Human expertise
Know-how emerges in everyday work, as people handle real cases and identify the right solution.
Continuous improvement for AI agents
Does your AI agent make the same mistake twice?
Steerloop captures user corrections and retrieves them when a similar case appears, helping your agent provide more reliable answers over time.
Works with existing AI agents and RAG systems.
01First request
User
Does the policy also cover accidental damage?
AI agent
Yes, it is always included in the standard plan.
OperatorCorrection: accidental damage requires the dedicated add-on.
02New similar case
User
Does the basic plan cover an accidental drop?
Similar corrected case retrieved
AI agent
The basic plan does not include it: the accidental-damage add-on is required.
Enterprise knowledge
Your most valuable know-how often goes undocumented
Every day, people use their experience to handle exceptions, correct answers and make decisions. This undocumented knowledge remains scattered across conversations, tickets and emails, making it difficult to find, share and transfer.
Documented knowledge
Steerloop captures corrections and connects them to the question, answer and context in which they emerged.
Shared knowledge asset
When a similar case appears, the relevant knowledge becomes available through the AI agent and can be reused across the organisation.
Experience no longer stays in the heads of a few people: it becomes documented, accessible enterprise memory that can be reused over time.
The problem
Every correction should improve the answers that follow
Users and operators constantly identify incomplete or incorrect answers. Yet those corrections often remain inside one conversation, ticket or email. As a result, the agent may repeat the same mistake.
Corrections get lost
Feedback stays isolated in the conversation where it was provided.
The same errors return
A case that was already solved can still produce another wrong answer.
Manual updates do not scale
Every error requires changes to prompts, rules or the knowledge base.
Steerloop turns existing corrections into useful context for future requests.
How it works
From a correction to a better answer
A simple loop that puts feedback where it can help: before the agent generates its next answer.
01
Capture
Steerloop captures the correction provided by a user or operator.
02
Organise
The correction is associated with the question, answer and relevant context.
03
Recognise
When a new request arrives, Steerloop identifies similar cases that were already corrected.
04
Improve
The relevant correction is added to the agent’s context before the answer is generated.
Benefits
An agent that puts feedback to work
Reduce repeated errors
Use past corrections when similar requests appear again.
Make user feedback valuable
Turn corrections into reusable knowledge.
Improve faster
Reduce the need for manual intervention after every individual error.
Work with what you already use
Add a new layer without replacing the agent interface, model or knowledge base.
Before and after
Feedback stops being a dead end
Steerloop helps reduce the chance that an already corrected error will return in a similar case.
Isolated feedback
Before Steerloop
- 1The agent generates a wrong answer
- 2A user or operator corrects it
- 3The correction stays in one conversation
- 4The same error can happen again
Reusable feedback
With Steerloop
- 1The correction is recorded
- 2A new similar case is recognised
- 3The relevant correction is retrieved
- 4The agent receives context to answer better
Use cases
Where Steerloop creates value
Customer care
Reuse operator corrections to improve answers to customers.
Enterprise assistants
Preserve guidance that emerges from everyday use of the knowledge base.
Technical support
Retrieve solutions and clarifications previously provided for similar issues.
Sales agents
Keep answers about products, procedures and terms more consistent.
Integration
Connect it to your agent. Do not replace it.
Steerloop fits into the agent’s generation flow and retrieves relevant corrections before the answer. The specific architecture is defined around the system already in production.
01User
02Existing agent
03Steerloop
04RAG + model
CorrectionRelevant context
Keep the interface, model and knowledge base you already use.
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by M-AI
Steerloop is an M-AI product
M-AI develops artificial intelligence solutions to automate and improve business processes. Steerloop is rooted in experience integrating AI agents and RAG systems into real enterprise workflows.
FAQ
Frequently asked questions
How does Steerloop turn undocumented know-how into enterprise knowledge?
Steerloop records corrections and guidance provided while the agent is being used, together with their context. When a similar case appears, it retrieves the relevant knowledge and makes it reusable.
Does Steerloop replace our AI agent?
No. It is a layer that works alongside the existing agent and provides relevant corrections when similar requests appear.
How are corrections collected?
Corrections can be collected directly in the chat or through a dashboard, where they are always confirmed. The dashboard also provides statistics on agent usage and corrections.
How is a similar case recognized?
The new request is compared with previously corrected cases to retrieve those that are relevant to the current context.
Does a correction modify the model?
No. The relevant correction is added to context before generation; it does not change the language model’s weights.
Does Steerloop work with RAG systems?
Yes. It is designed to work alongside existing AI agents and RAG systems without replacing the knowledge base.
How does it integrate with an existing agent?
It fits into the flow that prepares context for the answer. Technical details depend on the existing architecture and are defined during the assessment.
Where is data stored?
It depends on project requirements: data can be stored in the cloud or on premises.
Request a demo
Turn every correction into an opportunity to improve
Show us your agent and how you handle errors today. Together, we will assess how Steerloop could fit into your workflow.
- Start from the agent you already use
- Review how corrections are captured today
- Define a realistic integration path