The terms Allyvate uses

Defined in the words of the official information about Allyvate and the blog. In conceptual order, from the layer itself to the agents built on it.

Insights layer

Also: Insight layer

The layer of the enterprise intelligence architecture that sits above the data platform and the semantic/context layer and continuously discovers what matters and explains why. The data platform stores and organises the signals; the semantic layer defines what they mean; the Insights Layer investigates what those signals are saying and determines what is worth noticing. Allyvate is the Insights Layer for the enterprise data stack.

Read more: Where the Insights Layer sits · Your Business Needs an Insights Layer to Tell You What Matters

Continuous insight

Also: Continuous discovery, Continuous growth intelligence

Intelligence that does not wait for someone to know what to look for. Instead of an executive, analyst or operator formulating the question, agents continuously generate hypotheses, investigate the company's data, test competing explanations, verify the evidence and rank the findings by business impact. The loop runs all the time: question, test, validate, rank, monitor.

Read more: Allyvate and Continuous Insight · How discovery works

Answer versus insight

Also: Insight

An answer retrieves or calculates a number: churn rose from 3.8% to 4.6% this quarter. An insight establishes that something matters, explains what appears to be driving it, puts it in business context and makes the implication visible: what changed, why it matters, what may explain it, what evidence supports the explanation, and what the business should watch next. The most valuable insight is rarely an anomaly; it is usually a business relationship.

Read more: Answer vs. insight

Context layer

The layer that grounds an AI agent in a company's facts: its metrics, entities, definitions and the trusted data behind them. It fixes the first kind of hallucination, an agent inventing columns and joins, and it tells the agent what happened. It does not tell the agent what is worth investigating, which hypothesis to test, or which signal could change a business decision.

Read more: The Context Layer Grounds the Agent. Nothing Checks the Question. · The context Allyvate uses

Semantic layer

A governed model of what enterprise data means: it tells a system that revenue, customer, order and retention are business concepts with defined relationships, so people and machines can query the data consistently. A semantic layer reduces data discovery and query friction. It does not eliminate the investigation loop, in which someone still has to decide what to ask, run the analysis, validate it and iterate. Allyvate is not a semantic layer; it uses that context to investigate what matters.

Read more: AI Doesn’t Just Need Context. It Needs Curiosity. · How Allyvate complements the existing data stack

Question layer

The layer that forms the questions nobody got to and checks them before anything acts on the answer. It generates hypotheses breadth-first, tests each one against real data, discards what fails, and passes only what survives, with a confidence assessment, to a person or to an agent that acts on it. It closes the second kind of hallucination: a plausible, well-formed, wrong conclusion drawn from correct data.

Read more: The Context Layer Grounds the Agent. Nothing Checks the Question. · About Allyvate: the question layer

Agentic analytics

Also: AI analyst

Analytics carried out by AI agents that run the analyst's loop end to end: deciding what to ask, formulating the analysis, running it against the data, validating the result and iterating on what it turns up. The difference from conversational analytics is that nobody has to ask; conversational analytics still makes the human the search engine, while agents form the questions themselves and only verified findings reach a person.

Read more: AI Doesn’t Just Need Context. It Needs Curiosity. · The Future of Enterprise Analytics Is AI That Knows What to Look For

Investigation

What an analyst does after a query returns a number: each answer creates another question, and the analyst decides which one to chase next. Knowing what the data means is not the same as knowing what to investigate. Allyvate does not stop at detecting an anomaly or deviation; it investigates potential explanations and their business context, and it does not simply accept the first correlation it finds.

Read more: The Future of Enterprise Analytics Is AI That Knows What to Look For · The five questions an Insights Layer answers

The four filters

Also: Verification, Relevance, evidence, rigor, impact

The checks every candidate finding passes through before it is surfaced. Relevance: is it about something that moves the business? Evidence: is there enough clean data to answer it honestly? Rigor: does it survive cohort, bias and significance checks? Impact: is the effect large and durable enough to act on? Findings that fail are discarded before reaching a decision-maker.

Read more: How Allyvate works

Confidence assessment

Also: Confidence score

The measure attached to a finding that has survived verification against a company's actual data. It can then be delivered to a person or to another agent for action, without a person reviewing every finding first. Every surfaced insight is inspectable: the evidence can include source metrics, populations, time periods, comparisons, queries and the assumptions used in the investigation.

Read more: How Allyvate works

Governed AI versus generic AI

Also: Generic AI, Governed AI

Generic AI looks at raw transactions and guesses. It tests only what it is asked, so it only finds what someone already suspected, and it waits to be asked. Governed AI is grounded in business context, tests hypotheses continuously against the data, and ranks findings by their measured impact, so its output is evidence rather than a plausible guess.

Read more: Generic AI only guesses

Insight agents

Also: Retention agent, Expansion agent, Efficiency agent

Focused applications of Allyvate's Continuous Insight capability, not separate products. The Retention Agent discovers the early signals of long-term customer value: the first actions and metrics that distinguish durable customers from one-time buyers. The Expansion Agent finds the patterns that precede account growth: product combinations and usage shifts that reliably precede larger relationships. The Efficiency Agent finds where spend stops producing revenue: segments and channels where costs rise without a matching incremental return.

Read more: Insight agents · Use cases

Model-agnostic

Allyvate treats insight generation as an enterprise architectural capability rather than something embedded inside a particular LLM, prompt or agent framework. Models, agents and interfaces change; the accumulated intelligence about what matters to a business should not have to be rebuilt every time the underlying AI technology does.

Read more: Model-agnostic by design