AWS AI Agents
We build, optimize, and evaluate AI agents on AWS Bedrock. From first prototype to reliable production assistant, your agent works the way your business needs it to.
Talk to an AI Agent Specialist
Trusted by Companies Worldwide
When Your AI Agent Isn't Working as Expected
Your agent ignores the knowledge base or hallucinates
It generates answers that sound right but aren't grounded in your data. Users lose trust, and you can't ship it to production
Context drops after a few exchanges
The agent forgets what was said earlier in the conversation. Longer interactions become unreliable
No way to measure quality at scale
You are testing manually, catching issues one by one, with no repeatable way to track if things are getting better or worse
High cost, low accuracy
You picked a model that's expensive but still delivers inconsistent results. You're not sure which model fits your use case best
What We Build
We design and build AI agents that connect to your data, follow your business rules, and respond to users in context. Each agent runs on Amazon Bedrock with a serverless backend on Lambda and API Gateway. For EPIRA.ai, we built four specialized AI agents that turn weeks of manual grant work into minutes — from document extraction to eligibility scoring.
What you get:
- An agent grounded in your knowledge base, not general internet data
- Guardrails for sensitive topics and out-of-scope questions
- Structured outputs (JSON actions, redirects, data lookups) beyond plain text responses
- Personalized responses based on user profile and session data
- Modular architecture — multiple agents working together through shared APIs
We test multiple foundation models against your actual use case, measure accuracy, speed, and cost, then recommend the best fit. In our fintech case, we tested three different models on AWS Bedrock and found that the cheapest option failed 80% of knowledge base lookups.
What you get:
- Side-by-side model comparison on your data
- Clear recommendation based on accuracy, latency, and cost
- Prompt engineering and system prompt refinement
- Migration support if you need to switch models
We build automated testing pipelines that run hundreds of test cases against your agent after every change. No more manual QA for AI quality. In our fintech project, this pipeline ran 500+ test cases per iteration and reduced QA time by 70%.
What you get:
- Automated evaluation using frameworks like DeepEval
- Faithfulness, consistency, and latency metrics tracked per model and prompt version
- Repeatable test suites that catch regressions before they reach users
- A pipeline you can run yourself after handover
We build investor-ready AI products from scratch on AWS. For i Got This!, we delivered a complete AI mental health assistant MVP in 3 months — with empathetic avatars, Bedrock-powered conversations, and safety guardrails — achieving over 90% cost savings by moving AI logic from a third-party platform to AWS.
If you're evaluating AWS Bedrock for your startup's AI product, our guide on building an AI agent MVP on AWS Bedrock covers the architecture and approach in detail.
What you get:
- Working AI product built on AWS (Bedrock, Lambda, Cognito, DynamoDB, API Gateway)
- Secure architecture with authentication and data protection
- A product you can demo to investors with real functionality
- A path to scale without rebuilding
Custom AI Agents on AWS Bedrock
We design and build AI agents that connect to your data, follow your business rules, and respond to users in context. Each agent runs on Amazon Bedrock with a serverless backend on Lambda and API Gateway. For EPIRA.ai, we built four specialized AI agents that turn weeks of manual grant work into minutes — from document extraction to eligibility scoring.
What you get:
- An agent grounded in your knowledge base, not general internet data
- Guardrails for sensitive topics and out-of-scope questions
- Structured outputs (JSON actions, redirects, data lookups) beyond plain text responses
- Personalized responses based on user profile and session data
- Modular architecture — multiple agents working together through shared APIs
AI Model Selection & Optimization
We test multiple foundation models against your actual use case, measure accuracy, speed, and cost, then recommend the best fit. In our fintech case, we tested three different models on AWS Bedrock and found that the cheapest option failed 80% of knowledge base lookups.
What you get:
- Side-by-side model comparison on your data
- Clear recommendation based on accuracy, latency, and cost
- Prompt engineering and system prompt refinement
- Migration support if you need to switch models
AI Evaluation Pipelines
We build automated testing pipelines that run hundreds of test cases against your agent after every change. No more manual QA for AI quality. In our fintech project, this pipeline ran 500+ test cases per iteration and reduced QA time by 70%.
