AI that actuallyships to production.
Custom AI agents, chatbots, automations, RAG systems, and LLM-powered apps — built by a team that ships production software, not prototypes. We're building this hub as a complete library of every AI development problem real businesses face.
What’s coming
40+ problems, answered in depth.
We’re building this hub as a working library — service pages, comparison guides, problem walkthroughs, real case studies. Built to rank in Google, AI Overviews, ChatGPT, and Perplexity. Built to actually help, not just to be indexed.
AI agents for customer support
Custom GPT chatbots
RAG knowledge bases
Workflow automations
Voice AI agents
AI-powered internal tools
Document intelligence
AI search & recommendations
LLM cost optimization
OpenAI vs Claude vs Gemini
AI for Shopify stores
AI for SaaS products
Most AI projects fail in the gap between the demo and production.
Getting a model to do something impressive once is now trivial. Getting it to do that same thing reliably, for every customer, at a cost that makes sense, without confidently inventing an answer — that is the entire job, and it is ordinary software engineering more than it is prompting.
The failures we get called in to fix are almost never model failures. They are missing evaluation, no retrieval layer so the thing hallucinates on anything outside its training, token costs nobody modelled before launch, and no way to tell whether a change made the system better or worse.
So we build these the way we build everything else: a defined scope, a fixed price, an evaluation set written before the code, and a monitoring story for after it ships. If the honest answer is that a deterministic script does the job for a fraction of the cost, that is what we will tell you.
Agents and assistants
Support agents, internal copilots and triage systems grounded in your own documentation and data. The hard parts are scope boundaries, escalation to a human, and knowing what the agent must refuse to answer — not the conversation itself.
RAG and knowledge bases
Retrieval over your content so answers cite something real. Most of the quality in a RAG system comes from chunking, indexing and retrieval evaluation, which is why a system built on the same model as a weak one can be dramatically better.
Document and data extraction
Turning invoices, contracts, specifications and forms into structured data. Usually the highest-return AI work available to an established business, because the baseline it replaces is a person retyping things.
AI inside an ecommerce store
Search that understands intent, product recommendations, support deflection and merchandising assistance. We build these on Shopify regularly, which is where the rest of this studio spends its time.
Cost and model selection
Token spend is the line that surprises people after launch. Routing cheap requests to small models, caching aggressively, and measuring cost per resolved task rather than per call is usually worth more than switching provider.
Evaluation and monitoring
A test set that represents real inputs, scored before and after every change. Without it you are shipping on vibes, and quality regressions are invisible until a customer finds one.
Questions we get asked
Before you join the list.
Is this service available now?
The full hub is still being built, but the work itself is live — we already ship AI features inside the ecommerce and startup builds we do. If you have a defined project, talk to us now rather than waiting for the library to be finished.
What does an AI development project usually cost?
A focused integration — a support agent on an existing knowledge base, a document extraction pipeline — typically lands in the low five figures. Anything involving custom retrieval, evaluation harnesses and a production monitoring story is a larger engagement. We scope to a fixed number before starting rather than billing an open-ended hourly rate.
Do you build on OpenAI, Claude or something else?
Whichever fits the task, and we benchmark rather than guess. Model choice is also not a permanent decision — we build behind an abstraction so swapping providers is a configuration change, because the price and capability ordering between them has changed repeatedly and will again.
Will you tell me if I do not need this?
Regularly. A large share of what gets scoped as an AI project is a reporting problem, a data quality problem, or something a deterministic script does more cheaply and more reliably. We would rather say that at the proposal stage than six weeks into a build.
How do you stop it making things up?
Retrieval against your own content so answers are grounded in source material, an evaluation set written before the build so quality is measured rather than felt, and explicit refusal behaviour for questions outside scope. A demo that looks impressive and a system that is safe to put in front of customers are different pieces of work.
In the meantime