Generation constrained by your rules, not the model’s defaults
Drafting, summarising and asset creation that follows your tone, your templates and your approval process.
2 wks
To a working POC
15
AI capabilities we build
9
Years in business

What you get
What every generative AI build includes
Generated content needs guardrails. We agree style, sources and review before the first output reaches a customer.
Talk through the use caseYour templates and tone encoded as constraints
Human approval where output carries brand or legal risk
Versioned prompts so output does not drift silently
Our process
Four steps from brief to reviewed output
Quality is defined before generation starts.
- 01
Define the output
What is generated (text, images, summaries), for whom, and what good looks like.
- 02
Choose & compare models
Candidates tried on your own examples for quality, speed and cost.
- 03
Build the pipeline
Prompts, templates, brand rules and review steps around the model.
- 04
Review, release & monitor
People review where it matters, then quality and spend are monitored.
Tech stack
What we build it with
Four layers, each chosen per project. Open one to see what sits in it and why.
Models & frameworks
The layer that generates. Chosen per task against quality, latency and cost.
Retrieval & data
Where grounded answers come from: your documents, indexed and tuned.
Serving & ops
How it runs in production, and how you see what it is doing.
Evaluation
How we know the output is good enough before a user sees it.
More AI capabilities
Fourteen more ways we apply AI
AI Software Development
End-to-end AI product builds.
AI Chatbot Development
Smart, conversational AI for business.
AI Agents Development
Linked agents for smarter tasks.
AI Voice Agents
Voice intake and call handling.
LLM Chatbot Development
Grounded in your own content.
LLMs for SMEs & Enterprises
AI built to scale with you.
RAG Model Development
Smarter AI with search + generation.
GPT Integration
AI seamlessly in your workflow.
Prompt Engineering
Better prompts, better results.
Machine Learning Development
AI that learns from your data.
NLP Development
Classification, extraction, sentiment.
AI Workflow Automation
Process automation that holds up.
AI MVPs & POCs
Proof before you commit to a build.
AI Consulting
Where AI pays off, and where it doesn't.
Frequently asked questions
The questions we get asked about AI
Data handling, output quality, cost and ownership.
Start with one kind of content
Two weeks, your brief, working output and a clear view of quality and cost.
