Applied AI for Legal, Risk and Compliance
Compliance functions are asked to provide assurance over a transaction volume no team could ever review. The response has been sampling: test a few, infer the rest, and trust that the sample was representative. That was a reasonable accommodation to human capacity, and our view is that it is a harder one to defend now.
In short
Mach12 stands up AI labs that apply AI across legal, risk, and compliance: contract review and obligation extraction, policy interpretation, regulatory change monitoring, controls testing, and audit preparation. The lab connects contract repositories and GRC platforms to the transaction systems so compliance is tested against what happened.
Where Compliance Runs Out of Capacity
These are the patterns we see most. In our experience they come down to the distance between what the systems already hold and what anyone has had the hours to do with it, rather than to a technology gap.
Contract obligations are rarely tracked systematically
What a contract commits the company to is in the document. What the company does is in the operating systems. Little compares the two routinely, so the first signal is usually a dispute.
Controls testing is periodic and sampled
Testing a sample once a quarter leaves most exceptions undetected, and the ones that are caught surface long after they could have been corrected.
Regulatory change monitoring is manual
Somebody reads the updates and decides what applies. Coverage depends on that person's bandwidth and on whether they recognized the implication.
Contract review is a bottleneck on the business
Legal review queues delay deals. Most of what is reviewed is standard, and the review is looking for the handful of deviations that matter.
Policy questions route to a small number of people
The guidance is written. Finding and applying the right part of it requires expertise, so the questions route to the same handful of experts.
Audit preparation pulls the organization off its work
Evidence gathering, sample production, and walkthrough documentation absorb weeks of effort from people whose actual job is something else.
What the Lab Builds
Built against your systems and your data, shipped into production with the people who use them. A given lab will build a subset of this, in whatever order discovery ranks it.
Contract intelligence
Obligations extracted into something trackable, and then tracked.
- Obligation, deliverable, and term extraction across the portfolio
- Contract review against your playbook with deviations flagged
- Clause comparison and precedent retrieval
- Performance monitoring against contractual commitments
Continuous controls
Testing the full population continuously instead of a sample quarterly.
- Full-population controls testing against live transactions
- Segregation of duties and authorization monitoring
- Exception detection with routing and documented disposition
- Control evidence assembled as it is generated
Regulatory and policy
Keeping up with change, and making guidance available where decisions happen.
- Regulatory change monitoring with applicability assessment
- Policy question answering grounded in your own guidance
- Policy to control to test traceability maintenance
- Gap analysis against a new or revised requirement
Audit readiness
An audit as a retrieval exercise rather than a reconstruction project.
- Evidence package assembly on request
- Sample selection and supporting documentation retrieval
- Process walkthrough documentation maintained from system activity
- Finding remediation tracking to closure
Typical First Builds
Chosen for speed to production as much as for value. We would rather have something working in your environment early than something more ambitious on paper.
- 01Obligation extraction across the executed contract portfolio
- 02Continuous testing of the control that currently costs the most to test
- 03A policy answer agent over your own guidance, for the questions that repeat
- 04Contract review triage against your standard playbook
Built Against What You Run
Connectors are built during stand-up. You do not replace anything first, and your data does not leave your boundary.
Relevant Accelerators
Applications we have already built in this area. A lab can deploy one as-is, extend it, or use it as the pattern for something new.
Common Questions
- Is AI-assisted contract review defensible?
- It is defensible when it is built to be. That means explainable extraction with the source clause cited, measured accuracy on a real document set, and a lawyer making the decision. Review triage, where AI sorts and flags and a person decides, is the pattern we would recommend starting from.
- Can we test controls on the full population instead of a sample?
- Yes. Once testing is automated, the marginal cost of testing the full population rather than a sample is close to zero, and the assurance you can give changes accordingly.
- What about privilege and confidentiality?
- The lab runs inside your boundary. Privileged material stays in your control and is not sent to a third-party service or used to train anyone else's model. For legal functions this is usually the first thing established, and it is verified during stand-up rather than asserted.
- Will our regulator or auditor accept this?
- They will ask how it works, and you need to be able to answer. That is why the logging, evaluation results, and human decision points are designed in from the start. We have built in environments where DCAA, FDA, and financial auditors had a view, and the requirement was consistent: show the work.
Common in these industries
The work carries across industries. These are where we see it most often.
Build This in Your Business
Tell us what your legal, risk & compliance function runs on and where the work is piling up. We will scope the first build.
Start a Lab