AI & Intelligence · Training Center
Frontier, local open-source — or your firm's own model, trained on your own work.
Included with EdgeLex · Firm-model training on your infrastructure
🎓 Lex Training Center
Manage feedback, training examples, and track Lex quality.
Total Feedback
8
Thumbs Up
3
Thumbs Down
5
With Corrections
5
Legacy feedback remains review evidence. Dataset growth must use selected manual examples or selected traces.
draftLineage
firm_model_v2Records
0Created
6/30/2026
✅ All clear — no examples need review.
The problem
Every legal AI vendor picks your model for you — even “model choice” usually means choosing which cloud. Owning the intelligence layer should include the option to own the model itself.
How a firm model actually gets better
Captured. Curated. Assembled. Measured. Traced.
Follow the path from a thumbs-down to a better firm model — every screen below is the real product.
01 · Captured
Feedback is signal, never training data.
Thumbs, corrections, rephrases — captured from real work and kept as review evidence. The banner says it plainly: feedback rows are not imported into the clean baseline. A correction becomes training data only after a human recreates it as a curated example.
👍 Evalsignal only
Q: “now, update your answer”
👍 Legalsignal only
Q: “explain from a lawyer's perspective how the court rules work”
👎 SFTsignal only
Q: “set a reminder to call the client in the morning”
👎 promoted SFT pre-baseline
Q: “Hi”
Lex Response
Hello! How can I assist you today?
User Correction
Always start the response with my name, which is “Maya”.
02 · Curated
Examples are edited by hand, with a quality bar.
Each candidate example carries its category, its dataset route — few-shot, eval, SFT — and a quality score. A rephrased question gets flagged NEEDS HUMAN EDIT and sits at 40% until someone fixes it. Import is a deliberate act, not a pipeline.
03 · Assembled
The dataset refuses to ship until it's honest.
Readiness fails closed: empty train split, missing eval holdout — export blocked, dry-run not ready. The holdout exists so the firm can measure improvement on examples the model never saw.
JSONL export: blocked · Training dry-run: not ready
Blockers
train_split_emptyeval_holdout_missing
Warnings
validation_split_empty
Splits
None
Sources
None
Training routes
None
Safety labels
None
04 · Measured
Quality is a trend line, not a vibe.
Category breakdown shows where the feedback concentrates; the weekly trend shows positive-signal share climbing — always measured against the eval holdout, never the training set.
Feedback volume by category · last 30 days
Summary & review34%
Court rules & deadlines22%
General assistance17%
Calendar & scheduling15%
Drafting12%
Positive-signal share of rated turns
▲ +13 pts since curation began — measured against the eval holdout, never the training set.
05 · Traced
Every run is evidence.
The edgeflow trace log records each run's intent, model, tokens, and latency — deterministic steps included. Completed traces can be selected and imported as training candidates, so the dataset grows from work that actually happened.
The difference
Nothing auto-trains. Ever.
The lazy version of “your model learns from your firm” is a pipeline that trains on whatever lawyers type. EdgeLex refuses that: feedback is signal, examples are curated by hand, safety and privacy review gate every record, the dataset blocks itself until the holdout is honest — and the weights never leave your deployment.
Where the model runs →Signal-only feedback
Thumbs and corrections inform curation; they never flow into training directly.
Fail-closed datasets
Missing holdout or empty split blocks export — readiness is enforced, not suggested.
Holdout honesty
Improvement is measured on examples the model never saw. Anything else is theater.
Capabilities
What it does
Any model, one governance layer
Frontier models with your own keys, local open-source models on your hardware, or your firm's own fine-tuned model — all governed by the same default-deny policy, capability binding, and mutation-safety routing.
Feedback from real work
Ratings, inline corrections, rephrase detection, topic abandons, and natural follow-ups are captured where lawyers actually work — a living record of where Lex helped and where it fell short.
Signal, not training data
Feedback is signal-only review evidence — nothing auto-trains. Dataset growth uses lawyer-recreated manual examples or explicitly selected, completed traces. A thumbs-down never silently rewires the model.
The Clean-Baseline Training Factory
Named datasets with full lineage — draft to promoted — built on record contracts and a quality bar every example must clear before import. You always know exactly what a model was trained on.
Curation gates that fail closed
Legal-safety review, privacy review, and tool-failure skips gate every candidate record, and eval holdouts are split from training data so the measurement stays honest.
Fine-tune on your infrastructure
Parameter-efficient (LoRA-class) supervised fine-tuning of local open-source models, run on the firm's own hardware. The dataset and the weights never leave your deployment.
Quality you can inspect
Analytics, run traces, and holdout evaluation show whether the model is actually getting better at your firm's work — not just different.
The persona holds
Training records enforce Lex's grounded, cite-your-sources, approval-gated behavior — so a firm-tuned model gets sharper at your practice without getting looser about the rules.
In your control
The dataset is your firm's curated work product; the weights are yours; training and serving run in your deployment. And a firm-tuned model is still just a model under EdgeLex governance — evidence gates and approval gates apply to it like any other.
Related
Works with
Lex AI & Agents
Lex isn't one application among many. It's the horizontal layer connecting matters, documents, email, messages, meetings, deadlines, and billing — grounded in your data, gated by your approvals.
AI Governance
A governance layer built into the platform: classify the task before the model runs, bind only allowed tools, require typed evidence, gate actions on approval, and audit every decision.
Clause Benchmark
Source-grounded clause observations, lawyer-approved standards, and benchmarks built from your own negotiation history — not a vendor's market data.
See EdgeLex on your own terms.
We'll walk through self-hosting, model control, and your firm's workflows.