Scriptix
AI Automation
Custom AI models that automate the decisions and workflows eating up your team's time, and surface predictions they can actually act on. They're built into the systems you already run, not a bolt-on chatbot.
What's Included
How We Approach It
Most AI projects fail for the same reason: they try to do too much before proving the first thing works. We start narrow: one well-defined use case with a clear before/after, whether that's a classification model added to a support queue or a demand-forecasting pipeline feeding into planning. Get that working and measurably useful, then expand.
Where possible, we integrate rather than rebuild: connecting to an LLM API for language tasks, embedding a lightweight model directly into an existing pipeline, or adding a data layer that scores and enriches records before they reach your application. Your team keeps the systems it already knows; the AI layer sits on top or alongside it.
We're upfront about where AI is the wrong tool, too: deterministic, auditable rule-based logic still wins for financial calculations, compliance workflows, and anything where an incorrect answer carries real consequences. Part of the job is telling you when a simpler system will actually serve you better.
See how this approach applies in practice: an illustrative scenario on building a recommendation engine →
Built With
PyTorch, Hugging Face, LangChain, LlamaIndex, vector databases (Pinecone), and the OpenAI and Anthropic APIs, matched to what the use case actually needs, not a fixed stack.
Common Questions
Do we need our own data science team to work with you? expand_more
No. We handle the model selection, training or integration, and deployment. Your team's job is knowing the business problem well enough to help us define what "working" looks like.
Can this work with our existing software instead of replacing it? expand_more
In most cases, yes. That's usually the faster and lower-risk path. We look at your existing APIs and data first, and design the integration around what's already there before considering a rebuild.
How do you handle data privacy for AI features? expand_more
For sensitive data, we look at self-hosted or open-weight models instead of sending data to third-party APIs. What's appropriate depends on your data and compliance requirements, which we scope before recommending an architecture.
Have an AI use case in mind?
Tell us what you're trying to automate and we'll tell you honestly whether AI is the right tool for it.
Talk to Us