Learning from: Sep close reconciliations
v24 is learning
0 / 1,200 examples- evals
- waiting
- graded by
- qwen3.8-max + glm-5.3
- learns from
- each other
- lineage
- v23 → v24
- digest
- sha256:9f2c…e14a
AI that learns every day. And proves every step.
llit is the inference platform for regulated enterprises. Your models and agents improve continuously from their own work, while every answer and every update is signed, replayable and reversible
Several great models. Two house rules.
llit doesn't bet your work on one model. MoM runs leading models together, and holds them to two rules that make continuous learning safe.
No one grades their own homework.
The model that writes an answer never checks it. Independent models grade every answer and every update, so a mistake can never mark itself correct.
Everyone learns from each other.
When one model gets something right, the others learn from it. Every lesson is tested, signed and reversible, so the whole mixture improves without anyone drifting.
Like any good MoM: fair grading, shared lessons, and the same answer every time you ask.
Your AI gets better on the job. Provably.
Most AI is frozen the day it ships. On llit, models learn continuously from your real workloads, and nothing goes live until it passes your evaluations.
fail → exact rollback
Learns from production, not a labModels train on exactly what they served, so improvements match real behaviour.
Evals-gated by designEvery update must pass a sealed test set, under rules you set in advance, before it reaches a customer.
Versioned like codeEach update is a signed version with full lineage. See what changed, when, and why.
Instant, exact rollbackUndo any update and the model behaves precisely as before. Not close. Identical.
Private to each teamEach business unit's model learns its own work, and learning never leaves your environment.
Enterprise AI is stuck in pilot. Governance is why.
AI that keeps learning makes all three harder. Unless every step is deterministic.
Same question. Same answer. Every time.
Should invoice #20417 be paid?
Illustration of how answers drift when AI runs on busy shared servers.
Move AI from pilot to production in regulated work.
AI governance, built in.
Every answer carries a signed receipt: which AI, which version, what it read. Hand it to an auditor and they can re-run it themselves.
Model risk, managed.
Every model version, update and decision is traceable and replayable, ready for model risk, compliance and legal review.
Lower inference costs.
llit reads your files once and reuses them on every question, so repeat work gets faster and cheaper.
Agents you can trust to act.
Agentic workflows get a full, replayable record of every step, and improve from every run without drifting out of policy.
Three steps. No rip and replace.
Plug it in
Point your existing apps at llit like any AI service. Nothing else changes for your team.
It learns, under your rules
Your models improve from real work. You set the evaluations every update must pass.
Prove anything, anytime
Replay any answer or any model version months later and get the exact same result.
Built for work that has to hold up.
Ship AI-written code your auditors will sign off.
AI already writes much of your code. llit shows exactly what wrote each change, and lets you replay any step that broke something.
- A receipt behind every AI-written change
- Your code never leaves your environment
- Coding agents that learn your codebase and conventions
If an auditor might touch it, give it a receipt.
From an earnings release to an AML alert, every number and sentence your AI produces can be traced to its source and re-run to the same result.
- Disclosure-ready AI drafts
- Sign off once and trust every re-run
- Replay any flag when a regulator asks why
- Models that learn your house style and policies
Coming next: an AI version of you.
The same continuous deterministic learning, applied to a person: a digital twin that learns to write, look and sound like you, keeps a change only if it gets closer to the real you, and is yours to approve and undo.
Join the waitlist →The short answers.
What is MoM?
Mixture of Models. llit runs several leading models together under two rules: no model grades its own work, and every model learns from the others. Every mix and every lesson is versioned and signed, so it can be replayed or rolled back exactly.
Do we have to change our tools?
No. Your apps talk to llit the way they talk to any AI service today. The receipts come along automatically.
Where does our data go?
Nowhere. llit runs in your own cloud or data centre, so your files, code, prompts and learned improvements stay inside your walls.
How is continuous learning kept safe?
Every update is evaluated against a sealed test set before it ships, recorded as a signed version, and can be rolled back exactly. Nothing changes silently.
Which AI models does it run?
The latest DeepSeek, Qwen, GLM and MiMo families, flagship and flash versions alike: DeepSeek V4 Pro and V4.1 Flash, Qwen3.8 Max and Flash-Next, GLM-5.3 and 5.3 Flash, MiMo-V2.6 Pro and Flash. What your AI learns can be exported in a standard format and used elsewhere.
Isn't this just logging?
A log shows what the AI said. llit lets anyone re-run it and get the same answer, which is what an auditor actually needs.
How fast can we start?
Bring one real task. We'll run it on llit for two weeks and you'll see receipts on every answer.
Bring one task your auditors care about.
We'll run it on llit for two weeks. Watch it improve, and prove every step.