Purpose-built AI packages for healthcare and financial sectors with HIPAA, FCA, and SOC 2 compliance built in. From clinical diagnostics to fraud detection.
AI-powered diagnostic or triage system for healthcare. MVP delivered in 4 weeks with full regulatory foundation. Leverages domain-adapted transformer models and retrieval-augmented generation for evidence-based clinical decision support.
Custom fraud detection or risk scoring model powered by graph neural networks and transformer-based sequence analysis. Production-ready in 6–8 weeks with FCA and SOC 2 compliance built-in from day one.
Production AI infrastructure designed for regulated industries. Full audit trails, model governance, continuous compliance monitoring, and zero-trust security architecture — built on Kubernetes with immutable logging for regulatory inspections.
We build AI systems that understand medical language and clinical workflows at a depth that general-purpose models cannot match. Our clinical NLP pipeline leverages domain-adapted large language models — including fine-tuned variants of BiomedBERT, ClinicalBERT, and Med-PaLM architectures — to power diagnostic decision support, automated medical coding, and clinical documentation intelligence. Every model integrates seamlessly with major EHR platforms (Epic, Cerner, MEDITECH) via FHIR and HL7 APIs while maintaining full HIPAA compliance.
Our healthcare AI expertise spans patient triage and acuity scoring, readmission risk stratification, clinical trial matching, radiology report analysis, pathology text mining, and evidence-based treatment recommendation engines. We use retrieval-augmented generation (RAG) to ground every clinical recommendation in peer-reviewed literature, formularies, and treatment guidelines — eliminating hallucination risk in high-stakes medical decisions. Each model undergoes rigorous validation against clinical benchmarks including MedQA, PubMedQA, and institution-specific test sets before deployment.
Automated ICD-10/CPT coding powered by transformer-based sequence classification, clinical note summarisation, and documentation completeness detection. Our models achieve 99.2% coding accuracy and reduce clinician administrative burden by up to 60%, improving billing accuracy and regulatory compliance across NHS trusts and private healthcare networks.
Multi-modal clinical decision support systems that combine structured EHR data, clinical notes, lab results, and imaging metadata for patient acuity assessment. Our readmission risk models use gradient-boosted ensembles and temporal attention networks to identify high-risk patients 48–72 hours before adverse events, enabling proactive intervention and reducing readmissions by up to 23%.
Extract longitudinal insights from patient records using FHIR-native data pipelines, identify patient cohorts for clinical trials, and power population health analytics. Our de-identification protocols meet Safe Harbor and Expert Determination standards, enabling robust AI training while maintaining strict HIPAA compliance and patient privacy.
Computer vision models for radiology report analysis, pathology slide classification, and medical image triage. We combine vision transformers with clinical text data for multi-modal diagnostic support, validated against radiologist consensus and institutional ground truth datasets.
AI models in regulated industries must be auditable, explainable, and demonstrably fair. The EU AI Act, FDA's guidance on AI/ML-based Software as a Medical Device (SaMD), and the FCA's algorithmic trading regulations all demand rigorous model governance. We build automated governance frameworks that continuously test for fairness across demographic groups, generate regulator-ready compliance reports, and maintain immutable audit trails for every model decision and retraining event.
Our compliance automation integrates directly into your CI/CD pipeline — catching regulatory violations, data drift, and fairness degradation before models reach production. We provide multi-dimensional bias detection (age, gender, ethnicity, socioeconomic status), SHAP and LIME-based explainability dashboards, and continuous monitoring systems that satisfy HIPAA, FCA, SOC 2, GDPR, and emerging EU AI Act requirements. Over 50 regulatory audits passed across healthcare and financial services.
Continuous fairness evaluations, multi-dimensional bias detection across protected characteristics, and regulatory requirement verification that runs automatically with every model version. Our testing suites flag violations before deployment, with configurable thresholds aligned to HIPAA, FCA, and EU AI Act risk categories.
SHAP, LIME, attention visualisation, and counterfactual explanations that satisfy regulators and help clinicians and risk officers understand why the model made each decision. We generate human-readable reports tailored to different stakeholders — from technical audit teams to clinical governance boards and C-suite executives.
Complete immutable audit trails covering data provenance, model training parameters, hyperparameter selections, evaluation metrics, version history, and decision logs. Every artefact is cryptographically signed and timestamped for regulatory inspections — built to withstand scrutiny from HIPAA auditors, FCA supervisors, and data protection officers.
Real-time monitoring for data drift, concept drift, and prediction distribution shifts. Automated alerting and retraining triggers ensure models maintain performance and fairness standards over time, with full audit logging of every monitoring event and remediation action.
