Do we need our own data to build an AI feature?

Not always. GenAI features built on foundation models work without proprietary data. You need your own data for RAG, where the model answers from your documents, and for custom models trained on your specific domain.

What is the difference between RAG and fine-tuning?

RAG retrieves your documents at query time and feeds them to the model as context, so answers update the moment your content does. Fine-tuning changes the model's behaviour and tone through training. Most business use cases need RAG, not fine-tuning.

How do you handle data privacy when using LLMs?

We use enterprise API tiers where your data is not used for training, deploy inside your own cloud account where the compliance requirement demands it, and can run open-weight models entirely on your infrastructure so nothing leaves your network.

How do you stop the AI from producing wrong answers?

We ground responses in your own sources through RAG, add citation links so users can verify claims, build confidence thresholds that hand off to a human when the model is unsure, and run an evaluation suite against real queries before launch.

AI & ML Development — GenAI, LLMs & Automation

Lab · AI/ML Services

AI that works.

We build, deploy and scale AI solutions that actually solve business problems.

Predictive

NLP

Computer Vision

ML Ops

See our work

Lets Generate

Engineer

Products Shipped

M+

End User Reached

Client Retention

AI without the hype.

We focus on practical, production‑ready AI that delivers measurable outcomes.

AI Consulting

Know where AI actually fits.

GenAI & LLM

Chat, copilots, generative features.

Data AI

AI that knows your data.

Automation

Let AI do the repetitive work.

AI Consulting

Know where AI actually fits.

Capabilities

1AI readiness assessment
2Use-case identification
3Feasibility & roadmap
4Where AI helps, and where it doesn't

AI readiness assessment

Use-case identification

Feasibility & roadmap

Where AI helps, and where it doesn't

Deliverables

AI opportunity map
Feasibility report
Roadmap

GenAI & LLM Features

Chat, copilots, generative features.

Capabilities

1GenAI feature development
2LLM integration (best-fit models)
3Chat, copilot & assistant features
4AI interface design (AI UX)

GenAI feature development

LLM integration (best-fit models)

Chat, copilot & assistant features

AI interface design (AI UX)

Deliverables

Working AI feature
LLM integration
AI UX patterns

Data AI (RAG & Custom Models)

AI that knows your data.

Capabilities

1RAG pipelines — answers from your own data
2Custom ML model development
3Models trained on your data
4Data prep & deployment

RAG pipelines — answers from your own data

Custom ML model development

Models trained on your data

Data prep & deployment

Deliverables

RAG pipeline
Custom model
Model deployment

AI Automation

Let AI do the repetitive work.

Capabilities

1Intelligent workflow automation
2Replace manual, repetitive tasks
3Automation beyond simple rules
4Human-in-the-loop where it matters

Intelligent workflow automation

Replace manual, repetitive tasks

Automation beyond simple rules

Human-in-the-loop where it matters

Deliverables

Automated workflows
Integration
Monitoring

Quick Reference Guide

Services by Client Type

An overview map of client needs matched directly to our specialisations.

Where you're at	Start with
Want AI but don't know where to start	AI Consulting
Want a ChatGPT-style feature	GenAI & LLM Features
Have lots of data, want answers from it	Data AI
Need predictions specific to your business	Data AI (custom models)
Drowning in a repetitive manual process	AI Automation

Our Services

Why ai/ml matters

Turn your data into a competitive advantage.

Real value, not hype

AI that does a job, not AI for the headline.

Built on your data

Answers and predictions from what you actually have.

Automate the boring

Free your team from repetitive work.

Know where it fits

We'll tell you where AI helps, and where it won't.

Case Study

View Case Study

View All

Faq

Let's clear things up.

Everything you'd want to know before the first call.

What does AI/ML development include?

Adding real intelligence to your product: GenAI and LLM features like chat and copilots, RAG so AI can answer from your own data, custom ML models for specific predictions, and automation of repetitive work. Plus consulting to figure out where AI actually helps, and where it's just noise.

Which AI models do you use?

We match the model to the job rather than marrying one. Different use cases suit different models, so we pick the best fit for your accuracy, cost, and privacy needs, and swap as the landscape changes. You're never locked to one provider.

Do you build custom models or just plug in existing ones?

Both. A lot of use cases are solved best by integrating a strong existing model. Others need a model trained on your own data for predictions only you need. We'll tell you which one your problem actually calls for.

Can AI use our own data?

Yes. That's what RAG is for. We build a pipeline so the AI answers using your documents, your knowledge base, your data, not just what it learned from the internet. Grounded, accurate, and yours.

How do we know if AI is even worth it for us?

That's the consulting. We look at where AI could add real value in your product or process, check it's feasible, and tell you honestly if it's worth doing. Sometimes the answer is 'not yet,' and we'll say so.

Is our data safe?

Yes. We handle data carefully, with privacy and access controls built in, and we choose models and setups that fit your security needs. For deeper security and compliance, our SecOps team gets involved.

How is this different from just using ChatGPT?

A chat tool is generic. We build AI into your product, grounded in your data and your workflow, with the right interface and guardrails. It's the difference between a clever toy and a feature your users rely on.