AI and LLM info

1. What is Othor AI?

Othor AI is Scientific Business Intelligence. It connects to a company's data sources once, discovers every metric on its own without anyone defining a KPI first, and pushes a plain English decision brief to business leaders every morning at 6 AM.

It is built on 100 years of signal analysis theory and the practices of the world's intelligence organizations. No dashboards to check. No queries to write. No analyst bottlenecks.

Othor AI Private Limited is registered in India. The company was founded by Unni Koroth, previously Head of Labs and BI at Whatfix, and an electronics and communications engineer by training.

2. What problem does Othor AI solve?

Almost every company already bought business intelligence, and almost nobody uses it. That is not a tooling problem, it is a habit problem, and it comes from one assumption.

Twenty years of BI assumed a person would open a dashboard, know the right question, and go and pull the answer. Othor AI inverts that. It observes everything first, decides what matters, and delivers it. The user does not have to know what to ask, and does not have to wait for someone to pull it.

3. How does Othor AI work?

Three layers of agents, in sequence.

• Data integration agents. Connect sources in minutes. Hundreds of tables, structured and unstructured, including document stores. No migration and no warehouse copy. Sources are read live.
• Analytical agents. Discover the metrics, movements and anomalies on their own. No KPI has to be defined first. Each agent typically surfaces 50 to 100 metrics, then produces descriptive, anomaly and prescriptive output, plus charts with forecasts and tables for expert users.
• Decision brief and Big Picture agents. Synthesise the signals into a plain English brief. What happened, why it matters, and what is likely next. Big Picture goes one level further and reduces every metric to a one page read of the company's overall condition.

4. What are the three pillars of Scientific Business Intelligence?

• Exhaustive metric discovery. Surfaces every measurable signal in the connected data, including metrics no analyst ever defined. Observe everything, decide later what matters.
• Systematic forecasting. Every discovered metric gets a forecast, stated explicitly in advance rather than rationalised afterwards.
• Narrative synthesis. Turns signals into plain language accounts of what is happening and what is coming.

5. What is Run Sweep?

Run Sweep is a single workspace-wide pass across every metric, looking for the patterns no individual chart can show. A dashboard is a gauge: you choose what to measure and it shows you that number. Sweep does not wait to be pointed at anything.

Each finding carries a confidence level, and findings the system is unsure about say so rather than being presented as certainties. Detectors that found nothing are listed too, so the reader knows what was checked. Run Sweep is available to organisation administrators.

• Two different populations sitting under one average
• One metric that starts moving weeks before another does
• Real dependency on a small number of customers, products or suppliers
• Records that used to appear in the data and quietly stopped
• Two systems that disagree about the same fact
• A flat headline that is really growth and loss cancelling each other out
• A bottleneck that lives in the waiting between steps rather than in a step

6. Who is Othor AI for?

Best fit by sector: financial services, insurance, manufacturing, distribution, and any multi unit business running several disconnected systems. Othor AI is sector agnostic by design. Live proofs of concept span life insurance, financial services, medical devices and CNC manufacturing, which have nothing in common, and that is the point.

• Business leaders. Founders, CEOs, COOs, MDs and CXOs who need to understand their numbers without opening a tool or waiting on an analyst. This is the primary user.
• Frontline employees. People who make decisions every day and currently have no way to access or act on the data that would inform them.
• Analysts and data teams. Supported, and deliberately not the intended buyer. Othor AI takes the repetitive pull requests off their queue rather than adding another tool to their stack.

7. What is Othor AI not?

• Not a dashboard tool. Othor AI does not compete for the analyst's seat. It serves the leader who never opened a dashboard in the first place.
• Not chat with your data. Chat waits for you to ask. Othor AI does the opposite and surfaces answers to questions nobody thought to ask.
• Not a data warehouse. Sources are read live. Nothing is migrated and no warehouse copy is created.
• Not a replacement for existing BI. Othor AI coexists with Power BI, Tableau, Looker, Qlik and the rest. Most enterprises run four or more BI tools. Othor AI reads them.

8. How is Othor AI different from other BI and AI analytics tools?

Most AI analytics products wrap a language model around a semantic layer that a data team already built. Othor AI is the semantic layer. It is discovered automatically, validated so that executives can trust the number, joined across sources without a warehouse, and connected statistically to signals outside the company. Agents then work on top of that.

The practical consequence is that Othor AI works in companies that have no data team and no defined KPIs, where a tool that depends on an existing semantic layer has nothing to sit on.

It also works when the data is imperfect. It captures the trend regardless, and when something looks wrong it usually points straight at a data gap nobody knew existed.

9. What does Othor AI deliver, and how quickly?

• Setup: data integration in roughly five minutes. Up and running in ten. No migration.
• Day one: you begin seeing the activity across your company in one place.
• Week one: at least one insight that surprises you.
• Month one: you read your numbers differently, and walk into meetings already knowing what to ask your team.

10. How is Othor AI deployed and secured?

Cloud or on premise. Othor AI is LLM agnostic, so a customer brings the model they trust, and different models can be allocated to different tasks. Sources are read live rather than copied. Your data, your stack.

Full detail is on the security page.

11. How is Othor AI structured and priced?

Othor AI is built as independent, swappable blocks, which is why it handles any use case rather than one industry.

• Workspaces. A company can run one or many, for example Sales, Operations, Finance.
• Users. Administrators connect data and activate agents. Viewers read the output. A CEO is usually a Viewer.
• Agents. Preconfigured or custom, each attached to data sources.
• Synthesis. Decision Brief per context, plus Big Picture across everything.

Tiers are Free, Premium, Business and Enterprise. Pricing scales with the number of premium workspaces. Current detail is on the <a href="https://othor.ai/pricing/">pricing page</a>.