AI projects with a point
Content pipelines, product data enrichment, support triage, internal copilots — scoped around a measurable job, not a technology tour.
AI & business automation
AI and automation wired to real systems
Practical AI projects, workflow automation, and business process automation — connected to the commerce platforms, CMSs, and tools you already run. Systems that ship and stick, not demos.
What we automate
Workflow automation
Multi-step workflows with clear inputs, outputs, retries, and failure paths — auditable, not magic.
Business process automation
Order ops, reporting, data sync between commerce, CRM, and accounting — the copy-paste work your team should not be doing.
Commerce-aware by default
We know what an order, a SKU, and a fulfillment exception actually are — automation built by people who run commerce platforms.
Integration glue
APIs, webhooks, and queues connecting the systems that never quite talk: ERP, email, CMS, storefront, spreadsheets.
Guardrails & observability
Logging, human review steps, and kill switches — because automation you cannot see is automation you cannot trust.
When teams call us
- A team is copy-pasting between systems daily and it is starting to cost real hours
- Product data needs enriching, translating, or normalizing at a scale people can't do by hand
- Leadership wants AI somewhere useful, and someone has to make it concrete
- An existing automation runs nobody knows how, and it just broke
How an engagement runs
01
Find the job
We identify one process where automation pays for itself — measurable, bounded, real.
02
Prototype
A working slice against your real data and tools inside weeks, not quarters.
03
Productionize
Error handling, monitoring, review steps, and documentation — the difference between a demo and a system.
04
Extend
Once one workflow earns trust, we expand to the next — compounding, not big-bang.
Common questions
Is this a chatbot pitch?
No. Most of the value we ship is unglamorous: data enrichment, ops workflows, and integrations with an AI step where it genuinely helps. If a plain script beats a model, we will say so.
Which tools and models do you use?
Whatever fits the job and your constraints — hosted LLM APIs, workflow platforms, or plain code. We are not locked to a vendor and we do not resell licenses.
How do you keep automations from silently failing?
Every workflow ships with logging, alerting, retry logic, and human checkpoints where the cost of a wrong answer is high. Observability is part of the build, not an upgrade.
Have a process that should not need a human?
Describe the workflow — where it starts, where it ends, what goes wrong. We will tell you if automation is worth it.
Or see how we run migrations