Service
AI & Machine Learning Development
AI & machine learning development — custom LLM apps, RAG pipelines, computer vision & predictive models built for production. Get a free AI consultation today.
Why work with us on ai & machine learning development
Our AI specialists develop cutting-edge artificial intelligence solutions that transform businesses. From machine learning models and natural language processing to computer vision and predictive analytics, we create AI-powered applications that drive innovation and competitive advantage.
Custom machine learning model development
Natural Language Processing (NLP) solutions
Computer vision and image recognition
Predictive analytics and forecasting
Chatbots and conversational AI
AI integration with existing systems
Data preprocessing and feature engineering
Model deployment and monitoring
Get started
Technical proposal call
Scope and timeline agreed
Development and delivery
Stack
Technologies we use
Python
expert
TensorFlow
expert
PyTorch
expert
Scikit-learn
expert
OpenAI API
expert
Hugging Face
expert
Pandas
expert
NumPy
expert
OpenCV
advanced
NLTK
advanced
spaCy
advanced
Jupyter
expert
Docker
advanced
Kubernetes
advanced
AWS SageMaker
advanced
Google Cloud AI
advanced
PEFT
advanced
LoRA
advanced
QLoRA
advanced
DeepSpeed
advanced
ONNX
advanced
MLflow
advanced
Transformers
advanced
Keras
advanced
ComfyUI
intermediate
Gradio
intermediate
Streamlit
intermediate
n8n
intermediate
Choosing
Which kind of AI does your product actually need?
Most requests arrive as “add AI”. The useful first step is naming which of these the problem is, because they have different costs, different risks and different engineering.
Answering from your data
Search, support, knowledge, documents.
Retrieval-augmented generation over your own content, so answers are grounded and current. The engineering is in retrieval quality, chunking and re-ranking. We run this over 100,000+ documents in production and cut retrieval latency by around 40% on an existing pipeline.
pgvector · Supabase Vector · hybrid search
Voice and real-time
Receptionists, assistants, live interaction.
Streaming speech in, a model in the loop, speech out, with interruption handling and state kept across turns. Delivered under three seconds end to end; the work is in latency budgets and turn-taking.
WebSockets · STT · TTS · streaming
Models you control
Private hosting, fine-tuning, cost.
Open-weight models on your infrastructure via Ollama or vLLM, quantised to fit your GPUs, fine-tuned where prompting has plateaued. The right answer when data cannot leave your network or API spend at volume exceeds hosting.
Llama · Qwen · Mistral · fine-tuning · quantisation
Engineering
What we build
Retrieval pipelines
Ingestion, chunking, embedding into pgvector or Supabase Vector, hybrid search with re-ranking, and automatic re-indexing so answers stay current. Grounded citations back to the source.
Cost and latency engineering
Instrumentation first, then prompt trimming, caching, model routing, batching and self-hosting where it pays. Up to 50% token reduction on production systems without degrading quality.
Voice pipelines
Streaming STT, model in the loop, TTS out, over WebSockets with interruption handling and per-session state. Built for AI receptionists and assistants that people actually talk to.
Vision and document understanding
Image classification and detection, form and document extraction, and multimodal models that read screenshots and files. The Pegasus platform accepts image and file uploads and reasons over them.
Fine-tuning and private deployment
Open-weight model deployment with Ollama and vLLM, LoRA fine-tuning on your examples, quantisation for your hardware, and the serving layer to run it reliably in your VPC.
Evaluation and observability
Evaluation sets from real cases scored on every change, retrieval measured separately from generation, and tracing on every production request, so regressions are caught before customers find them.
Applications
AI systems we have shipped
Enterprise knowledge base
High-accuracy retrieval across 100,000+ company documents, replacing a slow, inaccurate vector search with a production pipeline that cut latency by nearly half.
Fully local AI platform
Pegasus: chat, reasoning, image and file understanding, real-time voice and browser-driving agents, running entirely on local models through Ollama with a node-based workflow to swap models.
Content platform chatbot
A Django and LangChain RAG chatbot over 100,000+ posts in Supabase Vector, answering strictly from stored content, with n8n indexing new posts automatically.
Audio annotation acceleration
OpenAI transcription and PyAudio capture helping annotators produce structured, validated labels, plus a browser extension that fills annotation fields on the client’s labelling platform.
Conversational voice
Low-latency audio streaming pipelines with interruption handling and state preservation, delivering responses in under three seconds.
Cost re-engineering
Re-engineered internal LLM calls on a production system to cut token usage by up to 50% with no measurable loss in response quality.
