AI automation
AI automation, assessed on your data first
Most AI proposals skip the only question that matters: is your information in a state where a model can be useful? We answer that before quoting, and sometimes the answer is no.
Where the work has exact rules, we say so and write code instead — the solar document system on this site is that decision made deliberately, and it is better software for it. Where judgement, language or volume is the real problem, AI earns its place.
Where AI actually helps
- Staff search PDFs, SOPs and old email threads for answers that exist somewhere.
- The same message gets read, classified and forwarded by hand a hundred times a week.
- Somebody has promised the board an AI project and nobody has checked the data.
What we build with AI
- Assistants that answer from your own documents, with the sources shown
- Workflow automation across the tools you already pay for
- Document and message triage, with a person kept in the loop
- Integrations with AI services, built to survive one of them failing
- An honest assessment first, including when the answer is not to build it
AI work we have shipped
PIDY — Shopify app & try-on
Integrated an AI try-on pipeline into a Shopify storefront: avatar generation runs on GPUs, so it was built as an asynchronous job with a primary provider and a fallback when one of them is down.
Read the case study →Outreach Engine — Outbound automation
Our own outbound system: deterministic detectors establish every claim, the model writes only the sentences around them, and a draft citing a defect that nothing found is discarded rather than repaired.
Read the case study →What AI automation costs
Assessed before it is quoted. We look at the data you have, tell you what is realistic, and then give one fixed price for the build. If the honest answer is that ordinary code would do the job better, that is what you will hear.
AI automation questions we get first
- What have you actually shipped with AI?
- Two things, and we will name the limits of both. The PIDY case study is an AI try-on pipeline integrated into a Shopify store, including the asynchronous job handling and the fallback for when a provider fails. The outreach engine is our own: a model writes the prose in it, and everything factual — every defect, every number, every gate — is ordinary code, deliberately. We are not going to pad that list with projects we did not build; the rest of our work is non-AI engineering, and some of it was non-AI on purpose.
- Can you build a chatbot over our documents?
- Yes, and the first step is looking at the documents. An assistant is only as good as what it reads, and we would rather find out in a call than after you have paid for one.
- Will an AI assistant invent answers?
- Any system like this can, which is why we build them to show their sources and to say when they do not know. Where being wrong is expensive, a person stays in the loop by design.
Tell us what you need.
One call, then a written scope and a fixed price. If we are the wrong people for it, we will say so rather than quote for it.