Model integration
LLMs and ML models wired into your product behind clean, typed interfaces.
AI engineering and applied machine learning: model integration, evaluation, and the production systems around the model that make it reliable.
A prototype that dazzles in a notebook is not a product. In production the model meets messy data, real latency budgets, cost ceilings, and failure modes no one scripted.
Most AI projects stall in exactly this gap, between a promising demo and a system you can trust, monitor, and afford to run.
We treat AI features like any other critical system: clear interfaces, evaluation harnesses, observability, and cost controls, so behavior is measured, not hoped for.
We integrate the right model for the job, wrap it in evals and guardrails, and design the data and infrastructure it depends on to scale.
LLMs and ML models wired into your product behind clean, typed interfaces.
Automated evals that quantify quality and catch regressions before users do.
Pipelines, vector stores, and serving designed for cost, latency, and scale.
Architecture, feasibility, and build-vs-buy guidance grounded in delivery reality.
Built an AI-powered video automation system using n8n that transforms ideas into fully scripted videos and auto-publishes them to TikTok, YouTube Shorts, Instagram Reels, LinkedIn, and more. Delivered AI workflow automation, content AI agents, and multi-platform publishing.
View case studyAI AutomationBuilt an AI-powered n8n workflow that prioritizes and schedules sales orders based on SLA, urgency, and profitability. Automated ERP integration, task creation, and logistics coordination, improving fulfillment speed, reducing SLA breaches, and optimizing warehouse operations.
View case studyAI engineering is the discipline of turning models into reliable products: integration, evaluation, observability, cost control, and the data and infrastructure around the model, not just prompting.
Both. We apply classical machine learning where it fits, classification, anomaly detection, forecasting, and large language models where reasoning over language is the job.
Often, yes. The usual gap is the engineering around the model: evals, data quality, latency, cost. We harden those so a demo becomes a dependable system.
Yes, architecture review, feasibility, and build-vs-buy guidance, with the same systems-first lens we bring to delivery.