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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$250M+
Pipeline generated
42%
Average pipeline growth
18.3%
Average budget saved
Results from actual client engagements.
Trusted by the teams we build with
Most Bedrock pilots never make it past the sandbox environment.
AWS Bedrock gives you API access to Anthropic Claude, Meta Llama, Amazon Nova, Mistral, and others through a single managed surface - which sounds like it removes the hard part. It does not. The hard part is deciding which model fits which workload, wiring up Knowledge Bases with embeddings that actually retrieve the right chunks, building Bedrock Agents that call the right tools without hallucinating action sequences, and putting guardrails in place before something embarrassing reaches a customer. Most mid-market teams get a working notebook demo in a week and then spend months trying to figure out why the same pipeline falls apart on real data, at real volume, with real latency constraints. IAM permission sprawl, chunking strategies that kill retrieval precision, prompt templates that degrade across model versions, and no observability layer are the usual culprits.
Revenue Institute comes in after the demo phase and before the production deadline. We audit what you have built, identify the specific failure points in your retrieval pipeline or agent orchestration, and rebuild the pieces that will not survive production load. We also set up the CloudWatch and third-party observability hooks you need to know when something breaks before your users tell you.
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 the wrong one costs real money at scale. We map your specific workloads - classification, generation, summarization, extraction - to the right model family, set up on-demand versus provisioned throughput correctly, and document the decision so your team can revisit it as models evolve without starting from scratch.
Knowledge Base and RAG pipeline design
Bedrock Knowledge Bases handle ingestion, embedding, and vector storage through managed connectors to S3, Confluence, SharePoint, and others. We design the chunking strategy, choose the right embedding model for your content type, configure the retrieval parameters, and test retrieval precision against real queries - not synthetic ones - before anything goes near production.
Bedrock Agents and action group wiring
Bedrock Agents let you define action groups backed by Lambda functions and OpenAPI schemas so the model can call your internal APIs. We build the action group definitions, write the Lambda handlers, tune the orchestration prompts, and test failure paths so the agent degrades gracefully when a tool call fails instead of looping or hallucinating a result.
Guardrails configuration and content filtering
Bedrock Guardrails let you define denied topics, sensitive information filters, and grounding checks at the API layer. We configure these against your specific compliance requirements - whether that is PII handling, industry-specific restricted topics, or brand safety rules - and wire them to your logging pipeline so you have an audit trail.
IAM, VPC, and security posture
Bedrock workloads running in a mid-market AWS account often inherit overly permissive IAM roles from earlier experiments. We scope service roles to least privilege, confirm that Knowledge Base data sources and S3 buckets are not publicly accessible, and set up VPC endpoints where your security team requires private connectivity.
Observability and prompt regression testing
Without structured logging, you cannot tell whether a model version update quietly degraded your outputs. We instrument your Bedrock calls with CloudWatch Logs and metrics, build a lightweight prompt regression suite you can run before deploying changes, and set up alerts on latency and error rate thresholds that matter for your specific use case.
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. We look at model choices, Knowledge Base configuration, agent definitions, IAM roles, and any existing logging. You get a written findings document with specific gaps and a prioritized build plan, not a slide deck full of recommendations without owners.
Build and integration
We build or rebuild the components identified in the audit: Knowledge Bases, agent action groups, Lambda handlers, guardrail policies, and observability hooks. Everything is built in your AWS account, documented in your internal wiki, and handed off with runbooks your team can follow without calling us.
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 on how to update prompts, add data sources, and read the monitoring dashboards. Ongoing support is available but the goal is a team that can operate this without a dependency on us.
Why AWS Bedrock is genuinely useful and genuinely tricky to operate.
AWS Bedrock solves a real problem for mid-market companies: it gives you managed access to a curated set of foundation models - Anthropic Claude, Meta Llama, Mistral, Amazon Nova, Cohere, and others - without requiring you to run GPU infrastructure or manage model weights yourself. You pay per token on-demand or reserve throughput capacity for predictable workloads, and the entire surface sits inside your existing AWS account with the IAM, VPC, and CloudTrail controls you already have. For a company that is not going to hire a team of ML engineers, that is a meaningful advantage over building on raw model APIs from multiple vendors with no unified security boundary.
The operational difficulty is that Bedrock exposes a lot of surface area and the defaults are not always production-appropriate. Knowledge Bases use fixed chunking strategies out of the box that work fine for clean, uniform documents and fall apart on mixed-format enterprise content. Bedrock Agents require careful prompt engineering in the orchestration layer and well-scoped action group definitions - an agent with vague tool descriptions will pick the wrong tool or attempt to call tools in the wrong sequence. Model version updates in Bedrock can change output behavior without a hard version pin, which means a pipeline that worked last month may produce different results today without any code change on your side. None of these are reasons to avoid Bedrock - they are reasons to build it correctly the first time.
What production actually looks like for a mid-market Bedrock workload.
A production Bedrock workload at a mid-market company typically involves a Knowledge Base pulling from two to five data sources - usually a mix of S3 documents, a SharePoint site, or a Confluence space - with an embedding model generating vectors that land in a managed OpenSearch Serverless collection. Queries come in through a Bedrock Agent that decides whether to retrieve from the Knowledge Base, call an internal API via an action group, or answer directly from the model's parametric knowledge. Guardrails sit at the API layer filtering denied topics and PII. CloudWatch captures invocation logs and latency metrics, and a lightweight test suite runs against a fixed set of known queries before any prompt or configuration change is deployed.
Getting to that state requires deliberate decisions at each layer - chunking strategy, embedding model choice, retrieval parameter tuning, action group schema design, guardrail policy definition, and IAM scoping. Teams that skip any of these steps usually discover the gap in production when a user gets a wrong answer, a hallucinated tool call, or a latency spike. Revenue Institute builds Bedrock workloads with all of these layers in place from the start, and we document every decision so your team understands what is running and why - not just that it is running.
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 license fee.
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