AI & Automation

AI is genuinely useful in a backend when it’s wired into a real workflow with real failure handling — not when it’s a chat widget bolted onto the side of a product. We integrate LLMs and machine learning services directly into application logic, and build the automation and orchestration around them so they behave predictably in production.

What we build

LLM integration, AI-powered analysis pipelines, model training, and bespoke automation using workflow orchestration tools like n8n, matched to the stack the rest of the system already runs on rather than introduced as a separate silo.

Evidence, not claims

Pixicular is our own AI-powered SaaS: an image-analysis API with ML-driven endpoints built on Amazon Rekognition, and a custom image-classification model — built with Hugging Face — currently in active development to extend what the platform can detect on its own. Datingular API uses automated, continuous assessment of user identity, input and uploaded assets for fraud prevention — an automation problem where the “workflow” runs on every single request, unattended, and has to get the call right without a human in the loop.

How we work

We start from the decision the automation actually has to make, then choose the smallest reliable tool for it — sometimes that’s a hosted LLM call, sometimes it’s a purpose-built model, sometimes it’s a deterministic pipeline with no AI in it at all once the requirements are clear. Production AI features need the same rigour as any other backend logic: rate limits, fallback behaviour, cost monitoring, and a plan for what happens when the model gets something wrong.

What you get

Automation that keeps running without supervision, and AI features that degrade gracefully instead of silently breaking the product when a provider has an outage or changes a model’s behaviour. If you already have an AI feature that’s expensive, slow, or unreliable, we’re equally happy to audit and rebuild it as to design one from scratch.

This work is usually inseparable from Backend Development — the pipeline is only as good as the API and data model feeding it.