Trusted analytics.
For every use case.
With its unique non-hallucinating AI technology, Selfr provides a solution for every use case where you need to turn natural language questions into SQL queries against specific data schemas.
EMPOWERING NON-TECHIES
Self-served analytics
Problem:
Your centralized data team wastes too much time answering data-related questions from non-technical business people in your organization. This time would be better spent doing more strategic work like adding more data sources or improve your data models. You want to automate the process and empower these people to answer and important part of these questions themselves and only ping you for the advanced questions that require subtle analytical skills.
The status quo:
You are thinking about using an LLM-based analytics chatbot to answer these questions automatically. This would most likely require that you build or adapt a semantic layer – which can represent considerable work – and you will still experience a non-negligible amount of hallucinations (realistically 30-40%). Because your users are non-technical business people, they don't understand SQL and are not familiar with data schemas: this means they can never verify answers delivered by the system, they have to trust the answers against their intuition!
This can have dramatic consequences:
Your internal stakeholders will likely make business decisions based on fabricated and misleading answers
These business decisions can have financial, legal, and reputation consequences that will be pinned down on you, the data team
When users do intuitively detect that the answers do not make sense they will ping you and you will spend more time auditing and explaining than if you manually answered in the first place
You will face trust issues where a significant amount of users will stop using the service altogether when they realize it cannot be trusted
Our solution:
Selfr provides the same benefits as LLM-based analytics chatbots – answering data-related questions in natural language – but without the trust issue. Our technology ensures that the user is never deceived:
Self always displays how it understands the question, the same way a human analyst would require confirmation. If the user agrees, then the answer can be used as such, and if the users meant something else, she/he can disambiguate further via the chat.
For a given displayed understanding of a question, the generated answer is always 100% consistent. The user can never be deceived.
Selfr never makes up answers, and it clearly says when it can't answer
Selfr always answers the same way to the same question
Users end up with a system they can trust: if Selfr can answer, its answer is 100% correct. When data is missing or the question is too complex, etc. then Self pings the data team to handle the question.
The data team is freed from having to answer or second guess all the questions that Selfr can effectively answers, and can spend time on more valuable tasks.
EMPOWERING YOUR CUSTOMERS
Customer-facing analytics
Problem:
Your customers have access to some form of analytics in your product interface (typically dashboards with a few filters), but they always have questions and want to do more. An important part of your customer support questions is about accessing that data and answering data-related questions from customers.
The status quo:
You are thinking about adding AI-powered features to help your customers answer their own data-relation questions without using support resources. This means using an LLM-based system, and experiencing hallucinations on a regular basis. Users don't have the option to verify answers because many of them don't necessarily understand SQL, and you don't expose your data schemas anyways. Getting hallucinated answers and not being able to verify answers can have serious consequences:
Giving misleading or wrong answers can have disastrous legal consequences (especially in regulated industries)
These hallucinated answers will destroy the actual and perceived value of your entire product
Delivering misleading or wrong answers will have an impact on your brand and its reputation
Our solution:
With its unique non-hallucinating AI technology, Selfr guarantees that you will never deliver a misleading answer. If the model misunderstood the question, the user can always see it clearly and disambiguate or refine her question, but she's never deceived with a completely wrong answer delivered confidently.
With Selfr you can completely automate the question-answering process, and alleviate your customer support team.
AGENTS TALKING TO AGENTS
Agentic workflows
Problem:
You want to build a fully automated agentic workflow where agents talk to other agents and without humans in the loop. You need an agent to generate reliable SQL queries and answers (text + charts) from natural language questions.
The status quo:
LLM-based agents are prone to hallucinations and can never guarantee the factualness of their answers. Because you are building autonomous workflows, by definition you cannot have a human expert in the loop to verify LLM-generated answers for hallucinations, and so you can never fully trust your agentic workflow. In use cases where factualness is critical, LLM-based agents simply don't match the requirements and cannot be used.
Our solution:
Selfr has the only AI technology on the market to guarantee zero hallucinated answers, and is the only option to build truly autonomous workflows without human intervention.