Staff asking the same question about a policy, a spec, or a job from three years ago. A private model reads your own files and…
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// AI, APPLIED
AI that earns
AI that earns
its keep
The useful question isn't whether a model is impressive. It's which task in your business it actually removes. These hold up, and they run on hardware you own.
Invoices, forms, emailed orders, PDFs from a vendor who will never offer an API. Turned into records you can query and report on.…
Read more →A model out of the box knows the world and nothing about you. Going from that to something your staff actually use is an…
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KNOWLEDGE RETRIEVAL
Answers out of your own documents
Staff asking the same question about a policy, a spec, or a job from three years ago. A private model reads your own files and answers in seconds with the source attached, instead of somebody digging through a shared drive.
STRUCTURED EXTRACTION
Messy input into clean rows
Invoices, forms, emailed orders, PDFs from a vendor who will never offer an API. Turned into records you can query and report on. The data-entry job nobody wants and everybody is behind on.
DEPLOY · GROUND · INTEGRATE
Getting AI into your infrastructure
A model out of the box knows the world and nothing about you. Going from that to something your staff actually use is an infrastructure job, not a subscription.
01
Stand it up on your hardware. Spec the GPU host, size the model to the
VRAM you have, and run it behind your own firewall so nothing leaves the building.
Swap models later without rebuilding anything.
02
Point it at your data (RAG). Your documents get indexed and the model is
handed only the relevant passages at question time, so answers cite the file they
came from. Adding knowledge means dropping in a new document, not retraining. This
is the right approach for facts that change, and it is almost always the one you
want. Fine-tuning is for tone and output format, and it is a slow, expensive way to
teach a model things it will need to unlearn next quarter.
03
Wire it into where work happens. A chat box nobody opens is worthless.
Connect it to the systems already in use so it drafts the reply, fills the record,
or flags the exception inside the workflow instead of beside it.
NOTE
Where I'd tell you not to use it: anywhere the answer has to be exact and auditable every time. Financial totals, compliance evidence, anything you'd defend in a review. Models are good at reading, drafting, and sorting. Deterministic systems are better at arithmetic and records. Knowing which side of that line a task falls on is most of the value.
Not sure which one you are?
Most jobs are a mix. Tell me what's broken and I'll tell you what it takes.