AWS Bedrock Development Services
InterCode builds enterprise generative AI applications on Amazon Bedrock — the fully managed foundation model service inside AWS. From RAG pipelines and Bedrock Agents to private model fine-tuning and content safety with Guardrails, we help you ship production AI without managing ML infrastructure.
Managed Foundation Models Inside Your AWS Account
Amazon Bedrock gives enterprises access to a curated catalogue of foundation models — Anthropic Claude, Meta Llama, Amazon Titan, Mistral, and Stability AI — through a single managed API inside your existing AWS account. There is no infrastructure to provision, no GPU clusters to manage, and no model weights to store. Your data stays within your VPC and is never used to train the underlying models. At InterCode, we help teams move beyond proof-of-concept by building production Bedrock architectures. We design Bedrock Agents that orchestrate multi-step business workflows — calling Lambda functions, querying databases, and looping through tool use without custom orchestration code. We set up Bedrock Knowledge Bases connected to OpenSearch or Aurora for retrieval-augmented generation over private documents. Where generic models fall short, we run private fine-tuning jobs on Bedrock to adapt Titan or Llama to domain-specific vocabulary and tone. We also implement Bedrock Guardrails to enforce content safety policies, deny-listed topics, and hallucination filters before responses reach end users. The result is a compliant, observable generative AI platform built on the AWS services your security and operations teams already trust.
What We Build With AWS Bedrock
We build enterprise chatbots using Claude on Bedrock with private VPC networking, IAM-scoped access, and CloudWatch observability — no data leaving your AWS environment. We design automated document processing pipelines where Bedrock and Lambda extract structured data from contracts, invoices, and reports at scale. For knowledge-intensive applications, we connect Bedrock Knowledge Bases to OpenSearch so employees can query internal wikis, policy documents, and support histories in natural language. We implement image generation workflows for e-commerce using Stable Diffusion on Bedrock, and we build Bedrock Agents that automate multi-step business processes like order management, IT ticket triage, and compliance checks — all without writing custom orchestration code.
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AI integration
Add AI capabilities to your existing software without a big-bang rewrite. InterCode provides ai integration services — embedding LLMs, AI agents, and intelligent automation into your SaaS platform, internal tools, or enterprise systems.
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Set up scalable, secure, and cost-optimised cloud infrastructure for your application. InterCode provides cloud deployment services, devops services, and managed cloud services — so your engineering team can focus on product features, not infrastructure problems.
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Learn moreFrequently Asked Questions
AWS Bedrock is the right choice if you are already on AWS and need your AI workloads to stay inside your cloud account with IAM-based access control and no data egress. Azure OpenAI makes sense for Azure-native stacks. The direct OpenAI API offers the latest models fastest but requires managing your own VPC controls. Bedrock's key advantage is model diversity — you can swap between Claude, Llama, and Titan without changing your application code.
Bedrock currently offers Anthropic Claude (Haiku, Sonnet, Opus), Meta Llama 2 and Llama 3, Amazon Titan (text and embeddings), Mistral and Mixtral, AI21 Jurassic, Cohere Command and Embed, and Stability AI Stable Diffusion for image generation. The model catalogue expands regularly.
Bedrock charges per input and output token for on-demand inference, with no minimum commitments. Provisioned throughput is available for predictable high-volume workloads at a reserved hourly rate. Fine-tuning jobs are charged by training tokens. Costs vary by model — Claude Sonnet is priced higher than Titan but often requires fewer tokens for the same task.
No. AWS explicitly states that customer inputs and outputs on Bedrock are not used to train or improve the underlying foundation models. Data is processed within your AWS account and region. You can further restrict data movement using VPC endpoints and AWS PrivateLink.
Bedrock supports fine-tuning for select models including Amazon Titan and Meta Llama. You prepare a JSONL training dataset in S3, launch a fine-tuning job from the Bedrock console or API, and the customized model is stored privately in your account. No ML expertise is required to run the job, though dataset quality is the main driver of improvement.
Bedrock Agents is a fully managed service that orchestrates multi-step workflows using foundation models and AWS Lambda action groups. It requires no custom orchestration code and integrates natively with AWS IAM and CloudWatch. LangGraph is an open-source Python framework for building stateful, graph-based agent workflows with more flexibility but more infrastructure responsibility. We recommend Bedrock Agents for AWS-native teams and LangGraph when you need complex custom graph logic or portability across cloud providers.
Start Your AWS Bedrock Project
Talk to our AI engineers about building production generative AI on AWS Bedrock. We will design the right architecture — RAG, agents, fine-tuning, or Guardrails — for your use case.
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