Seamline Studio
An internal tool for preparing our apparel brand’s designs on patterns and viewing them in 3D. In development and not for sale.
Leave the repetition to the machine and the decision to people.
We tie AI to a measurable business step and automate repetitive work.
Not every process needs AI; sometimes a well-built rule engine is cheaper and more reliable. We pick the right tool first and, if we use a model, design how its output is checked and where a person approves it.
Problems businesses run into
What we often find
- Repetitive office workA large share of team time goes to copying and classifying.
- Unchecked outputNobody notices when a model answers wrongly.
- Unpredictable costModel usage bills cannot be forecast.
What we propose
Our approach
- Process first, model secondWhat can be solved by rules is solved by rules.
- Human-approved flowsEmployees have the final say on steps that affect outcomes.
- Measurement and budget limitsAccuracy and cost are recorded and capped.
What we do
Document and text processing
Classification, summarising and information extraction.
Workflow automation
Event-driven, queue-based processes.
Image pipelines
Running models in background workers.
Extracting data from documents
Pulling fields from invoices, forms and contracts.
Answers from company knowledge
Assistants that cite sources from documents the user may access.
Quality measurement
Testing model changes with evaluation sets.
Where it helps
Request routing
Sending incoming messages to the right team.
Content preparation
Producing drafts for editor approval.
Image production
Automatically preparing product visuals.
Pre-processing support requests
Classifying requests and offering agents a draft reply.
Technologies we use
- TypeScript
- NestJS
- PostgreSQL
Related projects
Frequently asked questions
Is our data used to train models?
We choose the provider and contract accordingly, and consider self-hosted models for sensitive data.
Do you guarantee results?
We agree an accuracy target and measure it through trials. We do not promise numbers in advance.
