Building the open intelligence layer for legal AI.

Unlock the intelligence you already own.

When you use someone elses model you give it instructions, playbooks, corrections and workflows. In return, you get an answer. But you don't know if it's correct, why it did what it did , or whether it even followed your directions.

LiTiL helps you unlock the intelligence you already own and keep your information inside your organization. Use it to improve your agents, environments, and models.

Keep your data, workflows, agents, environments, and models. Use your data to make models that you own and control.

Control your own AI stack, without vendor lock.

One word can make all the difference.

In one contract-review test, five models rejected the same change. One accepted it. The model that accepted it read the whole agreement, noticed that the instruction said “should” rather than “must,” weighed that against the request to close a flagship client quickly, and found protection elsewhere in the contract.

The other models followed one instruction and missed the rest of the work. Their answers looked reasonable. They were still wrong for the facts in front of them.

That is the legal judgment we want to measure and train. An unusual answer can be the best answer in the set. The record tells us whether it was good judgment, a missing fact, or a model failure.

Train the model when the model is the problem.

Current models often fail because they did not get the right context, lost an instruction, used the wrong tool, or were evaluated against a weak test. We fix those parts first. When the same failure survives, we train the model.

01Work

The documents, instructions, playbooks, decisions, corrections, and outcomes your team already produces.

02Agent

The software that reads the request, builds the context, uses the right tools, and returns a first pass a person can review.

03Environment

The task, files, tools, and rules needed to run the work again, change the facts, and see what breaks.

04Model

Frontier, open, local, or post-trained. Give each model a defined job and keep the option to replace it.