Velorix builds custom AI solutions and intelligent automation — LLM-powered applications, AI agents, workflow automation, computer vision, and predictive analytics — turning your data and processes into durable competitive advantage.
Not demos, not prototypes — production AI systems built for reliability, security, and real business impact.
Custom applications built on GPT-4o, Claude, Gemini, or open-source models. AI chat interfaces, document Q&A systems, knowledge bases, content generators, and intelligent search — grounded in your data with retrieval-augmented generation (RAG).
Multi-step AI agents that plan, reason, use tools, and complete complex tasks autonomously. Customer service agents, research assistants, data pipeline agents, and internal copilots — doing in minutes what takes humans hours.
End-to-end automation of repetitive workflows using n8n, Zapier, Make, or custom code. Document processing, data extraction, approval flows, reporting, and cross-platform integrations — eliminating manual work at scale.
Image and video analysis for real-world automation. Product defect detection, document OCR, face recognition, object counting, medical imaging analysis, and real-time video processing pipelines — built with PyTorch, YOLO, and cloud vision APIs.
Machine learning models that turn your historical data into forward-looking decisions. Demand forecasting, churn prediction, fraud detection, recommendation engines, and pricing optimisation — trained, deployed, and monitored in production.
Connect AI capabilities into your existing products and workflows. API integration of foundation models, fine-tuning on your domain-specific data, embedding pipelines, vector database setup, and prompt engineering for consistent, reliable outputs.
Model-agnostic by design. We choose the best foundation model for your task — not the one we're locked into.
From single automations to full AI product builds — scoped to deliver measurable ROI fast.
Eliminate one high-impact manual process with intelligent automation. Fast to scope, fast to ship.
A full custom AI application or agent — scoped, designed, built, and deployed to production with your data and integrations.
Complex AI platforms, fine-tuned models, computer vision systems, or organisation-wide automation programmes.
All projects include a free discovery call to validate scope and ROI before any commitment. Book yours today.
The frameworks, models, and platforms we use to build reliable AI systems — from prototype to production scale.
A clear, de-risked path from business problem to deployed AI system — no endless discovery phases, no vague timelines.
We map your current processes, identify automation and AI opportunities, and quantify the business case before any build starts. We prioritise high-impact, low-complexity wins first — delivering ROI fast while laying the groundwork for larger programmes. You leave with a written scope and estimated return.
AI is only as good as the data behind it. We audit your data sources, quality, and availability. We design the ingestion pipeline, define embedding strategies for RAG systems, and map all third-party integrations required. Data issues are surfaced and resolved before they block the build.
We build a working prototype in 1–2 weeks — fast enough to validate assumptions before full investment. You interact with a real system, not a slide deck. We measure accuracy, latency, and edge cases. Findings from the prototype directly shape the production architecture decisions.
Validated prototype becomes a production system — with proper error handling, rate limiting, fallback logic, logging, and security. All integrations are wired up and tested. We build for maintainability: clean APIs, well-documented prompts, and modular architecture that your team can extend.
We deploy to cloud infrastructure with observability built in — tracking model performance, latency, cost-per-call, and accuracy drift over time. Post-launch, we run a 30-day optimisation cycle: tuning prompts, adjusting retrieval parameters, and retraining where needed based on real usage patterns.
Real production AI systems we built — and the outcomes they delivered.
LexCore's lawyers spent 6–8 hours per case manually searching case law across 40+ legal databases. They needed an AI research assistant that could surface relevant precedents instantly and cite sources accurately — without hallucinating.
StockPilot's operations team manually processed 800+ supplier invoices per month — extracting line items, matching SKUs, and flagging discrepancies. The process took 3 full-time staff and still produced errors costing $12K/month in overcharges.
PrecisionMade's quality control relied on manual visual inspection — missing 8% of defects that reached customers. Returns cost $280K/year. They needed automated defect detection running on their existing production line cameras in real time.
GrowthOS was losing 6% of customers monthly without warning. Their CS team only found out at cancellation — too late to intervene. They needed a predictive churn model that would flag at-risk accounts 30 days before they churned.
What clients ask before starting an AI or automation project with us.
Not necessarily. Many powerful AI systems use retrieval-augmented generation (RAG) — connecting a foundation model like GPT-4o to your existing documents, knowledge base, or database — requiring no training data at all. Fine-tuning a model does require labelled examples (typically 100–10,000 depending on the task), but off-the-shelf models with good prompting handle most use cases. In discovery, we assess what approach fits your data situation and avoid over-engineering the solution.
Hallucination prevention is a core part of our architecture — not an afterthought. For factual applications we use RAG (retrieval-augmented generation) so the model always grounds its answers in retrieved source documents, and we instruct it to cite sources or decline to answer when information isn't available. We also add output validation layers, confidence scoring, and human-review queues for edge cases. The goal is a system that says "I don't know" reliably — not one that confidently fabricates answers.
When using cloud APIs (OpenAI, Anthropic), prompts and documents are sent to those providers' servers for inference. Both OpenAI and Anthropic offer enterprise API agreements with zero data retention and no training on your data. For organisations with strict data residency requirements — healthcare, finance, government — we deploy open-source models (Llama 3, Mistral) on your own infrastructure or private cloud, so data never leaves your environment.
Traditional RPA (robotic process automation) follows rigid, pre-defined rules — it breaks when inputs change format or exceptions occur. AI automation can handle unstructured inputs (emails, PDFs, images, natural language), understand context, make judgement calls, and adapt to variation. For structured, stable processes RPA is often sufficient. For anything involving natural language, documents, or decisions — AI-powered automation is dramatically more robust and scalable. We use both, and often combine them.
Automation projects typically show ROI within the first month of deployment — the time saving is immediate and measurable. AI applications (chatbots, document processing, classification systems) usually show clear efficiency gains within 30–60 days. Predictive ML models (churn, demand forecasting) typically need 60–90 days of production data to validate their accuracy and business impact. We define success metrics before build starts — so you're measuring ROI against an agreed baseline, not guessing.
In most cases we integrate AI into your existing systems via API — adding AI capabilities to your current CRM, ERP, website, or internal tools without replacing them. We build clean API layers that your existing software calls, keeping disruption minimal. A new platform is only needed when your current system has no integration capability or when the AI use case is central enough to justify a dedicated product. We'll recommend the least-disruptive path that achieves your goals.
Every month without AI-powered automation is a month of manual work that shouldn't exist. Start with a free discovery call — we'll find your highest-ROI use case in 30 minutes.