We design, train, and deploy private large language models (LLMs) tailored to your data — fully secure, compliant, and optimized for Pacific enterprise environments.
Every model is fine-tuned to your domain — finance, healthcare, energy, or telecom — ensuring data never leaves your control.
On-premises or hybrid AI deployments powered by containerized environments (Docker / Kubernetes) for full data isolation.
Deploy production-ready models in weeks, not months — using open-weight LLMs, proprietary APIs, and Pacific-hosted compute.
Train domain-specific large language models on your proprietary data to create unique AI capabilities that understand your business context, terminology, and workflows.
Example Use Case: Regional bank automated 90% of loan document analysis, reducing processing time from 3 days to 4 hours while improving accuracy to 98%.
Deploy secure, on-premises AI systems within Pacific data centers ensuring complete data sovereignty, regulatory compliance, and zero cloud dependency.
Example Use Case: Healthcare provider deployed private AI for patient data analysis, achieving 100% HIPAA compliance while processing 10,000+ records daily without external data transfer.
Automate complex business processes including compliance checking, financial forecasting, contract analysis, and regulatory reporting with custom AI pipelines.
Example Use Case: Energy company automated regulatory compliance reporting, reducing 160 hours/month of manual work to 10 hours of review, saving $180K annually.
Speech recognition, natural language processing, and computer vision systems tailored for Pacific languages, accents, and visual contexts.
Example Use Case: Telecom operator deployed voice AI for customer support in English and Fijian, handling 65% of inquiries automatically and improving customer satisfaction scores by 35%.
Real-world AI systems for Pacific industries — built with compliance, speed, and reliability in mind.
Challenges: Complex regulatory compliance, fraud detection, loan processing delays, customer service bottlenecks
Our Solutions:
Expected Results: 75% faster loan approvals, 90% reduction in compliance review time, $250K+ annual savings
Challenges: Patient data privacy, diagnostic support, medical records management, staff shortages
Our Solutions:
Expected Results: 40% reduction in diagnostic errors, 60% faster medical coding, improved patient outcomes, full data sovereignty
Challenges: Equipment maintenance, grid optimization, demand forecasting, sustainability reporting
Our Solutions:
Expected Results: 50% reduction in equipment failures, 30% improvement in energy distribution efficiency, $400K+ annual savings
Challenges: Network optimization, customer churn, support costs, service quality monitoring
Our Solutions:
Expected Results: 60% reduction in support costs, 25% decrease in churn rate, 99.9% network uptime, improved customer satisfaction
Challenges: Personalized learning, administrative burden, assessment grading, student support
Our Solutions:
Expected Results: 45% improvement in student engagement, 70% reduction in grading time, better learning outcomes, scalable education delivery
Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, FastAPI
Llama 3.1 (8B-70B), Mistral (7B-8x7B), Falcon 40B, GPT-4 API, Claude API, Gemini API, Custom fine-tuned variants
Docker, Kubernetes, On-Premises GPU Clusters (NVIDIA A100/H100), Azure/AWS Hybrid Cloud, Near-Pacific Data Centers
End-to-end encryption, ISO 27001 controls, HIPAA compliance, GDPR alignment, SOC 2 Type II, Air-gapped deployments
PostgreSQL, MongoDB, Vector databases (Pinecone, Weaviate), S3-compatible storage, Redis caching
Prometheus, Grafana, MLflow, Custom AI performance dashboards, Real-time model monitoring
Flexible pricing to match your project scope, timeline, and budget. All prices in USD.
Success Rate: 92% of pilots progress to full implementation
Most Popular: 70% of clients choose this option
Long-Term Value: 40% lower cost than project-by-project
GPU servers, Kubernetes cluster, networking, monitoring
Data cleaning, annotation, quality assurance
Hands-on workshops for your technical and business teams
24/7 monitoring, model retraining, security updates
Transparent, proven methodology ensuring successful AI deployment from concept to production
Duration: 1-2 weeks
Deliverable: Comprehensive assessment report with recommendations and cost estimates
Duration: 1 week
Deliverable: Signed agreement, detailed project plan, and communication protocols
Duration: 2-4 weeks
Deliverable: Production-ready datasets with quality report
Duration: 4-8 weeks
Deliverable: Trained AI models meeting performance targets
Duration: 2-4 weeks (parallel with model development)
Deliverable: Production-ready infrastructure with 99.9% uptime SLA
Duration: 2-3 weeks
Deliverable: Live AI system integrated with your operations
Duration: 1-2 weeks
Deliverable: Trained team, complete documentation, and support plan
Duration: Ongoing (3-6 months initial period)
Deliverable: Continuously improving AI system with measurable ROI
A regional Pacific bank faced overwhelming manual effort reviewing thousands of regulatory documents to maintain compliance with AML, KYC, and Basel III standards.
Solution: Bespoke AI fine-tuned a large language model (LLM) on regional financial regulations and historical compliance data. The system automatically summarized, categorized, and flagged high-risk sections across lengthy documents in minutes.
Outcome:
An Australian healthcare provider required a privacy-preserving AI to assist researchers in analyzing large volumes of de-identified patient data for epidemiological insights.
Solution: A domain-specific LLM was designed and trained on anonymized EMRs and clinical literature. The model extracted structured insights, summarized clinical notes, and identified correlations between treatments and outcomes—all in a HIPAA-aligned, air-gapped environment.
Outcome:
A South Pacific energy utility was experiencing frequent equipment downtime and escalating maintenance costs across distributed assets.
Solution: Bespoke AI developed a predictive maintenance system powered by machine learning models trained on sensor telemetry, maintenance logs, and weather data. The system predicted potential failures weeks in advance and triggered automated maintenance alerts.
Outcome:
Your data. Your model. Pacific made. Launch your next private AI project with our engineering team.