Explore our portfolio of enterprise AI projects. Each case study shows the problem we solved, the approach we took, and the measurable outcomes achieved.
A 200-person London law firm was spending 40+ hours per week manually reviewing and extracting data from contracts, invoices, and court documents. The process was slow, error-prone, and a major bottleneck for deal closures.
We designed and deployed a multi-agent document processing system using ADLC methodology - covering extraction, classification, validation, and approval routing - with full audit logging and human-in-the-loop escalation for edge cases.
Legal AI Automation
A global bank with growing AI deployment needed an enterprise governance framework ahead of the EU AI Act enforcement deadline. Existing models had no documentation, no risk classification, and no monitoring infrastructure.
We designed a complete AI governance framework covering risk-based model classification, model cards for all in-production AI systems, bias monitoring pipelines, incident response procedures, and board-level reporting templates.
Financial AI Governance
A 50-engineer SaaS company wanted to leverage AI to accelerate their development velocity without degrading code quality. They had tried GitHub Copilot but lacked a systematic approach to measuring impact or maintaining quality.
We implemented a complete AI-augmented SDLC - intelligent PR review, automated test generation, AI documentation pipeline, and deployment risk scoring - with engineering metrics dashboards to track the impact on velocity and quality.
SDLC AI Transformation
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Real-world AI deployments are delivering measurable results. These benchmarks from peer-reviewed studies and enterprise reports set the performance baseline.
| Sector / Function | AI Application | Measured Outcome | Evidence Source |
|---|---|---|---|
| Software Engineering | AI coding assistants (Copilot, Cursor) | 26% productivity gain | Stanford HAI 2026 |
| Customer Support | AI resolution & routing agents | 14-15% improvement | Stanford HAI 2026 |
| Marketing | Generative content & personalisation | 73% output increase | Stanford HAI 2026 |
| Legal / Document Review | AI contract analysis & due diligence | ~50% time saving | McKinsey 2025 |
| Finance / Reporting | Automated report generation | 30-40% faster | Deloitte AI Survey 2025 |
| Recruitment | AI CV screening & shortlisting | ~60% screening reduction | WEF FoJ 2025 |
| Manufacturing | Predictive maintenance & QC | Cost benefit reported | McKinsey Nov 2025 |