Working together
Three ways to start
Most AI engagements begin with one of these. We will say which fits, including when the answer is a small trained model or better retrieval rather than a bigger LLM.
Audit an existing AI feature
Instrument what you have, find why it is slow, expensive or unreliable, and return a prioritised fix plan without a rewrite.
Book an audit call →Build a new capability
Retrieval, voice, vision or a private model deployment, scoped with an evaluation set from day one, on a fixed quote.
Get a scoped quote →Need autonomous agents?
If the feature has to plan, use tools and act on its own, that is our agentic AI service, built on the same foundations.
See agentic AI development →FAQ
AI and machine learning questions we get asked
What is the difference between this and your agentic AI service?
Agentic AI is about systems that decide what to do next and act — planning, tool use, multi-step autonomy. This service is everything underneath and around that: retrieval over your data, voice and vision, fine-tuned or self-hosted models, and the cost and latency engineering that makes AI features viable. Many products need this without needing an agent at all.
Our AI feature works in the demo but is unreliable with real users. Why?
Usually one of four things: retrieval returning the wrong context, prompts that drift under real inputs, no evaluation harness so regressions go unnoticed, or latency and cost that were never measured at volume. We start by instrumenting what you have, then fix in that order. Most fragile AI features can be made dependable without rebuilding them.
How do you reduce LLM API costs?
By measuring first: which calls dominate spend, how much of each prompt is redundant, and where a smaller model would do. Then prompt and context trimming, caching, routing easy cases to cheaper models, batching, and moving suitable workloads to self-hosted open-weight models. We have cut token usage by up to 50% on production systems without degrading output quality.
What is RAG and when do we need it?
Retrieval-augmented generation: the model answers from documents retrieved from your own data rather than from memory, so responses are grounded and current. You need it the moment an AI feature must be right about your content — policies, products, records, a knowledge base. We run RAG over 100,000+ documents in production, and the engineering is in retrieval quality, not the model.
Can we run models on our own infrastructure?
Yes. We deploy open-weight models such as Llama, Qwen and Mistral using Ollama or vLLM on your hardware or in your VPC, with quantisation to fit your GPUs. The Pegasus platform runs entirely on local models. This is the right answer when data cannot leave your network or when API costs at your volume exceed the cost of hosting.
When does fine-tuning make sense over prompting?
When you have a well-defined task, thousands of good examples, and prompting has plateaued — classification, extraction into a fixed schema, a consistent house style. It is not the fix for missing knowledge; that is retrieval. We fine-tune and quantise open-weight models and will tell you when a better prompt or better retrieval would get you there cheaper.
Can you build voice interfaces?
Yes. Streaming speech-to-text, a model in the loop, and text-to-speech back, over WebSockets with interruption handling and state preservation. We have delivered conversational voice pipelines under three seconds end to end and are engineering toward sub-second real-time conversation. The hard parts are latency budgets and turn-taking, not the models.
Do you do computer vision and classical machine learning?
Yes. Image classification and detection, document and form understanding, and predictive models on tabular data with TensorFlow, PyTorch and scikit-learn. Not every problem needs a language model, and we will say so when a small trained classifier will outperform an LLM at a fraction of the cost.
How do you make sure the AI is accurate before launch?
An evaluation set built from real cases, scored automatically on every change, plus human review on a sample. Retrieval is measured separately from generation so we know which is failing. Tracing on every request in production. Without this you are guessing, and the first sign of regression is a customer complaint.
Which model providers do you work with?
OpenAI, Anthropic Claude, Google Gemini and Vertex AI, AWS Bedrock, and self-hosted open-weight models through Ollama and vLLM. We build model-agnostic so you can switch as pricing and quality change, and we often route different steps of one feature to different models.
How we work
Our development process
Problem Definition
Understanding business challenges and AI solution requirements
1-2 weeks
Data Analysis
Data collection, cleaning, and exploratory data analysis
2-3 weeks
Model Development
Algorithm selection, training, and model optimization
4-8 weeks
Deployment & Monitoring
Production deployment, performance monitoring, and maintenance
1-2 weeks
Why us
Benefits of working with us
PhD-level AI researchers and experienced ML engineers
End-to-end AI solution development
Scalable and production-ready AI systems
Continuous model improvement and optimization
Ethical AI practices and bias mitigation
Comprehensive documentation and knowledge transfer
Ready to hire our ai & machine learning development experts?
A senior engineer replies within one business day with a real technical read on your project.
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