AI & LLM Platforms - AWS Bedrock
AWS Bedrock is powerful and
easy to misconfigure at scale.
We design and build production-grade AI workloads on AWS Bedrock - model selection, Knowledge Bases, Agents, and guardrails - so your team ships something that actually runs in operations, not just in a demo.
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Operators and teams we've worked with
Most Bedrock pilots never make it past the sandbox environment.
AWS Bedrock puts Anthropic Claude, Meta Llama, Amazon Nova, Mistral, and others behind one managed surface, which sounds like it removes the hard part. It does not. The hard part is matching the right model to each workload, wiring Knowledge Bases that retrieve the right chunks, building Bedrock Agents that call tools without hallucinating action sequences, and setting guardrails before something embarrassing reaches customers. Most teams ship a notebook demo in a week, then spend months on why it falls apart at real volume. The usual culprits: IAM sprawl, chunking that kills retrieval precision, prompts that degrade across versions, no observability.
Revenue Institute comes in after the demo and before the production deadline. We audit what you built, pinpoint the failure points in your retrieval pipeline or agent orchestration, and rebuild the pieces that will not survive production load - plus the CloudWatch and observability hooks to catch breakage first.
What we do with AWS Bedrock
What we build inside your AWS Bedrock environment.
Model selection and cost architecture
Bedrock's model menu is wide, and picking wrong costs real money at scale. We map each workload - classification, generation, summarization, extraction - to the right model family, set on-demand versus provisioned throughput correctly, and document the call.
Knowledge Base and RAG pipeline design
Bedrock Knowledge Bases handle ingestion, embedding, and vector storage via managed connectors to S3, Confluence, and SharePoint. We design the chunking strategy, pick the right embedding model, tune retrieval parameters, and test precision against real queries before production.
Bedrock Agents and action group wiring
Bedrock Agents define action groups backed by Lambda functions and OpenAPI schemas so the model can call your internal APIs. We write the definitions and Lambda handlers, tune orchestration prompts, and test failure paths so the agent degrades gracefully instead of looping.
Guardrails configuration and content filtering
Bedrock Guardrails define denied topics, sensitive information filters, and grounding checks at the API layer. We configure these against your compliance requirements - PII handling, restricted topics, brand safety - and wire them to your logging pipeline.
IAM, VPC, and security posture
Bedrock workloads in mid-market AWS accounts often inherit overly permissive IAM roles from early experiments. We scope roles to least privilege, confirm Knowledge Base sources and S3 buckets are private, and add VPC endpoints where needed.
Observability and prompt regression testing
Without logging, you cannot tell whether a model update quietly degraded your outputs. We instrument Bedrock calls with CloudWatch Logs and metrics, build a prompt regression suite run before deploys, and alert on the latency and error thresholds that matter.
Our framework
How an AWS Bedrock engagement runs.
Audit and architecture
We review your existing Bedrock setup, or your planned architecture if you are starting fresh, covering model choices, Knowledge Base configuration, agent definitions, IAM roles, and logging. You get a written findings document with specific gaps and a prioritized build plan.
Build and integration
We build or rebuild the components from the audit: Knowledge Bases, agent action groups, Lambda handlers, guardrail policies, and observability hooks. Everything lives in your AWS account, documented in your wiki, with runbooks.
Stabilize and hand off
We run the workload under realistic load, validate retrieval quality and agent behavior against your acceptance criteria, and train your team to update prompts, add data sources, and read the dashboards. Support is available, but the goal is self-sufficiency.
Why AWS Bedrock is genuinely useful and genuinely tricky to operate.
AWS Bedrock solves a real problem for mid-market companies: managed access to a curated set of foundation models - Anthropic Claude, Meta Llama, Mistral, Amazon Nova, Cohere, and others - without running GPU infrastructure or managing model weights. You pay per token on-demand or reserve throughput, inside your AWS account with the IAM, VPC, and CloudTrail controls you already have. For a company not hiring ML engineers, that beats wiring raw model APIs from multiple vendors with no unified security boundary.
