock. Every engagement is shaped around your goals, data and systems.
LLM apps and copilots
Assistants built into your product or internal tools that draft, summarise, search and answer in your tone of voice.
- Customer support bots
- Sales and ops copilots
- Multilingual replies
- Prompt and cost tuning
RAG knowledge assistants
Ask questions of your documents, policies and tickets and get answers with sources, kept up to date automatically.
- Document ingestion
- Vector search
- Cited answers
- Access control by role
Computer vision
Models that detect, classify and measure what is in images and video, from X-rays to shop floors and city streets.
- Object detection
- Medical imaging
- Video surveillance
- Face and liveness checks
NLP and document AI
Turn unstructured text, forms and scanned documents into clean, structured data your systems can use.
- OCR and extraction
- Classification
- Entity recognition
- Data standardisation
Voice AI
Speech-to-text and voice agents that transcribe calls and consultations and turn them into actions and records.
- Speech recognition
- Call summaries
- Clinical dictation
- Voice assistants
Predictive models and machine learning
Forecast demand, score risk and spot anomalies with models trained on your own historical data.
- Forecasting
- Classification and scoring
- Anomaly detection
- Recommendation engines
MLOps and deployment
Put models into production safely, with monitoring, evaluation and retraining so they stay accurate.
- Model APIs
- Evaluation and guardrails
- Monitoring and drift
- Cloud or on-premise
AI strategy and readiness
Find the use cases worth doing first, check your data and plan a proof of concept with a clear business case.
- Use-case workshops
- Data readiness review
- ROI estimates
- Proof-of-concept plan
How Technomous works
A clear, step-by-step process, so you always know what happens next.
- 01
Discover
We map the work you want AI to take on and pick the use case with the clearest return.
- 02
Prove it
A focused proof of concept on your own data shows accuracy and value before you commit.
- 03
Build and connect
We build the agent or model and connect it to your systems, data and approval steps.
- 04
Test and guard
We evaluate answers, add guardrails and keep people in the loop where it matters.
- 05
Launch and improve
We deploy, monitor quality and cost, and keep improving the model as you use it.
Tech stack and expertise
The platforms, languages and tools our team works with every day.
Language models
- OpenAI GPT
- Claude
- Llama
- Mistral
- Azure OpenAI
- Hugging Face
Agents and RAG
- LangChain
- LangGraph
- MCP
- Vector databases
- Embeddings
- Function calling
Machine learning
- Python
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- Pandas
Vision and speech
- OpenCV
- YOLOv8
- CNNs
- Speech to text
- OCR
- Medical NLP
Deployment
- FastAPI
- Docker
- Azure AI
- AWS
- REST APIs
- Laravel
AI and machine learning case studies
24 projects across 8 industries. Pick an industry to see what we have done for businesses like yours.
Products in this area
Ready-built software we deploy, customise and support.
Agentic AI
Agentic AI suites
A family of pre-built AI agent platforms. Each suite understands context, connects to your systems and carries out multi-step work such as resolving tickets, reconciling finance tasks or coordinating operations, then hands off to people when needed.
Features
- Contextual answers from your data
- Multi-step workflow execution
- System and API integrations
- Human hand-off
- Conversation memory
Clinical AIMawasqat
Clinical AI that turns unstructured speech and text into structured, interoperable clinical data, ready for EHRs, analytics and decision support.
Features
- Symptom detection
- Multi-model speech to text
- ICD-11 diagnosis extraction
- Medication and lab understanding
- Clinical summaries
Diagnostic AI
AI Virtual Radiologist
AI diagnostic triage for X-rays that flags high-risk cases first and gives radiologists explainable findings and preliminary reports inside their existing PACS workflow.
Features
- Automated X-ray analysis
- Fracture detection
- Chest pathology screening
- High-risk prioritisation
- Explainable findings
Imaging AI
Bone Fracture Detection
A computer vision model that analyses X-rays, detects and classifies fractures and writes an AI report in seconds.
Features
- Fracture detected or not
- Hairline, minor and major classes
- Severity identification
- Misalignment detection
- Soft-tissue swelling detection
Speech AI
Healthcare Speech-NLP
Converts doctor-patient conversations into standardised clinical data so documentation takes minutes instead of hours.
Features
- Conversation processing
- Speech recognition
- Medical entity extraction
- ICD-10 mapping
- SNOMED mapping
Hire AI, ML and RPA talent
Prefer to build in-house? Add dedicated specialists to your team, full-time, part-time or hourly, working in your tools and your time zone.
See all hiring optionsCommon questions
What clients usually ask before starting.
How long does an AI project take?
A proof of concept usually takes 2 to 6 weeks. A production AI agent or model typically takes 6 to 12 weeks, depending on integrations and data.
Which AI model will you use?
We pick the model that fits the task, budget and privacy needs, from OpenAI and Claude to open-source models you host yourself.
Do you work with healthcare and finance data?
Yes. Many of our AI projects are in healthcare and finance, where we work with standards such as ICD, SNOMED and FHIR and strict access controls.
Can the AI use our own data securely?
Yes. We connect to your data with role-based access, can keep it inside your own cloud, and never use it to train public models.
How do you stop the AI making mistakes?
We test against real examples, add guardrails, cite sources and route uncertain or high-risk cases to a person to approve.
Talk to us about AI and machine learning
Share a few details and a member of our team will get back to you soon to plan the next step.
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