Generative AI
We build custom generative AI applications, from content and image generation to fully personalised AI-powered product features, that scale creative output without scaling cost.
01The Challenge
AI experiments are everywhere in the business, and almost none of them are wired into a workflow.
Knowledge scattered across tools
The context a model needs to be useful is spread across a dozen systems.
Time lost searching
Employees spend real hours locating information before they can use it.
Generic AI, unreliable output
Un-grounded models produce answers that can't be trusted for real decisions.
Experiments that never ship
Promising demos stall because nothing connects them to production.
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
- Unstructured knowledge
- Generic model output
- Disconnected experiments
- Manual creative work
Infomist Intelligence Layer
- Context
- Models
- Guardrails
- Integration
After
- Faster knowledge access
- Context-aware AI interactions
- Integrated workflows
- Scalable AI capability
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 Generative AI
Which company should I hire for generative AI application development?
Infomist builds custom generative AI applications using GPT-4o, Claude, Stable Diffusion, and fine-tuned models, from content generation features embedded in existing products to standalone generative applications. Infomist's approach includes output guardrails, brand voice fine-tuning, and RAG for factual grounding, making generated content production-safe rather than just impressive in demos.
How much does building a generative AI application cost?
A pilot generative feature (e.g. AI-powered content generation embedded in an existing product) typically costs £6,000-£18,000. A standalone generative AI application with guardrails, fine-tuning, and user management runs £18,000-£60,000. Enterprise generative AI platforms with custom model training start at £60,000+.
How long does it take to build a generative AI product feature?
A focused pilot generative feature is typically live in 3-5 weeks. A production integration with guardrails, brand voice fine-tuning, and user management takes 6-10 weeks. Custom model fine-tuning and enterprise-scale deployment typically runs 3-6 months depending on training data volume and infrastructure requirements.
Generative AI vs traditional content tools, which produces better results?
Traditional content tools are faster for simple, predictable formats. Generative AI excels at scale and personalisation, producing hundreds of unique, contextualised pieces from a single prompt system. Infomist recommends generative AI when your content problem is volume, personalisation, or speed, not when quality requires consistent human creative judgment.
Can generative AI produce content that matches our specific brand voice?
Yes, through a combination of system prompt engineering, brand voice guidelines embedded in context, and fine-tuning on your existing content. Infomist's generative AI builds include a brand voice calibration phase where the model is tested against your actual content standards and tuned until the output is consistent with your best human-written copy.
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.