AI that earns its place in your product.
We put AI to work where it pays off: inside the product you already have, or in a new one built around it.
4.3 on Trustpilot
AI Enablement & Integration
Sound familiar?
Teams with real work to automate, or an idea that only works with AI.
- Your team spends hours on work a model could draft in seconds.
- You've tried AI features, but they're unreliable or off-brand.
- You don't know what's realistic, or what it costs to run.
What's included
Everything handled, end to end.
- 01AI opportunity assessmentWe find the tasks worth automating, and the ones that aren't.
- 02Answers from your dataAssistants and search that answer from your documents, with sources.
- 03AI features in your productDrafting, summaries, search, tagging and recommendations, built in.
- 04Workflow automationProcess documents, emails and tickets without the manual handling.
- 05Model integrationConnect the right model, or fine-tune one when it earns its keep.
- 06Guardrails & monitoringTesting, fallbacks and usage tracking so quality and costs stay in view.
Know before you start
What makes AI work in production.
Demos are easy; dependable AI features are not. This is how we get from one to the other.
- 01
Start with the task, not the model
Pick one job with a clear outcome, such as drafting a reply or extracting data from invoices. A narrow task is easier to measure, cheaper to run and far more likely to survive contact with real users.
- 02
Ground answers in your own content
Retrieval means the model looks up your documents before it answers, and cites what it used. It keeps answers current and makes confident-sounding nonsense far less likely than a model answering from memory.
- 03
Measure quality before launch
We build a test set of real examples and score accuracy, speed and cost against it. That turns "it feels better" into evidence, and shows when a cheaper model would do the same job.
- 04
Plan for cost, privacy and failure
Usage-based pricing means costs grow with success, so we monitor spend from day one. We agree what data may leave your systems, and design a sensible fallback for when a model is slow or unavailable.
Compare
Prompting, retrieval or fine-tuning?
Three ways to make a model useful for your business. Most products start with retrieval.
Prompting
- Best for
- Simple, general tasks
- Your data
- Not used
- Set-up effort
- Low
- Keeping it current
- Edit the prompt
- Running cost
- Lowest
Usually our pick
Retrieval (RAG)
- Best for
- Answers from your own content
- Your data
- Looked up at answer time
- Set-up effort
- Medium
- Keeping it current
- Update the documents
- Running cost
- Moderate
Fine-tuning
- Best for
- A consistent format or tone
- Your data
- Trained into the model
- Set-up effort
- High
- Keeping it current
- Train again
- Running cost
- Higher to set up
What you receive
Everything you walk away with.
All yours, documented and ready to use, whether you stay with us or not.
- Documented
- Handed over
- Yours to keep
- 01AI opportunity assessment
- 02Data and privacy review
- 03Working prototype
- 04Quality test set and results
- 05Integration into your product
- 06Guardrails and fallbacks
- 07Usage and cost monitoring
- 08Team handover and documentation
Tech stack
Proven tools, chosen for your product.
- Anthropic ClaudeModels
- OpenAIOpenAIModels
- Google GeminiModels
- Hugging FaceModels
- PythonBuild
- LangChainBuild
- PyTorchBuild
- Node.jsBuild
- Next.jsBuild
- PostgreSQLData
- SupabaseData
- RedisData
- AWSAWSCloud
- AzureMicrosoft AzureCloud
- Google CloudCloud
- VercelCloud
Thinking about ai enablement & integration?
Tell us what you need and we'll come back with a plan, a timeline and a fixed price.
What you get
Hours back
Manual work handled in seconds.
Grounded answers
Responses from your data, with sources.
Costs in view
Usage and spend tracked from day one.
- 17+
- Products shipped
- 4.3
- Rating on Trustpilot
- 2–4weeks
- Idea to validated MVP
- 2locations
- Glasgow & Pakistan
What clients say
In their words.
“Great communication and clear project updates from beginning to end. We always knew what stage the app was in.”
“We appreciated their focus on building only the most important features. It saved time and kept the project streamlined.”
“The development team did an excellent job building our first mobile prototype. The UI was simple but functional, which is exactly what we needed.”
How it works
Four clear steps.
- 1
Compass
Assess
Find the tasks worth automating and the data behind them.
- 2
Pillar
Prototype
Prove the approach on real examples, fast.
- 3
Forge · Bridge
Integrate
Build it into your product, with guardrails.
- 4
Sentinel · Everest
Evaluate & launch
Score quality, watch costs, then roll out.
Why Vebryx
Why founders choose us.
Validation first
We test demand first, so every feature is backed by real users.
Fixed scope, fair price
Transparent pricing and regular updates. No surprises.
Built to scale
Proven tech like React, Next.js and AWS, ready to grow.
FAQ
Common questions.
Yes. That's the most common request: we connect a model to your data and build the feature into the product you already run.
Insights
Related reading.
Ready to start step one?
Book a free 30-minute call and we'll map out your journey together.
- 30 minutes, no obligation
- Fixed scope and price
- You own everything we build
- 4.3 on Trustpilot