Production-grade AI built for your exact domain.
We design and build tailored ML models, LLM-powered pipelines, and full inference layers — not off-the-shelf tools wrapped in a dashboard. Everything is purpose-built for your data, your workflows, and your scale targets.
Everything you need, nothing you don't.
Each engagement is scoped to what you actually need. No upselling, no bloat.
LLM Fine-tuning & RAG
Domain-adapted language models and retrieval-augmented generation pipelines trained on your proprietary data.
Custom ML Models
Supervised, unsupervised, and deep learning models built from scratch or adapted from foundation models.
Computer Vision & NLP
Object detection, OCR, document understanding, and entity extraction tailored to your inputs.
Data Ingestion Pipelines
Reliable ETL and streaming pipelines that feed your models with clean, structured data at scale.
MLOps & Model Monitoring
CI/CD for ML, automated retraining triggers, drift detection, and performance dashboards.
Inference Layer & APIs
Low-latency serving infrastructure with versioned REST/gRPC APIs for seamless product integration.
Simple steps. Clear outcomes.
We keep the process lean so you spend less time in meetings and more time seeing results.
Discovery & Data Audit
We review your data sources, define the ML problem, and map feasibility before writing a single line of code.
Model Build & Iteration
Rapid prototyping, benchmark comparisons, and iterative training until we hit your accuracy and latency targets.
Deploy, Monitor & Improve
We ship to production, set up monitoring, and retrain as your data and requirements evolve.
How it plays out in practice.
A real-world scenario from start to finish — the kind of outcome we build toward.
A shipment-classification model that cut manual sorting by 90%
A freight company was manually categorising thousands of inbound shipment descriptions a day into 40+ handling classes. It was slow, inconsistent between staff, and the backlog delayed dispatch.
We ran a data audit on two years of historical shipments, cleaned and labelled the categories, and fine-tuned a text-classification model with a retrieval layer for rare edge cases. We wrapped it in a versioned REST API and added a confidence threshold so only low-confidence items route to a human.
The model now auto-classifies 90% of shipments at over 97% accuracy in under 100ms each, the remaining 10% are queued for quick human review, and dispatch delays from the sorting backlog disappeared.
Seen it work across industries.
These are the kinds of problems we've solved — and the results they produced.
Product Recommendation Engine
Real-time recommendation model that surfaces the right products at the right moment — increasing average order value.
Clinical NLP Pipeline
Structured data extraction from unstructured clinical notes, cutting manual review time by over 70%.
Fraud Detection at Scale
Custom anomaly detection processing thousands of transactions per second with sub-50ms inference latency.
Questions about custom ai development.
The things prospects ask us most. Still unsure? Book a free call and ask us directly.
What is the difference between custom AI development and using an off-the-shelf tool?
Off-the-shelf tools wrap a generic model in a dashboard. Custom AI development means the model, data pipeline, and inference layer are built around your specific data, workflows, and accuracy targets — so it solves your problem instead of a generic one and stays your IP.
Do you fine-tune models or use retrieval-augmented generation (RAG)?
Both, depending on the problem. RAG is usually the faster, cheaper first choice for grounding an LLM in your documents and data. Fine-tuning makes sense when you need a specific tone, format, or task behaviour that prompting and retrieval cannot reliably produce. We benchmark both before committing.
Who owns the model and the code you build?
You do. All custom models, training code, and pipelines we build are delivered to you and are yours to keep, run, and extend. We do not lock you into a proprietary platform.
How much data do I need to build a custom model?
It depends on the task. RAG and prompt-based systems need little to no labelled data. Classic supervised models may need a few hundred to a few thousand labelled examples. In our discovery and data audit we tell you honestly whether your data is sufficient before any build starts.
How long does a custom AI project take?
Most focused custom AI builds — a RAG system, a classification model, a vision pipeline — go from kickoff to a working production version in 4–8 weeks. We work in iterations so you see a measurable benchmark early, not just at the end.