The memory center for your AI agents and workflows.
Let your AI systems learn from your team's experience and expertise.
Corrigere — Purpose-built for businesses. Designed for humans.
Why Corrigere
Your AI workflow should remember what humans fix.
Production AI teams already have prompts, models, validators, queues, and business systems. Corrigere adds the missing feedback loop around them: a Review workflow that captures each Correction and makes that intelligence available to the next output.
Easy to integrate
Corrigere meets you where you are. It can be integrated with your systems and workflows with a flexible interface adaptable to your data and tasks.
Trust-enabling Review Loops
Correct high-stakes outputs. Corrigere's human-friendly UI enables efficient and fast review loops while producing highly reliable workflow results.
Built-in learning mechanism
Reuse what reviewers fix. Leverage operational knowledge and experience through Corrigere's Intelligence Assets that adapt and grow over time.
Correction lifecycle
From routed output to reusable intelligence.
Corrigere follows a simple loop that turns human review into durable system memory without taking over your model or the rest of your workflow.
01 · Route
Only what needs a human reaches one.
Send low-confidence, high-value, or policy-sensitive outputs to your human operators — everything else flows through untouched.
Incoming AI outputs
threshold 80%2 of 4 routed · the rest never interrupts a person
02 · Review
One field, the evidence, a decision.
Give operators a focused desk for the fields, evidence, and decision context they need to verify the output.
Payment due date
03 · Learn
The fix becomes structured memory.
Every Correction is captured as a durable, auditable record — the upcoming learning engine will turn them into structured Intelligence Assets: examples, rules, and confidence signals.
Captured as
Few-shot context for similar capital call notices.
Wire deadline (T-1) is not the payment due date.
Raises routing confidence on this field next time.
04 · Reuse
Next up: the prediction will ask first.
Prior corrections will feed future prompts, validators, and workflow decisions programmatically — the reuse surface ships with the learning engine.
// roadmap — before the next prediction
query learning engine
{ "task": "capital_call", "field": "due_date" } → 200 OK
{
"examples": 3,
"rules": ["wire deadline (T-1) ≠ payment due date"],
"confidence_boost": "+18%"
} 05 · Monitor
See where the model keeps slipping.
Track failure-mode hotspots and keep your team enabled to improve performance while holding operational control.
Failure-mode hotspots
Due-date field confidence
Example Review UX
Review workflows
One correction loop. Every review desk.
You just clicked through a handful of review desks. The same primitives compose a review surface for any use case — from finance operations to the AI systems themselves.
Composable review UI components
your data renders herePDF viewer
Documents · regions
Email viewer
Threads · messages
Field review
Extracted fields
Choice selector
Labels · categories
Item picker
Match to a list
Document upload
Reviewer inputs
Each desk composes the components its task needs. Corrigere renders the right surface for every field.
Business & operations
-
Invoice / AP coding
GL account assignment → AP clerk re-codes.
-
Insurance claims triage
Coverage decision → adjuster overrides.
-
Contract clause review
Clause classification → counsel corrects.
-
KYC / identity checks
Document match → compliance confirms.
AI & engineering
-
LLM / agent eval
Model answer graded → reviewer marks correct and why.
-
RAG answer grounding
Claimed citation → reviewer fixes the backing source.
-
Security finding triage
Scanner flags a finding → engineer confirms real vs. false positive.
-
Data-labeling QA
Model pre-label → annotator corrects the label.
The learning loop
Every correction is banked to make the next prediction better.
Corrections don't stay in the queue — they grow living intelligence assets. The upcoming learning engine will feed them back automatically, so the same fix gets applied next time, confidence climbs, and the review queue shrinks.
Auto-applied
The matching example will pre-fill the next similar item.
Smarter routing
Confidence will climb, so fewer items cross the review threshold.
Shrinking queue
Over time only genuinely novel cases will reach a human.
Intelligence assets
Grows with every correction
API / integration
A drop-in correction loop around your current architecture.
Keep your existing model, prompts, document processing, and application flow. Corrigere receives the outputs that need memory, routes the ones that need review, and returns reusable intelligence through API calls.
import requests co = requests.Session() co.headers["Authorization"] = "Bearer sk-live-…" # 1 · log the model's output + its confidence pred = co.post(f"{API}/predictions", json={ "project_id": pid, "input": notice, "output": model(notice), "confidence": 0.62, }).json() # 2 · capture the reviewer's fix — raw material for the upcoming learning engine co.post(f"{API}/corrections", json={ "prediction_id": pred["id"], "corrected_output": {"due_date": "2026-04-15"}, "reason": "Wire deadline (T-1) isn’t the payment due date", }) Open source
Inspect, run, and integrate the loop yourself.
Inspect the loop
Read the implementation, routes, models, and lifecycle decisions directly in GitHub.
View repository →
Run it yourself
Start the open-source API and product locally, seed sample data, and explore the Correction lifecycle.
Read Getting Started →
Integrate by API
Connect predictions, corrections, and review resolution without moving the rest of your stack.
Browse API docs →
Give your production AI a Correction lifecycle.
Start with a focused Review workflow, capture the corrections your team already makes, and reuse that intelligence in future outputs.