AI development

AI development at the studio means shipping product features customers actually use, not GPT-wrapped demos. Most engagements wire LLM features into a product that already has users: a chatbot grounded in the team's documentation, a voice agent that does tool-calling against a real calendar or CRM, or an internal operator-assist surface that turns a 30-minute lookup into a one-line query.

The pattern matters more than the model

We run the same engineering rigour on AI as on any production system. Prompt and cost engineering are documented from day one, RAG indexes are versioned alongside the code, prompt-injection guardrails are wired in at the boundary, and observability ships with the first release so a regression in answer quality is caught before the user notices it. OpenAI, Claude and DeepSeek are the default model surfaces because they ship fast and swap cleanly when a cheaper or better model catches up, the architecture doesn't marry one vendor.

Retrieval and agents

Most useful AI features need the model to know things it was never trained on. We build retrieval-augmented generation properly: documents chunked and embedded into a vector store (pgvector on PostgreSQL for most builds, a managed index where scale demands it), retrieval tuned and evaluated rather than assumed, and the index versioned alongside the code so answer quality is reproducible. Agents go a step further, the model plans and calls tools, and we wire those tools (often through a framework like LangChain) against real APIs with strict input and output validation, so an agent can act without going off the rails.

Voice and multi-modal

For voice and multi-modal, the standard architecture is Whisper for transcription, an LLM for reasoning, and a deterministic tool layer that talks to the real systems behind the conversation, the calendar, the CRM, the inventory database, the support inbox. A single Python service runs the transcribe, reason, act loop with predictable latency and an audit trail for every tool call. The same pattern moves into client work without rewriting it.

Automation and data pipelines

A lot of AI value is unglamorous plumbing: getting data in and out reliably. We build AI-assisted import and sync pipelines, and use workflow tooling like n8n to connect the systems a business already runs. For Power Vape Shop that meant importing products from a warehouse system and a CRM into Shopify with AI-assisted matching, then keeping stock in sync across physical shops and online. The model does the fuzzy matching; deterministic code does the moving.

When AI isn't the answer

The fastest win on most AI projects is figuring out which 20% of the brief actually needs an LLM and which 80% is better solved by a deterministic rule. We name that boundary in scoping and write the deterministic parts as plain code; the LLM gets the bits where ambiguity is the feature, not the bug.