Proxa: Agent-Driven Executive Intelligence Platform with Grounded AI Retrieval
Challenge
Proxa’s existing AI framework was built for prototyping, not production reliability. The architecture relied on multiple orchestration layers, custom integrations, and opaque workflows that made failures difficult to detect and debug under real usage conditions.
Executives depended on the platform for decision-making, which made hallucinated or untraceable outputs unacceptable. Existing retrieval approaches produced confident-sounding narratives without clear attribution to source data, eroding trust in the system.
At the same time, Proxa’s engineering team needed an architecture they could independently operate and extend without maintaining a large AI infrastructure stack or dedicated AI operations team.
Proxa’s existing AI framework was built for prototyping, not production reliability. The architecture relied on multiple orchestration layers, custom integrations, and opaque workflows that made failures difficult to detect and debug under real usage conditions.
Executives depended on the platform for decision-making, which made hallucinated or untraceable outputs unacceptable. Existing retrieval approaches produced confident-sounding narratives without clear attribution to source data, eroding trust in the system.
At the same time, Proxa’s engineering team needed an architecture they could independently operate and extend without maintaining a large AI infrastructure stack or dedicated AI operations team.
Solution
The inherited orchestration framework was replaced with a simplified agentic retrieval architecture running directly on the Claude API. Instead of relying on vector databases, embedding pipelines, or layered retrieval services, the system uses an iterative retrieval loop where the model determines what information to retrieve next through direct document access.
A grounded synthesis layer was implemented so every generated answer and report includes inline source attribution. If claims cannot be supported by retrieved documents, the system refuses to generate unsupported output and surfaces conflicting information explicitly.
The platform includes a conversational React interface that allows executives to query document corpora in natural language. The infrastructure runs on Azure and integrates directly into Proxa’s existing product environment without introducing proprietary orchestration dependencies or operationally heavy infrastructure.
The architecture was intentionally constrained to components that Proxa’s internal team could fully understand, operate, and extend after deployment.
The inherited orchestration framework was replaced with a simplified agentic retrieval architecture running directly on the Claude API. Instead of relying on vector databases, embedding pipelines, or layered retrieval services, the system uses an iterative retrieval loop where the model determines what information to retrieve next through direct document access.
A grounded synthesis layer was implemented so every generated answer and report includes inline source attribution. If claims cannot be supported by retrieved documents, the system refuses to generate unsupported output and surfaces conflicting information explicitly.
The platform includes a conversational React interface that allows executives to query document corpora in natural language. The infrastructure runs on Azure and integrates directly into Proxa’s existing product environment without introducing proprietary orchestration dependencies or operationally heavy infrastructure.
The architecture was intentionally constrained to components that Proxa’s internal team could fully understand, operate, and extend after deployment.
Results
The system now operates in production as an executive-facing AI intelligence layer integrated into Proxa’s existing platform.
The architecture provides grounded retrieval and generative reporting with inline attribution across all outputs, improving trust and visibility into AI-generated reasoning. Failure modes are surfaced directly instead of hidden behind retrieval pipelines, making the system easier to debug and maintain.
By simplifying the stack and removing unnecessary orchestration layers, the platform reduced operational complexity while allowing Proxa’s engineering team to independently manage the runtime, workflows, and infrastructure without relying on external support.
The system now operates in production as an executive-facing AI intelligence layer integrated into Proxa’s existing platform.
The architecture provides grounded retrieval and generative reporting with inline attribution across all outputs, improving trust and visibility into AI-generated reasoning. Failure modes are surfaced directly instead of hidden behind retrieval pipelines, making the system easier to debug and maintain.
By simplifying the stack and removing unnecessary orchestration layers, the platform reduced operational complexity while allowing Proxa’s engineering team to independently manage the runtime, workflows, and infrastructure without relying on external support.
