Introducing CapNexus…
Where the Strength of Three Industry Leaders Capstone, RPE, and Makira - Become One.
Introducing CapNexus…
Where the Strength of Three Industry Leaders Capstone, RPE, and Makira - Become One.
AI should work where you need it, not where a vendor prefers. We meet organizations where they are in their AI journey, in their infrastructure, and in their industry. Whether that means deploying generative AI on AWS, running inference on dedicated GPU hardware in our data centers, or building agentic systems that connect to legacy applications on-premises, we deliver AI across every environment.
Cloud, data center, edge, or hybrid. The deployment model is determined by your latency requirements, data residency needs, cost model, and security posture. For organizations that need private AI infrastructure, our Infrastructure practice will operate GPU-accelerated compute in our data centers. For cloud-native AI, we build on AWS.
AI adoption depends on more than clean data. It requires organizational readiness: the right skills, governance structures, infrastructure, and a data estate that can support AI workloads in production. For organizations evaluating where to start or how to scale what they’ve already begun, we assess the full picture. Your team’s capabilities, your data landscape, your infrastructure posture, and your governance maturity.
Assess organizational AI readiness, data estate, governance maturity, infrastructure posture, skills gaps, and compliance readiness across all systems.
A prioritized AI roadmap, actionable recommendations, and the documentation your leadership needs to invest with confidence. What to build, in what order, and where the risks are.
Ongoing advisory as your business priorities shift, new AI capabilities emerge, and your organizational maturity evolves. The roadmap is a living document, not a one-time report.
We build and deploy generative AI platforms using Amazon Bedrock. Knowledge bases, retrieval-augmented generation (RAG), conversational interfaces, intelligent document processing, and AI-powered search grounded in your proprietary data. This is not about plugging in a chatbot. It’s about building AI that accesses your systems, understands your context, and produces outputs your teams can use in production with the guardrails, security, and governance that requires.
Not every organization can or should send its data to a cloud API. We deploy generative AI on AWS for elastic demand, in our data centers for cost optimization and data residency, or fully private for regulated and air-gapped environments. The deployment model is determined by your requirements, not ours.
Define the use case, data sources, deployment model, and governance requirements. Determine whether cloud, hybrid, or private deployment is the right fit.
Build the generative AI application, connect it to your systems and data, implement guardrails, and deploy to the right environment.
We operate the platform in production including monitoring, model updates, and guardrail management.
AI that doesn’t just respond to prompts but autonomously plans, executes multi-step workflows, uses tools, and makes decisions within defined guardrails. We build agentic AI systems that operate across your business processes: customer service agents that resolve issues end to end, document processing agents that handle intake and routing, and operational agents that monitor, decide, and act without waiting for a human in the loop.
This is the most significant shift in how organizations use AI. The difference between a chatbot that answers questions and an agent that completes work. We build these systems with governance from day one, including policy controls, human-in-the-loop checkpoints, and audit trails that regulated industries require.
Identify the workflows where autonomous AI creates measurable value. Define the agent architecture, tool access, governance policies, and human oversight model.
Build, test, and deploy agentic AI systems connected to your business processes with guardrails, policy enforcement, and monitoring built in.
We operate agentic AI in production including agent monitoring, policy updates, performance optimization, and escalation management.
Foundation models are general purpose. Your business is specific. We fine-tune and customize AI models on your proprietary data so the outputs reflect your terminology, your processes, and your domain expertise. This includes fine-tuning large language models for industry-specific tasks, training custom models for classification and extraction, and optimizing inference performance for production workloads.
We deploy fine-tuned models on AWS, in our data centers for organizations with data residency requirements, or on-premises for air-gapped environments. The result is AI that performs better on your use cases than any off-the-shelf model, running where your data and compliance requirements demand.
Assess the use case, evaluate base model options, define training data requirements, and determine the deployment surface.
Prepare training data, fine-tune the model, validate performance against business metrics, and deploy to the right environment with optimized inference.
We manage models in production including retraining cycles, performance monitoring, drift detection, and optimization.
We implement machine learning for use cases where the data supports it and the business impact is clear. Demand forecasting, anomaly detection, predictive maintenance, process optimization, and predictive analytics built on Amazon SageMaker and the AWS analytics stack. Every model is scoped and validated against real operational data before it reaches production.
Identify the use case, assess data readiness, and define success metrics. Determine the right modeling approach and deployment surface.
Build, train, and validate models against real data with real stakeholders. Deploy to production with monitoring and feedback loops.
We manage models in production including retraining, performance monitoring, and optimization. You get the performance without the overhead.
Tell us what you’re trying to solve and where your infrastructure lives. We’ll tell you what we’d recommend.
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