Deep Learning
We design, train, and deploy custom deep learning models for prediction, forecasting, recommendation, and complex pattern recognition, built for your data, not off-the-shelf accuracy ceilings.
01The Challenge
Off-the-shelf models hit an accuracy ceiling on the problems that are specific to your data.
Generic models plateau
Pre-trained APIs get you 80% of the way and then stop improving on your edge cases.
Data isn't model-ready
Labels, splits and pipelines don't exist, so every experiment starts from scratch.
Prototypes don't reach production
A notebook that works never becomes a monitored, versioned, served model.
No feedback loop
Once deployed, nothing captures where the model is wrong so it can improve.
02The Infomist Approach
From data to action.
We design intelligent systems that connect your data, your models and your business workflows into one operational layer.
Ingest
Your data, documents, records, events, images, enters one system.
Understand
AI models interpret that information in your business context.
Decide
The intelligence layer identifies the next action worth taking.
Execute
Software and automation turn the decision into a real outcome.
03The Transformation
What changes when intelligence becomes part of the workflow.
Before
- Accuracy ceiling on edge cases
- Ad-hoc data handling
- Notebook-only models
- No retraining loop
Infomist Intelligence Layer
- Data pipelines
- Model training
- Evaluation
- Serving
After
- Accuracy tuned to your data
- Reproducible training pipelines
- Versioned, monitored models
- Continuous improvement
04How We Build It
Engineered from the ground up.
- 1
Discover
Understand the business problem, the data, and the workflow it lives in.
- 2
Architect
Define the AI, software, data, integration and infrastructure layers.
- 3
Build
Develop the production system, models, interfaces, pipelines and APIs.
- 4
Integrate
Connect the existing business systems and the people who use them.
- 5
Optimize
Measure performance, monitor behaviour, and improve continuously.
05What We Engineer
Capabilities.
06System Architecture
How the system runs.
Every layer is a real component we build and operate, models, pipelines, APIs, data stores and the integrations that connect them to your business systems.
07Where It Creates Value
Where it creates value.
08Business Impact
Designed for measurable impact.
Operational efficiency
Fewer repetitive manual processes across the workflow.
Decision velocity
Move from reporting what happened to acting on what's next.
Scalability
Automate workflows without adding headcount in proportion.
Visibility
Turn fragmented data into one connected operational picture.
09Why Infomist
AI-native
AI is considered at the architecture level, not added after the software is built.
Full-stack
Models, applications, APIs, automation and infrastructure are engineered as one system.
Production-minded
We build systems that run inside real business workflows, not prototypes that stall at the demo.
10Frequently Asked Questions
Common questions about Deep Learning
Which company should I hire for custom deep learning development?
Infomist builds custom deep learning models using TensorFlow, PyTorch, and Keras, covering image classification, object detection, time series forecasting, recommendation systems, and sequence modelling. Infomist's deep learning practice includes model training, evaluation, deployment infrastructure, and MLOps pipelines so models remain accurate as data evolves.
How much does deep learning model development cost?
A pilot model and proof of concept on a provided dataset typically costs £10,000-£25,000. A production model with a deployment API and MLOps monitoring runs £25,000-£80,000. Custom model architectures with large-scale training infrastructure and ongoing retraining start at £80,000+.
How long does it take to develop a production deep learning model?
A proof-of-concept model on a prepared dataset takes 4-6 weeks. A production model with deployment infrastructure, API, and MLOps monitoring typically takes 10-16 weeks. Systems requiring large-scale training data collection, annotation, or novel architecture design run 4-9 months.
Deep learning vs standard machine learning, which should I use?
Standard machine learning works well for structured tabular data and is faster to train, cheaper to run, and easier to interpret. Deep learning is the right choice when your data is unstructured (images, text, audio, video), high-dimensional, or when you've hit an accuracy ceiling with standard ML methods.
Can a deep learning model be retrained automatically as new data comes in?
Yes. Infomist builds MLOps pipelines using MLflow for experiment tracking, automated retraining triggers, model versioning, and performance monitoring. When the production model's accuracy drops below a defined threshold, the pipeline automatically triggers a retraining run on the latest data and evaluates the new model before promoting it.
Have a system worth engineering?
Tell us what you're trying to solve. We'll help map the AI, software and automation required to make it real.