AI Development for Legal Innovation

Transform your legal practice with cutting-edge AI solutions. We build intelligent systems that automate workflows, enhance decision-making, and deliver measurable ROI.

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85%
Reduction in Document Review Time
3x
Faster Contract Analysis
£2M+
Saved in Operational Costs
99.9%
Accuracy in Compliance Checks

AiDi Methodologies and Principles

Our proven framework for delivering exceptional AI solutions in the legal sector

Our Core Principles

1. Client-First Innovation

We believe that technology should serve business objectives, not the other way around. Every solution we develop starts with understanding our client's unique challenges and goals.

2. Quality Without Compromise

  • Rigorous code review processes
  • Automated testing at every stage
  • Continuous integration and deployment
  • Performance optimisation as standard practice

3. Security by Design

  • Data privacy and protection built into every solution
  • Regular security audits and compliance checks
  • Transparent security practices with client visibility
  • GDPR and industry-specific compliance adherence

4. Collaborative Partnership

  • Regular governance meetings and transparent communication
  • Agile methodologies with client involvement
  • Flexible engagement models to suit client needs
  • Knowledge transfer and team empowerment

Our Development Methodologies

Agile Development Framework

Sprint Planning and Execution

  • 2-week sprint cycles for optimal velocity and feedback
  • Daily standups for team synchronisation
  • Sprint reviews with stakeholder demonstrations
  • Retrospectives for continuous improvement

Definition of Ready (DoR)

  • Clear acceptance criteria
  • Technical specifications defined
  • Dependencies identified
  • Effort estimated

Definition of Done (DoD)

  • Code reviewed and approved
  • Unit tests passed
  • Integration tests completed
  • Documentation updated
  • Deployed to staging environment

AI Development Methodology

1. Discovery Phase

  • Problem definition and feasibility analysis
  • Data availability and quality assessment
  • Model selection and architecture design
  • Success metrics definition

2. Development Phase

  • Data preparation and feature engineering
  • Model training and validation
  • Hyperparameter optimisation
  • Performance benchmarking

3. Deployment Phase

  • Model containerisation and API development
  • Integration with existing systems
  • Monitoring and alerting setup
  • A/B testing implementation

4. Maintenance Phase

  • Model performance monitoring
  • Drift detection and retraining
  • Continuous improvement cycles
  • Stakeholder feedback integration

Quality Assurance Framework

Code Quality Standards

  • Static Code Analysis: Automated scanning with Sonar, GitLeak
  • Peer Review Process: Mandatory code reviews before merge
  • Test Coverage: Minimum 80% code coverage requirement
  • Documentation: Inline documentation and API documentation

Testing Methodology

  1. Unit Testing: Component-level testing
  2. Integration Testing: System interaction validation
  3. Performance Testing: Load and stress testing
  4. Security Testing: Vulnerability assessment
  5. User Acceptance Testing: Client validation

Continuous Improvement

  • Regular team training and upskilling
  • Technology radar for emerging trends
  • Hackathons and innovation days
  • Knowledge sharing sessions

Communication and Governance

Communication Cadence

  • Daily: Development team standups
  • Weekly: Client sync meetings (RAG meetings)
  • Bi-weekly: Sprint reviews and planning
  • Monthly/Quarterly: Strategic account reviews

Reporting and Metrics

  • Sprint Velocity Reports: Track development speed
  • Burndown Charts: Monitor progress within sprints
  • Quality Metrics: Code quality, test coverage, defect rates
  • Business KPIs: Aligned with client objectives

Escalation Matrix

  1. Level 1: Team Lead / Scrum Master
  2. Level 2: Engagement Manager
  3. Level 3: Account Manager
  4. Level 4: Senior Leadership

Innovation Framework

Research and Development

  • Dedicated R&D time for team members
  • Proof of Concept development
  • Technology evaluation and adoption
  • Patent and IP development support

Knowledge Management

  • Internal wiki and documentation
  • Best practices repository
  • Lessons learned database
  • Technical blog and publications

Community Engagement

  • Open source contributions
  • Technical conference participation
  • Industry partnership programmes
  • Academic collaborations

Sustainability and Ethics

Ethical AI Development

  • Bias detection and mitigation
  • Explainable AI practices
  • Privacy-preserving techniques
  • Responsible AI governance

Environmental Responsibility

  • Cloud resource optimisation
  • Green coding practices
  • Remote-first approach
  • Paperless operations

Social Impact

  • Diversity and inclusion initiatives
  • Community outreach programmes
  • Pro-bono technical consulting
  • STEM education support

These methodologies and principles guide every project at AiDi, ensuring consistent delivery of high-quality, innovative solutions that drive real business value.

Our Agile Process

From discovery to deployment in weeks, not months

1. Discovery Sprint

We dive deep into your workflows, identify pain points, and map out AI opportunities that deliver immediate value.

1

2. Rapid Prototyping

See your AI solution come to life with working prototypes in just 2 weeks. Test, iterate, and validate before full development.

2

3. Secure Development

Built with bank-grade security, GDPR compliance, and your data sovereignty in mind. Every line of code is reviewed and tested.

3

4. Seamless Integration

Your new AI seamlessly integrates with existing systems. No disruption, just enhancement of your current workflows.

4

Engagement Models

Flexible approaches tailored to your needs and objectives

👥

Dedicated Team Model

Full-time resources dedicated to your project with direct team management and oversight. Ideal for long-term engagements with flexible scaling.

  • Full-time resources dedicated to your project
  • Direct team management and oversight
  • Ideal for long-term engagements
  • Flexible scaling up or down
📋

Project-Based Model

Fixed scope and timeline with milestone-based delivery. Clear deliverables and outcomes with shared risk between parties.

  • Fixed scope and timeline
  • Milestone-based delivery
  • Clear deliverables and outcomes
  • Risk shared between parties
🔄

Hybrid Model

Combination of dedicated and project resources for maximum flexibility. Optimal resource utilisation with balanced risk distribution.

  • Combination of dedicated and project resources
  • Flexibility for changing requirements
  • Optimal resource utilisation
  • Balanced risk distribution

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