We find the expensive repetitive work in a company and replace it with a system.
DIGS AI brings AI automation into everyday operations - the manual steps that eat hours every week and never make it into anyone's job description. We turn them into software that runs quietly and keeps running.
Four steps, one process at a time.
We do not automate "everything". We pick one process where the money leaks, prove the result, and only then move on to the next one.
-
Audit
We sit with the people who do the work, map the process, and measure where time and money actually go. The output is a written picture of what is worth automating and what is not.
-
Pilot
One process, one success metric agreed in writing. We build a working system on real data and check it against that metric before anyone decides on the next step.
-
Rollout
The pilot becomes part of daily operations: integration with mail, documents and internal systems, access rules, training for the team, monitoring.
-
Ongoing support
Models change, documents change, people change. We keep the system running, update it, and stay responsible for how it behaves in production.
It can run inside your perimeter.
Most AI tools send your documents to someone else's cloud. For a law firm, an accounting practice or any company with client data, that is often a non-starter. We build systems that can run on the client's own servers, with open models, so the data never leaves the building.
Cost is fixed. There is no per-token meter that grows with usage and surprises finance at the end of the month.
- Runs on the client's servers when data is sensitive.
- Data does not leave the company perimeter.
- Fixed cost, no token counter.
- Engineering discipline: metrics, monitoring, ownership.
Built by someone who ran LLM inference in production.
Before DIGS AI, the founder built and operated a self-hosted LLM inference platform for three years - the kind of infrastructure that has to work every day, not just in a demo.
These are the founder's numbers from previous work, not a claim about DIGS AI as a company. They are here to show what "running models on your own servers" looks like in practice.
What companies usually come with.
Examples of the kind of work we are equipped to handle. No client names - the patterns repeat across industries.
Billable hours in law firms
Lawyers forget to log time, so real work never reaches the invoice. A system watches mail and documents per employee, spots work that was done but not logged, and raises a flag before the billing cycle closes.
Manual data entry in accounting
Invoices, bank statements and receipts typed by hand into the ledger. The system reads the documents, prepares the entries and leaves a human to confirm instead of retype.
Incoming mail and documents
A shared inbox that someone has to sort every morning. The system classifies what came in, extracts what matters, routes it to the right person and drafts the routine replies.