BLOCKCHAIN & AI

AI Development

Develop AI applications around measurable tasks, usable data and human oversight, with evaluation built into delivery rather than added after deployment.

Overview

AI is most useful when the task, acceptable error rate and decision owner are clear. We help organisations identify applications where prediction, classification or language processing can reduce manual effort or improve access to information. Discovery includes checking whether simpler rules or conventional software would solve the problem more reliably.

Our work spans model selection, data preparation, application integration and operational monitoring. Depending on the task, that may involve existing model APIs, retrieval systems or custom machine-learning components. We document data use, performance limitations and escalation routes so the application supports accountable decisions rather than concealing uncertainty behind confident outputs.

What We Build

Document processing applications

Build extraction and classification workflows for forms, reports or correspondence, with validation rules and review queues for uncertain or inconsistent results.

Prediction and decision-support tools

Develop forecasting or scoring applications using suitable historical data, showing useful performance measures and limitations in the context of the decisions being supported.

AI-assisted business workflows

Connect models to internal tools for drafting, categorisation or information retrieval, with approval steps and scoped permissions for actions affecting business records.

Key Features

Task-specific evaluation

Create representative test sets and acceptance criteria that reflect real user tasks, including difficult cases and the consequences of incorrect outputs.

Data-conscious architecture

Define access boundaries, retention rules and provider settings before processing information, with particular attention to confidential records and personal data.

Observable model behaviour

Track response quality, latency, usage costs and model changes so operators can identify regressions and decide when human review or rollback is needed.

Our Process

1. Discovery

   Define the task, available data, decision responsibilities and practical success measures, including situations where automation would be inappropriate.

2. Design

   Select an architecture and evaluation approach, mapping data flows, review points and how uncertainty will appear in the interface.

3. Development

   Build preparation pipelines, model integrations and user workflows with versioned configurations and controlled access to connected systems.

4. Testing & Security Audit

   Assess accuracy, bias, misuse risks and data exposure against representative scenarios, then document unresolved limitations before release.

5. Launch & Support

   Introduce the application to a defined user group, monitor outcomes and maintain evaluation as data, models and business requirements change.

Tech Stack

• Python
• PyTorch
• scikit-learn
• FastAPI
• OpenAI API
• React
• PostgreSQL

Frequently Asked Questions

Do we need to train our own model?

Not necessarily. Existing models or straightforward statistical methods may meet the requirement. Custom training is considered when available data and expected benefits justify the added complexity.

Can AI outputs be treated as facts?

No. Models can make plausible mistakes. Applications should use validation, source references where available and human review proportionate to the consequences of an error.

How much data is required?

It depends on the task and approach. We assess relevance, quality, permissions and coverage before recommending a dataset size or promising that training will be useful.

Can you integrate AI with our existing software?

Yes, subject to available APIs and permissions. We define how information moves between systems and which actions require approval, rather than giving models unrestricted access.