GenAI Consulting & Assessment
Start your GenAI strategy with judgement, not hype
A GenAI assessment: we survey your use cases, prioritize them by impact and feasibility, and hand back a reference architecture on Amazon Bedrock plus a roadmap with estimated costs.
Prioritized use cases + architecture + roadmap
The problem
- You're under pressure to 'do something with AI', but PoCs stay as demos and never reach production.
- You're worried about the security and privacy of your data when using language models.
- You don't know which model to use, or how to keep the assistant from hallucinating about your information.
- You're afraid inference costs will spiral out of control.
What's included
- Use cases prioritized by real impact and feasibility, not by hype.
- Assistants and copilots with RAG over your documents and data.
- Agents that take actions, not just answer questions.
- Secure architecture on Amazon Bedrock: your data doesn't train third-party models.
- Evaluation with real metrics (quality, hallucination, cost) before going to production.
- Inference cost control with the same FinOps discipline as the rest of your cloud.
Your data is yours
We build on Amazon Bedrock, where your data isn't used to train third-party models and stays inside your AWS environment. We pick the model by outcome and cost, including Anthropic's Claude, not by hype.
How we work
Discovery
We identify the highest-impact use case and validate technical and data feasibility. No hype.
Plan & Quote
We design the architecture (model, data, security, cost) and propose a scoped MVP at a fixed price.
Execution
We build the MVP, evaluate it with real metrics and iterate until it's production-ready.
Hand-off & MSP
We ship it to production, monitored and with costs under control. We can keep operating and evolving it with you.
Stack & technologies
Betterfly: 10,000+ users across 5+ countries, in 8 weeks
FAQ
Is my data exposed when using a language model?
No. With Amazon Bedrock your data isn't used to train third-party models and stays inside your AWS environment.
What is RAG and why does it matter?
Retrieval-Augmented Generation: the model answers using your documents as the source. It reduces hallucinations and makes the AI useful on your information, not generic.
How do you keep the assistant from making things up?
With RAG, systematic evaluation and guardrails. We measure quality and hallucination with metrics before enabling production.
Which models do you use?
Mainly those available on Amazon Bedrock, including Anthropic's Claude, chosen per use case by outcome and cost.
Do you have a real case in production?
Yes: Betterfly's 'Buddy AI' assistant, with RAG over 1,000+ documents, now serving 10,000+ users across 5+ countries. Launched in 8 weeks.
