AI isn't a feature we add. It's how we think.
How We Think About AI Differently.
There are two kinds of agencies doing AI work right now.
The first kind treats AI like a plugin. Client asks for a chatbot, they connect the OpenAI API, wrap a UI around it, and ship it. It works — until it hallucinates, breaks at scale, or gives users answers that make no sense in your specific domain.
We're the second kind. We treat AI as an engineering discipline — not a feature request. That means we start with your data, your domain, and your actual business problem before we pick a model or write a single prompt.
The difference shows up in production. Our AI features don't just demo well — they hold up under real traffic, with real users, doing real things you didn't predict during development.
What We Build With AI.
Not a list of buzzwords. Specific things we've built, can build, and know how to get right.
Generative AI Integration
We plug large language models into your existing product. The result — your software starts understanding language the way a human would. It can search, summarise, write, and respond to your users without anyone on your team being involved.
- A customer support tool that reads your entire knowledge base and answers tickets accurately — without a human touching 80% of them
- A legal platform that summarises 50-page contracts into 5 bullet points — in your client's specific legal language
- An e-commerce search bar that understands "something warm for a weekend trip" — not just "blue jacket"
Custom LLM Work
Off-the-shelf models are smart. But they don't know your business. We fine-tune models on your data, build RAG pipelines so the AI pulls from your actual documents, and write prompt chains that handle edge cases — not just the happy path.
- Fine-tune models on your company's data so responses sound like your brand, not like a generic chatbot
- Build RAG systems that pull from your internal docs, databases, and knowledge bases — so the AI answers with your information, not the internet's
- Design prompt chains that handle follow-ups, clarifications, and weird edge cases without breaking
- Optimise model performance so you're not burning money on API costs that scale out of control
Predictive Analytics & Data Intelligence
Your business already generates data. We build systems that turn that data into something useful — patterns you'd never spot manually, predictions you can actually trust, and alerts when something looks off.
Computer Vision
We build systems that see. Product inspection on manufacturing lines. Document scanning that extracts data from messy handwritten forms. Security systems that detect what matters and ignore what doesn't.
Intelligent Automation
This is the AI work that pays for itself the fastest. Your team is probably spending hours every day on tasks that don't need a human brain — processing documents, sorting emails, pulling numbers into reports. We build AI agents that take over that work entirely.
The AI Stack We Actually Use.
Not logos on a page for credibility. Tools we use in production, every week, on real projects.
Large Language Models
- OpenAI (GPT-4o/Turbo)
- Anthropic (Claude 3.5)
- Google (Gemini Pro/Ultra)
- Meta (LLaMA 3)
- Hugging Face Models
AI Frameworks & Tools
- LangChain
- LlamaIndex
- Vector DBs (Pinecone)
- Transformers
Cloud AI Platforms
- AWS Bedrock / SageMaker
- Google Vertex AI
- Azure OpenAI Service
Why this matters
- Vendor Neutrality
- Cost Optimisation
- Data Privacy
- Provider Failover
We're not locked into one vendor. We pick the right model for your use case, your budget, and your data privacy requirements. Sometimes that's GPT-4. Sometimes that's a self-hosted open-source model that keeps your data entirely on your servers. We know the tradeoffs and we'll be straight with you about them.
Where AI Creates the Most Value.
Not theory. Specific use cases we've either built or are ready to build — based on what we've seen work across industries.
Healthcare & Fitness
- Symptom checkers with smart follow-ups
- 20-page report summarisation for doctors
- Patient no-show prediction
- Adaptive weekly fitness recommendations
E-Commerce & Retail
- Semantic search understand intent
- Dynamic pricing based on demand/stock
- AI-driven product descriptions
- Churn prediction & retention triggers
Logistics & Supply Chain
- Predictive warehouse demand
- Real-time dynamic route recalculation
- Automated shipment tracking flags
- Predictive vehicle maintenance
Food & Restaurant
- Context-aware ordering chatbots
- Menu profitability data intelligence
- Customer preference memory
- Automated review response drafting
Real Estate
- Data-driven market valuations
- Automated lead scoring & triaging
- AI property listing generation
- Rental application processing
EdTech
- Personalized learning paths
- AI tutors with multiple explanations
- Automated assignment grading/feedback
- AI course content generation
How We Take AI From Idea to Production.
AI projects fail for different reasons than regular software projects. Our process is built around the specific risks that kill AI initiatives.
Problem First, Not Model First
We start with your business problem — not with "let's use GPT." Most failed AI projects start with a technology and go looking for a problem. We do it the other way around. If AI isn't the right solution, we'll tell you that in week one — not month three.
Data Assessment
AI is only as good as the data you feed it. We audit what data you have, what's missing, what's messy, and what needs to be cleaned before any model training begins. This step alone saves months of wasted effort.
Rapid Prototyping
We build a working proof of concept in 2–3 weeks — not a slide deck, not a mockup, but something you can actually test with real data. If it works, we scale it. If it doesn't, we've lost weeks, not months.
Production Engineering
A prototype that works on your laptop is not a product. We take what's proven and build it properly — the kind of properly where it handles 10,000 users at once, recovers when something fails, and doesn't run up a cloud bill that makes you question the whole project.
Monitor and Improve
AI models drift. What works today might give worse results in six months as your data changes. We set up monitoring so you know the moment performance starts slipping — and we stick around to fix it, not just wave goodbye after launch.
Why Clients Pick Us for AI Projects.
We don't fake it.
Half the agencies claiming "AI expertise" in 2024 are just wrapping the OpenAI API and calling it a product. We build RAG pipelines. We fine-tune models. We handle multi-provider failover. If you ask us how we'd architect your AI system, we'll draw it on a whiteboard — not hand you a sales deck.
We track costs from day one.
AI can get expensive fast if nobody's watching. We instrument every API call with token tracking and cost limits. You'll never wake up to a surprise bill because a developer left a loop running overnight.
We've shipped AI in production.
The izHR platform isn't a demo. It's live. Employees use it every day. The AI layer handles real work — generating training videos, answering knowledge base queries, extracting skills from documents. We're not learning AI on your project.
We don't lock you into one vendor.
OpenAI goes down sometimes. So does Anthropic. We architect with failover so your product keeps working when one provider has a bad day. You're not stuck betting everything on one company's uptime.