Corewell SystemsAI Automation
The repetitive reading, writing, and routing work inside your operation, done by software and checked by your team.
The problem it solves
Every operation has work that's judgment-light but time-heavy: summarizing intake forms, drafting routine replies, routing requests, keying data out of documents. That work is now automatable, reliably, inside your own system, with your team reviewing the output. We build AI into operational software where it saves real hours, not where it makes good demos.
What's included
- Automation assessment on your real workflows
- AI features built into your existing system
- Document and data extraction pipelines
- Human review and approval flows
- Measurement of the hours actually saved
Evidence
What we have actually built
Each entry below exists in a system we designed and shipped. Client and product names are withheld by policy. The engineering is not.
A conversation becomes a finished document
We have AI documentation running in production: a professional records a meeting, and the system transcribes it and drafts a structured document in that organisation's own template, reviewed and approved by a person before it counts.
Identifiers removed before the model sees anything
Names, dates of birth, record numbers, phone numbers, and email addresses are stripped from text before it reaches an AI model, and re-associated afterwards. Privacy is a design constraint in these systems, not a policy statement.
Suggestions that show their reasoning
The classification and coding our systems propose always arrives with the evidence behind it, so the person approving can check the logic in seconds instead of trusting a label.
Structured records pulled out of prose
Free-written documents are read and turned into structured data (goals, plans, referring parties, projected schedules), populating fields a person would otherwise re-key from something they just wrote.
Prompts tuned without a redeploy
The instructions driving generation live in an editable file the system reloads on change, so refining how output reads takes minutes and never requires a release.
Built to fail safely
Generation runs as tracked background work with progress, retry, and partial-failure recovery, so a failed piece is reprocessed from stored source rather than losing the whole job. An assistant grounded in the product's own documentation answers staff questions in plain language.
Where this shows up
Industries where we have delivered this capability. It applies well beyond them. These are the ones with systems behind them.
Under the hood, for technical buyers
Server-side model integration with no keys exposed to the client, identifier scrubbing ahead of every call, asynchronous job processing with progress reporting and per-unit retry, editable prompt sources loaded at runtime, human approval gates on anything that becomes a record, and audit logging of automated actions.
Full technology stack, and why we chose it →Straight answers
AI Automation, answered plainly
What buyers ask before committing. Short answers, no hedging.
Is our data used to train someone else's AI model?
No. Identifiers are stripped from text before it reaches a model, generation runs server-side under our own keys with no data used for model training, and every automated action is logged. Privacy is a design constraint in these systems, not a policy statement.
What if the AI gets something wrong?
A person approves it before it counts. Nothing generated becomes a record on its own. The output is drafted for review, and the suggestions our systems make arrive with the evidence behind them so the reviewer can check the logic in seconds instead of taking a label on trust.
Where does AI actually save time?
In work that is judgment-light but time-heavy: turning a recorded conversation into a structured document, pulling fields out of free-written text, classifying and coding, and summarising for a non-expert reader. We build it where it removes hours, not where it makes a good demo.
Can we change how it writes without paying for a new release?
Yes. The instructions driving generation live in an editable file the system reloads on change, so refining tone and structure takes minutes and never requires a deployment.
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