Our Work

The Challenge: A mid-sized professional services organization with a national footprint needed to move beyond a mediocre marketing capability. As a conservative business, they lacked a selling culture but needed to leverage their large client base. They required high-level, knowledge-based conversations to engage senior customers: something their junior staff and lead-gen teams lacked the expertise to conduct credibly.

Our Solution: We designed, deployed, and managed two synthetic employees that work within the business. One operates as a specialized SDR, handling inbound leads and conducting low-pressure outbound engagement. It manages a formal BANT scoring approach for every lead.

Crucially, we deployed a second Governance Agent that acts as a supervisor. It reviews every draft generated by the SDR agent in real-time, ensuring all responses meet defined acceptable language rules and stay strictly within approved guardrails before they are ever sent to a customer.

The Challenge: Households with solar power had no efficient way to trade excess energy with neighbors, and existing grids were dumb pipes unable to handle decentralized transactions.

Our Solution: A major prototype initiative that made it into limited production to proof the concept for investors. We built an autonomous trading agent system long before "Agentic AI" was a buzzword. We combined IoT sensors to monitor real-time usage with a blockchain ledger to tokenize energy. The system used early machine learning to predict usage patterns and autonomously buy/sell power at optimal rates without human intervention.

Relevance to AI: Proved our ability to build agents that hold wallets, make financial decisions, and interact with physical hardware safely.

The Challenge: A national tax office faced massive inefficiencies in reconciling business payments, leading to a "tax gap" and administrative burden for suppliers.

Our Solution: Developed as a prototype for government to test and expand on their planning around emerging technologies. We architected a national supplier blockchain where every invoice was hashed and recorded on an immutable ledger. It proved that all payments made in the national economy could be captured, audited, and reconciled with services rendered in near real-time.

Relevance to AI: This is the foundation of our "Compliance Sentinel." We know how to build systems that enforce rules rigidly and securely.

The Challenge: During the pandemic, global data was messy, biased, and inconsistent. Health bodies couldn't accurately forecast hospital bed capacity because the input data was flawed.

Our Solution: In collaboration with GISAID, we built an AI normalization engine. It proved that AI could be used to provide a more scalable approach to data sanitisation and analysis. It ingested disparate global datasets and used machine learning to identify and correct for reporting biases, providing a "normalized" truth for capacity planning.

Relevance to AI: Validates our ability to handle massive, messy, sensitive datasets: the fuel for any enterprise RAG system.