Everything we build,
& everything we do.

A fine-tuned large language model isn't just a chatbot with your logo on it. It's a knowledge system trained on your SOPs, your customer interaction history, your internal documentation, your product catalogue, and the specific vocabulary your team and customers use every day.

We start with a foundation model — typically GPT-4, Claude, or an open-source variant — and layer your proprietary data on top via RAG (Retrieval-Augmented Generation) and fine-tuning. The result is a model that answers questions about your products, routes your specific support queries, and generates content in your brand voice.

Deployment is entirely flexible — embedded into your website as a customer-facing assistant, plugged into Slack as an internal knowledge tool, integrated into your CRM to surface contextual recommendations, or exposed via a private API for your engineering team to build on.

Every model includes a continuous improvement loop. We monitor response quality, track where the model falls short, and retrain on new data regularly. Your AI gets smarter as your business evolves — not stale the moment it's deployed.

n8n is the most powerful open-source workflow automation platform available — 200+ integrations, conditional logic, webhooks, code execution nodes, sub-workflows, and full self-hosting capability. But raw power without someone who knows how to wield it means months of trial and error.

We do the engineering for you. We sit down with your team, map every manual process in your business, identify the highest-leverage automation opportunities, and build production-ready workflows that integrate with your exact stack — not generic templates you'd need to reshape.

Every automation we build includes error handling, retry logic, monitoring dashboards, and alerting. Workflows don't just run — they run reliably. When something breaks (and with complex systems, something occasionally will), you get a clear notification with a plain-English explanation and an immediate fix, not a silent failure.

For businesses ready to move beyond simple if-this-then-that logic, we layer AI decision nodes into the workflows — sentiment analysis that routes support tickets, lead scoring that triggers different follow-up sequences, document classification that tags and files automatically. The automation thinks, not just executes.

There's a significant difference between reporting and intelligence. Reporting tells you what happened. Intelligence tells you what will happen — and what to do about it. We build the latter.

Our analytics engagements start with a data audit — understanding what you're collecting, where it lives, how clean it is, and what questions you actually need answered. We then architect the pipeline: ingestion from your existing sources (CRM, payment processor, product analytics, support tool), transformation and storage in a cloud data warehouse, and a model layer that runs predictions on the unified dataset.

Prediction models we commonly build: churn propensity (who is likely to leave in the next 30 days), lifetime value forecasting (expected revenue per customer segment), demand forecasting (inventory and capacity planning), anomaly detection (fraud, error, or unusual patterns in real time), and next-best-action models (what to offer each customer at each stage of the journey).

The output isn't a static report — it's a live dashboard your team can actually use, connected to alerting that fires when a prediction threshold is crossed, and integrated with your automation workflows so the business responds automatically, not just observes.