Introducing AI Operating System for Business & School — our most powerful products yet.Explore products →
App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Turn your data into your deepest competitive moat
We build production-grade AI systems — custom ML models, LLM pipelines, computer vision, and intelligent automation — that create measurable, compounding business value.
40+
AI projects shipped
34%
Avg. accuracy improvement
$2.1M
Avg. cost reduction
11 wks
Avg. time-to-production
Most AI projects fail because they optimise for demo quality rather than production reliability. Our AI practice is built entirely differently: we start with a specific business KPI, build the simplest model that moves that KPI, instrument everything with MLOps tooling, and iterate from real production feedback — not benchmark scores.
We have shipped ML systems that process billions of events per day, reduced operational costs by millions of dollars, and created net-new revenue streams from data clients already owned. Our team includes published ML researchers, MLOps engineers with cloud architecture depth, and product designers who know how to put AI models in front of users in ways that build trust rather than friction.
End-to-end model development from data exploration and feature engineering through training, evaluation, and production deployment. We select architectures based on your data reality, not our preferences.
OpenAI, Anthropic, Mistral, and open-source LLM integration with retrieval-augmented generation, fine-tuning, prompt engineering, and grounding pipelines that reduce hallucination.
Object detection, image classification, OCR, defect detection, and visual search systems built on PyTorch and deployed to cloud or edge environments with sub-100ms inference.
Demand forecasting, churn prediction, pricing optimisation, fraud detection, and risk scoring models connected directly to your operational systems and decision workflows.
Experiment tracking, automated retraining pipelines, model registry, canary deployments, drift monitoring, and observability dashboards — so your models stay accurate long after launch.
Translating AI capabilities into user-facing features that build trust and generate adoption. We bridge the gap between what the model can do and what users will actually engage with.
Before writing a single line of model code, we conduct a structured audit of your data assets, schema history, labelling quality, and business KPI targets. We identify the highest-value use cases, quantify the expected impact, and produce a Data Readiness Report that becomes the north star for the entire engagement.
We establish a simple baseline model on cleaned data, then run structured experiments across feature engineering strategies, model architectures, and training configurations — all tracked in MLflow so every decision is reproducible and auditable.
The winning model enters a production engineering sprint: containerised inference API, automated retraining pipeline, A/B testing framework, latency optimisation, and comprehensive monitoring dashboards. We treat the serving infrastructure with the same engineering rigour as the core product.
We integrate the model into your product surfaces, run a controlled canary launch with human-in-the-loop review, and conduct a thorough handover — including documentation, runbooks, and team training — so your engineers own and evolve the system confidently.
A nationwide veterinary network needed to reduce the 48-hour average wait for diagnostic specialist reviews. Their 3,200+ clinics were generating thousands of clinical photos daily with no scalable review pathway.
We trained a multi-class PyTorch computer vision model on 280K labelled veterinary images, built a mobile upload flow for clinic staff, and integrated the model into their existing electronic medical record system — returning a preliminary AI assessment within 90 seconds of photo upload, automatically prioritising high-confidence findings for specialist review.
Many of our best projects combine two or more services from the list below.
Let's build something great
Book a free 45-minute scoping call. Walk away with a clear picture of what we would build, how long it would take, and how much it would cost — regardless of whether you move forward.
Average response time: 2 business hours. No commitment required.
"AppCorp delivered a production-ready AI platform in 11 weeks that our internal team estimated would take 9 months. The architecture is clean, the docs are thorough, and the product works exactly as specified."
Sarah Chen
CTO, PetScreening
50+
MVPs
94%
Retention
10wk
Avg. launch