Teravera is a pioneering provider of secure AI infrastructure for developers, offering a zero trust API that safeguards data, prevents hallucinations, and ensures AI integrity across leading platforms like OpenAI, Google, and AWS. Teravera technology empowers developers to confidently integrate AI into critical enterprise application Agents and workflows by tightly controlling data access and output accuracy. With a mission to bring trust and reliability to AI, Teravera serves as a cornerstone for developers and organisations adopting advanced AI solutions responsibly.
Teravera needed a secure, scalable way to extend its zerotrust AI approach into AWS while replicating the architecture and functionality of its existing Azure based system. The goal was to design a dynamic RAG driven knowledge base and agent solution, one that could create new knowledge bases on the fly through an API. If a requested knowledge base didn’t exist, it would be instantly created; if it did, new documents would be seamlessly uploaded, ingested into the underlying vector database, and made ready for immediate querying.
A key requirement was departmental segmentation, enabling users to query specific knowledge bases and receive tailored, accurate answers. Instead of relying on OpenSearch serverless, the solution needed to leverage Aurora PostgreSQL, using a single database with separate tables for each department’s knowledge base. This approach demanded a precise architectural design to balance flexibility, data integrity, and scalability while ensuring a smooth migration from Azure to AWS without disrupting Teravera’s established processes.
To meet Teravera’s goal of replicating and enhancing their Azure-based setup on AWS, we designed and delivered a highly technical, API-driven Retrieval-Augmented Generation (RAG) solution powered by Amazon Bedrock. This architecture allows knowledge bases to be created, managed, and queried on demand, while ensuring scalability, security, and departmental segmentation requirements.
Key components of the solution include:
Dynamic Knowledge Base CreationBy combining S3 for secure storage, Aurora PostgreSQL for structured vector data, Lambda for orchestration, and Bedrock for AI reasoning, the solution delivered a dynamic, on-demand knowledge base ecosystem. It not only replicated Teravera’s Azure-based architecture but also improved scalability, security, and operational efficiency through AWS-native services.
When planning Teravera’s platform, Cloud Combinator assessed multiple LLM delivery options to balance performance, security, and enterprise readiness. While OpenAI’s GPT family demonstrated strong generative capabilities, the need for AWS-native integration, enterprise compliance, and low-latency delivery made Amazon Bedrock the preferred solution.
Below is a comparison of Amazon Bedrock (using Claude 3.5 and Titan in latency optimised mode) against OpenAI’s GPT4/GPT4o API models:
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Amazon Bedrock (Claude 3.5 / Titan – Latency Optimised) |
OpenAI GPT‑4 / GPT‑4o |
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Typical Latency (Optimised) |
~1.2–1.3 seconds (latency optimised mode reduces response time by ~40%) |
~2.5–3.0 seconds (varies by model and traffic load) |
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Cost Model |
Pay as you go per token; eligible for AWS credits and enterprise programs |
Pay as you go per token; no AWS credit integration |
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Infrastructure Control |
Fully AWS managed, running natively within AWS for tighter security and integration |
API only; hosted on OpenAI’s external infrastructure |
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Scaling Approach |
Serverless – scales automatically with demand |
API scaling with explicit rate limits |
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Privacy & Compliance |
Built on AWS’s enterprise compliance framework (ISO, SOC, HIPAA) |
High compliance standards but outside AWS ecosystem |
Automated Knowledge Base Creation – When documents are uploaded to a department without an existing knowledge base, the platform now provisions all required AWS resources automatically, including the knowledge base, data source, agent, and alias which eliminated any manual setup.
Fast, Relevant Responses – Leveraging Aurora PostgreSQL for vector storage and Amazon Bedrock’s latency optimised inference mode, the system delivers sub-second retrieval times and has reduced LLM response latency by over 40%, ensuring quick and accurate answers.
Error Handling Built In – Queries sent to non-existent departments are instantly rejected with a clear error message, providing consistent and predictable feedback for users.
Secure and Scalable by Design – The entire solution is built exclusively on AWS managed services (S3, Lambda, Bedrock, Aurora PostgreSQL), ensuring enterprise grade security, compliance, and automatic scalability as usage grows.
Teravera’s CEO, Keith Wood, reflected on the project: "The implementation of a dynamic knowledge base architecture has already streamlined the way we manage data internally, and their expertise with services like Amazon Bedrock and OpenSearch has given us a platform we can rely on.
What stood out most was Cloud Combinator’s collaborative approach; they felt like an extension of our own team. "
Teravera expressed strong satisfaction with the solution delivered by Cloud Combinator, praising both the technical execution and the collaborative approach taken throughout the project. While they explored joining the AWS Solution Provider Program (SPP), their strict client privacy obligations meant they could not proceed, as participation would have required granting external access to their billing information.
Despite this limitation, Teravera has been clear about their intent to continue partnering with Cloud Combinator on future AWS initiatives. They recognise the value, deep technical expertise, and trusted partnership demonstrated during this engagement, and view Cloud Combinator as a key collaborator for their ongoing cloud and AI transformation.