What you get:
- Automated evaluation using frameworks like DeepEval
- Faithfulness, consistency, and latency metrics tracked per model and prompt version
- Repeatable test suites that catch regressions before they reach users
- A pipeline you can run yourself after handover
AI Agent MVPs for Startups
We build investor-ready AI products from scratch on AWS. For i Got This!, we delivered a complete AI mental health assistant MVP in 3 months — with empathetic avatars, Bedrock-powered conversations, and safety guardrails — achieving over 90% cost savings by moving AI logic from a third-party platform to AWS.
If you're evaluating AWS Bedrock for your startup's AI product, our guide on building an AI agent MVP on AWS Bedrock covers the architecture and approach in detail.
What you get:
- Working AI product built on AWS (Bedrock, Lambda, Cognito, DynamoDB, API Gateway)
- Secure architecture with authentication and data protection
- A product you can demo to investors with real functionality
- A path to scale without rebuilding
Who This Is For
Startups building AI products
You have an idea for an AI-powered product and need a technical team to build it on AWS
Companies with underperforming AI agents
Your agent is live but hallucinating, losing context, or giving inconsistent answers
Teams without AI evaluation processes
You ship AI updates with no automated way to test quality before they reach users
Products that need to switch or compare models
You are on a model that's too expensive or not accurate enough and need a data-backed comparison
Startups moving to AWS for investor or credit requirements
Your investors expect AWS infrastructure, or you need to migrate to qualify for AWS Activate credits. We build your AI agent on AWS from the start so you don't rebuild later.
AWS Services We Use
Foundation models, knowledge bases, guardrails
End‑to‑end platform to build, train, and deploy ML models at scale
Serverless compute for agent logic and orchestration
Secure API endpoints for agent communication
Session storage, user data, conversation history
Knowledge base storage, document repositories
User authentication and profile management
Frontend hosting for web applications
Why Choose Perfsys?
- AI agents built around your business logic and your data
- Powered by AWS Bedrock and Lambda for reliability at scale
- We handle the infrastructure, you focus on the use case
- From prototype to production without rebuilding the stack
Our Achievements
Our Achievements
30+
International clients
across Media, Software & Technology, Financial Services, Energy, Logistics
10+
Years Experience
in AWS & DevOps
70+
Projects completed
delivered successfully
Media
Software & Technology
Financial Services
Energy
Logistics
What our clients say
Perfsys is trusted by startups and SMBs worldwide for delivering scalable, reliable, and cost-optimized AWS cloud solutions.
FAQs
Common questions about building and optimizing AI agents on AWS with Perfsys.
An AI agent is software that uses a foundation model to process user input, reference a knowledge base, and return relevant answers or actions. On AWS, agents typically run on Amazon Bedrock with serverless infrastructure handling the logic, storage, and API communication.
We work with all foundation models available through Amazon Bedrock, including Amazon Nova and Anthropic Claude families. We test multiple models against your specific use case and recommend the best fit based on accuracy, speed, and cost.
We combine prompt engineering, knowledge base grounding, guardrails configuration in Bedrock, and automated evaluation pipelines. In our fintech case, we improved faithfulness scores from 80% to 95% by systematically testing and refining the agent across 500+ automated scenarios.
A focused AI agent MVP takes 8 to 12 weeks. Optimization of an existing agent (model selection, prompt refinement, evaluation pipeline) typically takes 4 to 6 weeks. Timelines depend on complexity, number of knowledge sources, and integration requirements.
Yes. We audit your current setup, build an evaluation pipeline to measure where it's failing, test alternative models or prompt strategies, and deliver measurable improvements. This is exactly what we did for the fintech AI advisor.
An automated system that runs hundreds of test cases against your agent after every change. It measures faithfulness (does the answer match the knowledge base), consistency, latency, and edge case handling. We use frameworks like DeepEval and integrate the pipeline into your CI/CD workflow so quality checks happen before deployment.
Cost depends on scope. A focused agent MVP starts in the $10,000–$25,000 range. Optimization projects for existing agents are typically $5,000–$15,000. AWS infrastructure costs for AI agents are pay-per-use — you pay per Bedrock invocation, per Lambda execution, and per GB of storage. Most early-stage agents cost under $100/month in AWS fees.
Yes. Everything runs in your AWS account. We hand over full documentation, evaluation pipeline code, and architecture diagrams. Your team can operate, update, and extend the agent independently.
Yes. Many startups migrate to AWS because investors require it or because AWS Activate credits can significantly reduce infrastructure costs. We build AI agents on AWS from scratch or migrate existing ones from other platforms, so you get both the AWS foundation investors expect and the credits that help fund your growth.
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