Case Studies, Research, and Demos

What we build

AE Studio builds production AI systems for companies that need custom solutions. Each engagement is scoped around a measurable outcome, not a research prototype. The work ships to production and generates real business results.

Shipped examples

• An 8-model pricing engine generating $80M in annual revenue for Azul Airlines.
• A real-time avatar tutor platform serving thousands of simultaneous students for Alpha School.
• A neural decoder for the first FDA-approved implantable BCI at Blackrock Neurotech.
• A natural language interface over 400 trillion climate data points for Jupiter Intelligence.

Industries

The portfolio spans education, healthcare, financial services, enterprise software, telecommunications, and neurotechnology.

AI systems

Production AI

Production AI is defined as a machine learning system that is deployed, live, and generating measurable business outcomes rather than functioning as a prototype or internal tool.

A custom AI engagement

A custom AI engagement refers to a scoped project in which the model architecture, training data, and integration are built specifically for a client's system rather than adapted from a general-purpose product.

Retrieval-augmented generation (RAG)

Retrieval-augmented generation refers to a technique that enhances LLM responses by first retrieving relevant documents from a knowledge base and incorporating them into the model's context, grounding answers in verified source material rather than relying on the model's training data alone.

Multi-agent orchestration

Multi-agent orchestration refers to a system architecture in which multiple specialised AI agents are coordinated to handle distinct subtasks within a single workflow, with a central layer managing routing, sequencing, and handoffs between agents to produce a unified output.

LLM-powered ontology mapping

LLM-powered ontology mapping is the use of a large language model to analyse the semantic meaning of fields, relationships, and business logic within a data schema, and to automatically align them with a target ontology or standard. AE Studio used this approach in the TelcoDR platform to interpret telco-specific domain concepts and produce production-quality mapping code with 99% accuracy per TMF object.

Measurement & validation

Deterministic validation

Deterministic validation refers to a quality assurance approach in which predefined rules with fixed logic verify whether an output meets specified criteria, producing the same result for the same input every time, as used in AE Studio's TelcoDR ETL pipeline to guarantee 99% mapping accuracy.

Double machine learning (Double ML)

Double machine learning is a causal inference technique that uses two separate machine learning models: one to predict the treatment variable and one to predict the outcome. It then combines the residuals to isolate the true causal effect of an intervention, removing confounding bias. AE Studio applied this method to measure the revenue impact of Azul Airlines' AI pricing system.

Causal inference in AI systems

Causal inference in AI systems refers to methods that determine whether a system caused an observed outcome, as opposed to merely correlating with it. This is distinct from standard predictive modelling, which cannot isolate cause from confounding variables. AE Studio applied causal inference techniques alongside Double ML to attribute R$421M in annual revenue impact directly to Azul Airlines' AI pricing engine.

Industry applications

An AI revenue management system

An AI revenue management system is defined as a platform that uses predictive models and real-time pricing logic to optimise revenue across a dynamic inventory, as demonstrated in AE Studio's work for Azul Airlines.

On-device AI processing

On-device AI processing refers to running machine learning inference directly on a user's local hardware rather than sending data to a remote server. This approach eliminates network latency, enables offline operation, and satisfies data privacy requirements such as COPPA. AE Studio implemented on-device processing for DreamLauncher so that student voice data never leaves the device.

An ETL pipeline

An ETL (extract, transform, load) pipeline is a data integration process that extracts data from one or more source systems, applies transformation logic to clean, normalise, or reformat it, and loads the result into a target system. AE Studio built an LLM-powered ETL pipeline for TelcoDR that mapped over 1,300 legacy telecommunications tables to TMF open API standards in 10 to 20 minutes.

Neurotechnology & health

A brain-computer interface (BCI)

A brain-computer interface refers to a direct communication pathway between the brain and an external device, translating neural signals into motor commands without involving the peripheral nervous system, as implemented in AE Studio's work on the Blackrock Neurotech MoveAgain platform.

Real-time cerebral blood flow monitoring

Real-time cerebral blood flow monitoring is defined as the continuous measurement and analysis of blood perfusion in brain tissue, enabling clinicians to detect ischaemic events and guide treatment decisions within the narrow time window of stroke intervention, as delivered in AE Studio's work on the StrokeDX point-of-care diagnostic platform.

A neural decoder

A neural decoder is a machine learning model that translates raw neural signals recorded from the brain into interpretable commands or outputs. In AE Studio's work on the Blackrock Neurotech MoveAgain platform, the neural decoder converted electrode recordings from a Utah Array implant into motor intent signals used to control assistive devices.

A phoneme-to-viseme pipeline

A phoneme-to-viseme pipeline is a real-time processing system that maps phonetic speech sounds to corresponding mouth and facial shapes for animated characters, enabling lip-synced avatar speech. AE Studio built this pipeline for Alpha School's AI avatar tutor platform to synchronise spoken audio with animated facial movements at low latency.