None of this is as new as it sounds. Lawyers have always worked with tools they didn't build. West bound the cases into reporters; the reporters became Westlaw. The tool keeps changing. The judgment about what the law means stays with the lawyer.
Any decent model already knows what a limitation-of-liability clause is and can spot an off-market indemnity. That knowledge isn't scarce anymore, and it isn't where your risk sits.
The hard part is everything around the model. Your best positions live in people's heads and have to be written down. Privileged material has to stay privileged. The output has to hold up on the tenth matter the way it did on the first, and a lawyer has to know exactly where to sign off.
Most teams are doing none of it. A lawyer pastes a contract into a chat window at 9pm, gets a reasonable answer, and keeps nothing. There's no record of what was asked and no way to do it the same way next week.
Right now that's one clever person improvising, with client data along for the ride. We make it something your whole practice can rely on.
We turn the work you already do by hand into a system you can rely on. None of it is glamorous. It's the deliberate part most firms never get around to.
We sit down with your team and get the standards out of people's heads and onto paper. How you actually mark up a liability cap. When something gets escalated. Where your real risk tolerance is, as opposed to the one in the engagement letter. That playbook is your practice's judgment, and it stays yours.
We break each review task into small, well-defined jobs: NDA triage, contract review, DPA screening. Each one is scoped so the system knows which document it's looking at and applies the right logic, and so the work that comes back looks like your practice produced it.
We run the system against your own closed matters and check what it does. You see how it behaves on real files before it goes anywhere near live work. A demo can be staged; a run against a hundred of your old contracts can't.
We write down the rules: how data is handled, where privilege lives, what gets logged, and where a lawyer has to review before anything goes out. That's what makes the whole thing demonstrably reliable enough to meet the expectations of clients and courts. It's also the line that protects the practice if something goes wrong.
Your team learns to use it well, and we keep it from going stale. The law changes, and the models change faster. We keep the playbook current so the system is still worth using a year from now. Most teams would rather not carry that themselves, so we do.
The enterprise legal-AI platforms are built for the biggest firms and priced accordingly. Everyone else has been choosing between doing nothing and signing a contract that's hard to leave. There's a third option.
The judgment we build in is your practice's asset. If we ever part ways, the playbook and everything built on it goes with you.
Enterprise platforms price for organizations ten times your size. Here you pay for the system your practice actually uses, at a scope you set.
When something breaks, you call the person who built it. The advisor who set up your system is the one who keeps it running.
One honest caveat. Owning your system means maintaining it; that's what the retainer covers. And a lawyer is still responsible for every output. Verification isn't a backstop for the occasional error. It's the job. Courts are already sanctioning firms that filed AI work nobody checked, so the sign-off is built in on purpose: a human (lawyer) in the loop, at the point that keeps your practice inside its standard of care.
You know AI should be part of how you practice, and you're not interested in gambling on half-built tools or handing your workflow to a platform. We build something that fits how you already work. You own it.
You've done the experimenting. Now you want something the whole team uses the same way, with safeguards you can stand behind to a client or a partner.
You're a GC or legal-ops lead moving fast on a thin budget. You need something that works and that you can defend internally, without betting the department on a single vendor.
Blackletter is the work of Chris Archer, a lawyer who also builds information systems and software. His twenty years of professional experience include practicing at two AmLaw100 firms and nearly a decade as general counsel and chief compliance officer of a real estate and construction organization. He has sat in the seat his clients sit in, carrying the same confidentiality and compliance weight, and deciding for himself where technology belongs.
He came to law from the technical side, with an undergraduate focus in information systems and finance. He has also studied artificial intelligence and machine learning while earning an MBA from Columbia Business School, and is now pursuing a master's in computer science at Georgia Tech.
That combination gives Chris perspective and experience few can offer. Most legal-AI advice comes from technologists who have never practiced, or from lawyers who don't build. Chris does both, which is what lets a firm walk away owning a system rather than taking one more thing on faith.
Own Your System. Don't Rent the Platform.
Maybe this is your first real move into AI, or maybe you're cleaning up a drawer of experiments that never stuck. Either way, we'd want to understand how your practice works before recommending anything.
carcher@blackletterlegal.ai