What we build on Bedrock
LLM applications
Chat assistants, document analysis, decision support and content workflows wired to your data.
RAG & knowledge bases
Answers grounded in your documents with citations, permissions and automatic reindexing.
AI agents
Autonomous agents that plan, call tools and act on your systems under guardrails.
Fine-tuned models
Domain-specific models for tone, format and task accuracy when prompting and RAG plateau.
Production discipline on AWS
- Models and data controls inside your own AWS tenancy
- Bedrock guardrails, scoped tool permissions and audit logs
- Human-in-the-loop checkpoints for sensitive actions
- Evaluation suites and cost/quality observability per conversation and agent
Evidence in production
Frequently asked questions
Why build AI on Amazon Bedrock?
Bedrock gives you access to frontier models inside your own AWS tenancy, with data controls, guardrails and pay-per-use economics — no data leaves your cloud.
Which models do you use?
We choose the model that fits the task and budget — for example Anthropic Claude, Llama and others available through Bedrock — and validate the choice with evaluation suites on your real use cases.
Can you build RAG and agents on Bedrock?
Yes. We build RAG pipelines with Bedrock knowledge bases, and AI agents that call your systems through the Model Context Protocol (MCP) with guardrails and audit logs.
Do you handle guardrails and compliance?
Yes. We configure Bedrock guardrails, scoped tool permissions, human-in-the-loop checkpoints and observability so production systems stay within policy.
Do you support fine-tuning?
Yes, where it measurably improves quality. We evaluate prompting, RAG and fine-tuning, and only tune when it pays off against your metrics.
Related
Build your AI on AWS with Develo
Bring your use case and we'll map models, architecture, guardrails and costs on Amazon Bedrock.
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