VYKRONAI & LLM Integration
AI development built for production, not demos.
We build LLM-powered features with OpenAI, Anthropic, and Amazon Bedrock: chatbots, RAG pipelines, and automation that hold up under real usage and real cost constraints.
We embed AI where it drives measurable business value, not where it demos well.
How it typically works
A process built for this discipline.
Use-case audit
We map where AI actually moves a business metric versus where it just looks impressive in a demo.
Model and architecture selection
We pick the model, retrieval strategy, and hosting setup for your latency, cost, and data constraints.
Prompt and eval design
We build a test set before we build the feature, so quality is measured, not guessed at.
Build and integrate
We ship the feature inside your existing product, with logging and fallbacks for when a model call fails.
Monitor and tune
We track cost, latency, and accuracy in production and keep tuning after launch.
What we build
What you get when we take this on.
LLM-powered workflows and internal tools
Chatbots and product assistants
RAG pipelines over private and proprietary data
AI automation for operations and support
Prompt design and evaluation frameworks
Cost and latency optimization for models already in production
Shipped in production on Slay
We built AI companion and auto-caption features into Slay's engagement economy platform, and our engineers have judged 200+ AI solutions at Google's GenAI Hackathon.
FAQ
Common questions about this work.
How do you decide whether a feature actually needs AI?
We start from the business metric you want to move, then work backward. If a rules-based system or a simpler UI gets you there faster and cheaper, we say so. AI earns its place in the stack, it does not get added by default.
Which models do you build with, and how do you choose?
Mostly OpenAI, Anthropic, and Amazon Bedrock, chosen per use case on accuracy, latency, and cost, not brand preference. For most products this means a smaller, cheaper model for high-volume tasks and a stronger model reserved for the calls that need it.
How do you keep AI costs predictable at scale?
We design for token efficiency from the start: caching, prompt compression, model tiering, and usage caps where they make sense. We also instrument cost per request so you see the number before it surprises you in a bill.
Can you add AI features to an existing codebase?
Yes. Most of our AI work is integration into a product that already exists, not greenfield builds. We audit your current architecture first so the AI layer fits how your system already handles auth, data, and jobs.
How do you handle data privacy with third-party model providers?
We review what data actually needs to reach a model provider, apply redaction or scoping where it doesn't, and configure provider settings (retention, training opt-out) to match your compliance requirements before anything ships.
Let's build something that ships.
Tell us your goals, timeline, and budget. We will tell you honestly whether we are the right team and what it would take to get started.
Average response time: 12 hours