ARTIFICIAL INTELLIGENCE
Intelligence for Complex Systems
Execution-grade AI for voice, forecasting, infrastructure, and governed deployment
Our AI programme is organised around four execution layers: conversational AI (Vivik), predictive intelligence (Predicta), infrastructure fabric (HyperFabric, Nexus-V, Edge-Sync), and governed deployment (Protocol-X, Synth-Data). Each layer connects back to research in meta-learning, federated systems, and neural architecture search. Clients engage us at any layer: strategy-only, flagship deployment, or fully custom.
Our methodology
We anchor every engagement on measurable production outcomes — latency budgets, forecast error bounds, compliance evidence. Research is the upstream; production SLAs are the downstream. Senior architects own continuity from first scoping call through go-live.
Research pillars
How we work in artificial intelligence.
Conversational & execution AI
Vivik delivers sub-500ms conversational latency with CRM-native action execution — handling the volume that would otherwise require manual queues.
Predictive intelligence
Predicta powers operational forecasting for supply chain, treasury, and risk desks. 85% forecast accuracy in representative deployments; custom signal libraries per industry.
AI infrastructure
HyperFabric, Nexus-V, and Edge-Sync address compute bottlenecks, low-latency interconnect, and edge inference at the firmware and kernel level.
Capabilities
What we deliver.
- Conversational AI & NLU (Vivik)
- Predictive forecasting & scenario modelling (Predicta)
- AI infrastructure fabric & interconnect (HyperFabric, Nexus-V)
- Edge inference & model compression (Edge-Sync)
- Synthetic training data generation (Synth-Data)
- Runtime governance & regulatory compliance (Protocol-X)
- MLOps, model lifecycle, and production hardening
Portfolio · Artificial Intelligence
Projects in this sector.
Vivik
Execution-grade conversational AI
Sub-500ms conversational latency with CRM-native action execution. 40% call volume reduction and 95% first-contact resolution in representative deployments. Handles the interactions that used to require manual queues.
View projectPredicta
Predictive intelligence for operational decisions
Operational forecasting for supply chain, treasury, and risk desks. Custom proprietary signal libraries per client industry, scenario modelling for liquidity and credit shocks, real-time alerting into enterprise workflows.
View projectHyQCOpt
Hybrid quantum-classical optimisation engine
Maps enterprise combinatorial objectives to QUBO and Ising formulations. 25–40% faster convergence vs genetic algorithms on representative benchmark sets. Benchmarked rigorously before any production commitment.
View projectQuantumMetaML
Quantum-accelerated meta-learning
Extends neural architecture search into regimes where classical AutoML exhausts search budgets. Integrated with Predicta pipelines. Published convergence comparisons against classical baselines.
View projectHyperFabric
AI infrastructure fabric
Hardware-software co-design for AI inference at scale. Eliminates data-movement bottlenecks from interconnect through kernel. Material reduction in cross-estate AI latency in representative deployments.
View projectNexus-V
Low-latency AI interconnect
Sub-millisecond-class interconnectivity between distributed AI inference nodes. Built from the same research base as HyperFabric. Reduces coordination overhead in multi-model pipeline architectures.
View projectEdge-Sync
Distributed inference for edge hardware
Pushes model execution to cameras, gateways, and plant-floor devices. Up to 70% reduction in cloud inference costs in representative deployments. Federated updates keep edge models current without bulk retraining.
View projectProtocol-X
Runtime governance for compliant AI deployment
Sub-second regulatory guardrails at inference time. EU AI Act policy packs, audit evidence generation, and real-time constraint enforcement. Governance that ships alongside the model rather than bolted on after.
View projectHelixForge
AI-powered drug discovery — target to lead in 2–4 weeks
Replaces costly wet-lab HTS with an in-silico AI pipeline: graph neural networks, molecular docking, MD simulation, and closed-loop active learning. 80–90% in vitro confirmation rate vs 30–40% for standard virtual docking. Four modalities: target discovery, small molecule, gene therapy, and antibody engineering. $100K–$600K per program.
View projectAI-Guided Drug Discovery
Meta-learning applied to target identification
Meta-learning and architecture search applied to drug-target interaction prediction and ADMET profiling. Fewer wet-lab iterations per validated candidate. Active research with select pharmaceutical partners.
View projectComputational Structural Biology
Protein folding and binding affinity prediction
Protein folding ensemble modelling, structure-based virtual screening, and binding affinity prediction using HyperFabric-class infrastructure for throughput. Building toward production-grade computational screening pipelines.
View projectWorking on a problem in artificial intelligence?
We engage selectively. Tell us what you're solving and we'll respond with a clear assessment — not a generic nurture sequence.