We build AI systems that detect sophisticated fraud patterns, score credit and counterparty risk, and ensure algorithmic compliance across financial services. Our models leverage graph neural networks (GNNs) to map transaction relationships, transformer-based sequence models to detect temporal anomalies, and federated learning to train across banking consortia without exposing sensitive transaction data. Every model delivers real-time decisions at sub-40ms latency with full explainability for regulators.
Financial institutions face an estimated $2 trillion in annual fraud losses globally, alongside mounting regulatory pressure for algorithmic transparency under MiFID II, PSD2, and the FCA's Consumer Duty framework. Our financial AI integrates seamlessly with core banking systems, payment rails, and trading platforms — providing fraud detection that catches patterns rule-based systems miss, reducing false positive rates by up to 70%, and generating audit-ready SHAP-based explainability reports for every flagged transaction.
Graph neural network and transformer-based anomaly detection models that flag suspicious transactions with sub-40ms latency. Our adaptive thresholding system reduces false positives by up to 70% compared to rule-based systems, while maintaining 98.7% detection rates. Models continuously learn from new fraud patterns through online learning and federated updates across institutional boundaries.
Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) estimation using gradient-boosted ensembles and deep learning models. Our credit scoring systems meet Basel III, IFRS 9, and CRD V regulatory requirements, with built-in SHAP explainability for every lending decision to satisfy FCA fairness requirements.
Multi-layered AML detection combining network analysis, behavioural profiling, and NLP-based sanctions screening. Our systems identify politically exposed persons, detect structuring patterns, and monitor cross-border flows — all aligned with FATF recommendations, the 6th Anti-Money Laundering Directive (6AMLD), and HM Treasury guidance.
Post-trade surveillance, market manipulation detection, and algorithmic trading compliance engines built to MiFID II and MAR requirements. Real-time monitoring of trading algorithms with automated reporting and circuit-breaker integration for risk management.
Deploying AI in healthcare and financial environments demands infrastructure that is secure by design, auditable by default, and resilient under regulatory scrutiny. We build zero-trust deployment architectures on Kubernetes with encrypted inference endpoints, immutable audit logging, automated model versioning, and real-time compliance dashboards — all running on HIPAA BAA-eligible, FCA-aligned, and SOC 2-certified cloud infrastructure across AWS, GCP, and Azure.
Our deployment practice goes beyond infrastructure. We implement continuous monitoring for data drift, concept drift, prediction distribution shifts, and fairness degradation — with automated alerting and retraining triggers that maintain model performance and compliance posture over time. Every deployment includes penetration testing, vulnerability assessments, PII masking, and role-based access controls designed for regulated industry requirements.
End-to-end encrypted inference APIs with mTLS authentication, OAuth 2.0 token management, rate limiting, and comprehensive audit logging. Our APIs are deployed on HIPAA BAA-eligible and FCA-aligned infrastructure with automatic failover, geographic redundancy, and sub-40ms response times across AWS, GCP, and Azure.
Real-time drift detection using statistical divergence measures (KL divergence, PSI, Wasserstein distance), automated retraining triggers with human-in-the-loop approval gates, immutable model registry with full version control, and compliance dashboards accessible to regulators on demand.
End-to-end encryption at rest and in transit, automated PII masking and de-identification, regular penetration testing by certified ethical hackers, OWASP-aligned vulnerability assessments, and adversarial robustness testing to protect models against data poisoning and evasion attacks.
For organisations with the strictest data sovereignty requirements, we deploy models on-premise or in air-gapped environments. Our containerised architecture supports bare-metal, private cloud, and hybrid deployments with the same governance and monitoring capabilities as our cloud offerings.
The AI landscape is evolving rapidly. We bring the latest research advances into regulated environments — safely, compliantly, and with measurable impact.
AI agents that autonomously execute multi-step clinical workflows — from lab order interpretation to care coordination — with human-in-the-loop oversight at critical decision points. Built on chain-of-thought reasoning and tool-use architectures that maintain full audit trails for every autonomous action taken in clinical and financial contexts.
Not every use case requires a 70B-parameter model. We deploy efficient small language models (1B–7B parameters) fine-tuned with LoRA and QLoRA for specific regulated tasks — achieving domain-expert performance while running on-premise on standard GPU hardware. Ideal for air-gapped healthcare environments and latency-sensitive financial trading systems.
Next-generation retrieval-augmented generation with hybrid search (dense + sparse retrieval), re-ranking pipelines, and citation-grounded responses. Our clinical RAG systems connect LLMs to NICE guidelines, BNF formularies, and peer-reviewed literature. Financial RAG systems ground responses in FCA handbook sections, case law, and compliance documentation.
Generate privacy-safe synthetic patient records and financial transaction data for model training and testing. Our synthetic data pipelines use differential privacy guarantees to create realistic training datasets that preserve statistical properties without exposing any real patient or customer information — enabling model development where real data access is restricted.
Tell us about your healthcare or financial AI challenge and we'll design a compliant solution tailored to your regulatory environment.
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