Custom AI Agent Development
AI agents that do the work, not just talk about it
We design and build agents on Amazon Bedrock that query your data, operate your systems and reach production with permissions, evaluation and cost under control.
From proof of concept to production
The problem
- You have a demo that impresses but that nobody dares put in front of a customer.
- The agent answers well 80% of the time and you have no idea what happens in the other 20%.
- Every team starts its own experiment and there's no shared foundation.
- There's no way to tell whether the agent improves the business or just adds cost.
What's included
- Use case definition and success metrics agreed with the business.
- Agent design: tools, memory, limits and human-in-the-loop checkpoints.
- Built on Amazon Bedrock (including models like Anthropic's Claude) with RAG over your data.
- Guardrails, identity-based permissions and human approval on sensitive actions.
- Systematic evaluation against a set of real cases, before and after each iteration.
- Production rollout with observability, traces and per-use-case cost control.
Multichannel and in production, not in a notebook
For Betterfly we built 'Buddy AI', a multichannel GenAI assistant with RAG over more than 1,000 documents, five conversational personalities and ticketing integration. It serves more than 10,000 users across more than five countries, and we launched it in 8 weeks.
See the Betterfly caseHow we work
Discovery
We pick the highest-impact use case and validate that the data and systems can support it.
Plan & Quote
We design the agent architecture and agree on a tight MVP, with success metrics and a closed price.
Execution
We build the agent, evaluate it against real cases and iterate until it hits the agreed threshold.
Hand-off & MSP
We leave it in production, monitored and with cost under control. We can keep operating and evolving it.
Stack & technologies
Agents evaluated, monitored and in production
FAQ
How fast can I have something in production?
It depends on the case, but we work with tight MVPs. At Betterfly the assistant went to production in 8 weeks.
Which models do you use?
Mainly the ones available on Amazon Bedrock, including Anthropic's Claude models. We choose by result and by cost for each task, not by hype.
How do you keep the agent from hallucinating?
With RAG over curated data, guardrails, narrow tools and an evaluation set we run on every iteration. And a human in the loop where the risk calls for it.
Does it integrate with my current systems?
Yes. We build the tool and connector layer into your databases, APIs and SaaS, with identity-based permissions.