The difficulty is that Bedrock exposes a lot of surface area and the defaults are not always production-appropriate. Knowledge Bases use fixed chunking that works on clean documents and falls apart on mixed-format enterprise content. Bedrock Agents need careful orchestration-prompt engineering and well-scoped action groups - vague tool descriptions make an agent pick the wrong tool. Model version updates can change behavior without a hard pin, so a pipeline that worked last month may behave differently today. None of this is a reason to avoid Bedrock - it is a reason to build it correctly.
What production actually looks like for a mid-market Bedrock workload.
A production Bedrock workload typically pulls a Knowledge Base from two to five data sources - S3 documents, a SharePoint site, or a Confluence space - with an embedding model generating vectors in a managed OpenSearch Serverless collection. Queries arrive through a Bedrock Agent that decides whether to retrieve from the Knowledge Base, call an internal API via an action group, or answer directly. Guardrails filter denied topics and PII. CloudWatch captures invocation logs and latency metrics, and a test suite runs against known queries before changes ship.
Getting there requires deliberate decisions at each layer - chunking strategy, embedding model, retrieval tuning, action group schema, guardrail policy, IAM scoping. Teams that skip any of these find the gap in production, when a user gets a wrong answer, a hallucinated tool call, or a latency spike. Revenue Institute builds workloads with all these layers from the start, and documents every decision so your team knows what is running and why.
We're vendor-agnostic
Other AI & LLM Platforms platforms we specialize in
Not sure AWS Bedrock is the right fit? We implement and optimize these too - and we'll tell you honestly which one fits your business.
AWS Bedrock questions, answered
We already have a Bedrock proof of concept running. Do you start over or work with what we have?
We start with what you have. The audit phase is specifically designed to assess existing work and identify which pieces are worth keeping versus which ones will cause problems in production. Starting over is rarely necessary and almost always more expensive than fixing the specific failure points. We will tell you honestly which category each component falls into.
How do you choose between Claude, Llama, Nova, and the other models available in Bedrock?
Model selection depends on your specific task type, latency requirements, context window needs, and cost tolerance. We run structured evaluations against your actual data and queries - not benchmarks from a paper - and make a recommendation with documented reasoning. We also flag where a cheaper model is good enough so you are not paying for capability you do not use.
Our data is sensitive. Can Bedrock workloads be kept private and compliant?
Bedrock does not use your prompts or completions to train foundation models by default, and it supports VPC endpoints for private connectivity. We configure IAM roles at least-privilege scope, set up VPC endpoints where required, and configure Bedrock Guardrails for PII filtering. We are not a compliance auditor, but we build the technical controls your compliance team needs to do their review.
What does retrieval quality actually mean and how do you measure it?
Retrieval quality means the Knowledge Base is returning the chunks that actually answer the query, not just chunks that are topically adjacent. We measure it by building a test set of real questions with known correct source documents, running retrieval against that set, and calculating precision and recall. We then tune chunking size, overlap, and retrieval parameters until the numbers are acceptable for your use case.
Do you work with teams that have no prior AWS experience?
We can, but Bedrock is not the right starting point for a team with no AWS footprint. You need an account, basic IAM hygiene, and S3 in place before Bedrock is practical. If your team is starting from zero on AWS, we will tell you what needs to be in place first and help you get there before we touch Bedrock-specific work.
How long does a typical Bedrock engagement take?
A focused engagement - audit plus one production workload - typically runs several weeks depending on the complexity of your data sources and the number of agent action groups involved. We do not quote a timeline until we have seen the scope. Engagements that drag on are usually caused by unclear acceptance criteria or data access delays on the client side, not the build itself.
Can you integrate Bedrock agents with our existing CRM or ERP systems?
Yes. Bedrock Agents call external systems through action groups backed by Lambda functions and OpenAPI schemas. If your CRM or ERP has an API, we can write the action group definition and Lambda handler to call it. If the system only has a database connection or file export, we build the appropriate intermediary. We have done this with Salesforce, HubSpot, NetSuite, and several custom internal APIs.
Make AWS Bedrock actually earn its keep.
Stop paying for a tool your team routes around. Start running on one they trust.
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