Data to Data + Platform to Platform
Your platform already holds the orders, records or transactions that run the business. We connect that data to forecasting, classification and automation, then integrate the result into the workflow people use.
Delivered implementations
These examples show the complete implementation: data, models, interfaces and the people using them. Each case study records its own context and results.
2021–22 · Manufacturing
A perishable-goods manufacturer needed to price more than 100 SKUs as input costs changed. Recipe costs, sales and wastage fed forecasting and optimisation, with commercial decisions grounded in the resulting data.
Read the case study →2018 · Wildlife monitoring
Video analysis identified individual tigers from stripe patterns. Sightings appeared on a map, and operator corrections fed a retraining loop. The delivered system combined the model, the interface and the review workflow.
Read the case study →2018–19 · Civic analytics
News, complaints, feedback and phone surveys entered a shared sentiment pipeline. Ward-level views and alerts helped users investigate changes across the different channels.
Read the case study →Implementation
01
Identify what a person currently reads, decides or retypes. Agree the result that would make the change useful, and the cases that still need human judgement.
02
Trace the inputs to their sources. Resolve identifiers, missing records and timing before training or integrating a model. Keep enough history to reproduce an output.
03
Test against representative examples and the existing workflow. Connect the output through a versioned service, with clear error handling and an approval path where needed.
04
Watch input quality as well as model output. Record versions, collect corrections and review changes before promotion. Agree who responds when the model or an integration fails.
Our implemented AI proposal workflow separates generation, human review and publication. It retains the source and model response, checks the draft state before approval and handles interrupted message delivery. This is implementation evidence; customer rollout and business outcomes are separate milestones.
Read the engineering account →A useful prediction still needs to reach someone who can act on it. We build the integrations, operational views, event processing and exception handling around the model. A deterministic rule is often the right implementation for a repeatable step.
Our production examples include conventional machine learning, computer vision and NLP. For a generative AI requirement, the first conversation establishes the data, task and evaluation criteria before agreeing a solution. The historical results above belong to those specific implementations.
Tell us what your system does today, what people do manually and what you want to change. We can help identify the first useful implementation.
