How it works

Start with what the evidence supports.

Across every intelligence product, AI Insights 716 begins with observable evidence, documents how conclusions are reached, and keeps facts, calculations, inference, and uncertainty distinct.

Discuss your intelligence question Explore our intelligence

Shared evidence framework

Clear labels make uncertainty useful.

The subject changes across intelligence products; the discipline does not. Material findings are classified so a reader can see the difference between source-backed fact and analytical judgment.

KNOWN: Documented directly by authoritative or traceable evidence.
CALCULATED: Derived mathematically from documented inputs.
INFERRED: Supported by evidence but not directly stated by a source.
UNKNOWN: Not reliably determinable from the evidence available.
COMPANY CONFIRMATION REQUIRED: Not responsibly established from public evidence alone.

Observation before assumption

Observe first. Explain second.

AI recommendation systems are complex, change over time, and do not provide businesses with a simple public ranking formula.

Because of that, we do not begin with a checklist of supposed ranking factors. We begin with observable output, without claiming access to internal model data.

01Which businesses appear
02Which businesses do not
03Where recommendations are consistent
04Where results vary
05Which competitors repeatedly surface

Buyer-intent questions

Test the decision, not the company name.

Testing begins with realistic commercial questions a prospective customer might ask an AI assistant. The objective is to understand whether a business appears when a buyer is asking for help choosing among companies—not whether it ranks for its own name.

Proprietary prompt sets, sampling protocols, and testing infrastructure remain private.

Representative question patterns
“Which companies should I consider for…?”
“Who are the best providers of…?”
“What are the best options for…?”

Establish the baseline

Measure before changing anything.

The first step is to document current visibility. A baseline turns later observations into measurable change.

Appearance: Whether the business appears or does not appear for each tested question.
Position: Where it appears among recommendations when the ordering is meaningful.
Repetition: Whether visibility repeats across relevant buyer questions.
Competition: Which other businesses are being surfaced in the same decision context.
Variation: How results differ across the tested set of questions.

Compare

Compare what AI surfaces with what it doesn't.

When competitors appear and the target business does not, we investigate observable differences. The presence of a difference does not prove causation; it identifies a candidate explanation that can be tested.

01Question-specific website evidence
02Product and service specificity
03Independent references
04Reviews and trust signals, where relevant
05Brand and entity clarity
06Public authority
07Supporting content and evidence

Form a testable hypothesis

Turn differences into questions we can test.

An observed difference becomes a hypothesis only when it can guide a focused intervention and another measurement.

01
Observation
Competitor A repeatedly appears while Company B does not.
02
Difference
Competitor A has stronger evidence relevant to the exact buyer question.
03
Hypothesis
Increasing comparable evidence for Company B may affect its visibility.
04
Test
Make a focused intervention and measure again.

This illustrates a reasoning pattern. It does not claim that this factor universally determines AI recommendations.

Controlled intervention

Change what can be measured.

Recommended changes should be narrow enough to evaluate. When practical, avoiding many unrelated changes at once makes it easier to understand what may have affected the result.

The principle is disciplined attribution, without disclosing proprietary intervention protocols.

Focused
Attributable where practical
Based on observed differences
Documented before implementation
Measurable afterward

Measure again

The result matters even when nothing changes.

All three broad outcomes provide information. A null result is particularly important because it prevents an attractive theory from being treated as fact.

Positive: Visibility improves after the intervention.
Negative: Visibility declines or the result becomes less favorable.
Null: No meaningful change is observed.

The learning loop

Treat visibility as measurement over time.

AI systems change. The work is an ongoing learning loop, not a one-time claim of permanent optimization.

01
Observe
→
02
Compare
→
03
Hypothesize
→
04
Test
→
05
Measure
→
06
Learn
↺

What we do not claim

Confidence through clear boundaries.

We do not claim

Access to proprietary AI ranking algorithms
Guaranteed placement in AI recommendations
A universal list of AI ranking factors
That correlation automatically proves causation
That every intervention will improve visibility

We do provide

Systematic observation
Documented comparison
Evidence-based hypotheses
Controlled testing where practical
Measurement of outcomes

Frequently asked questions

About the company and its process.

What is AI Insights 716?

AI Insights 716 is an evidence-based competitive intelligence company.

We analyze observable information to help organizations understand business questions where the answer may be distributed across multiple sources, affected by changing conditions, or difficult to evaluate from any single data point.

Our current intelligence work includes AI Visibility Intelligence and Regulatory Impact Intelligence.

Rather than presenting assumptions as facts, we preserve the evidence behind material findings and distinguish what is known from what is calculated, inferred, unknown, or requires company confirmation.

What do KNOWN, CALCULATED, INFERRED, UNKNOWN, and COMPANY CONFIRMATION REQUIRED mean?

These classifications describe the evidentiary status of important findings.

KNOWN: — Supported directly by sufficiently reliable evidence.
CALCULATED: — Derived from known inputs using a documented calculation or deterministic method.
INFERRED: — A conclusion reasonably supported by available evidence but not directly established as fact.
UNKNOWN: — The available evidence is insufficient to make a reliable determination.
COMPANY CONFIRMATION REQUIRED: — Information that cannot responsibly be established from external evidence alone and should be confirmed by the company.

The purpose of the framework is simple: a reader should be able to distinguish evidence from calculation, interpretation, and uncertainty.

Where does the information in your analyses and reports come from?

Sources depend on the question being analyzed.

AI Insights 716 may use authoritative government records, regulatory materials, procurement and contract data, company-published information, relevant independent sources, and other evidence appropriate to the analysis.

Sources are evaluated according to their relevance and reliability. Material conclusions should preserve sufficient provenance to show what evidence supports them.

When sources conflict, information is incomplete, or a conclusion cannot be established reliably, the analysis should say so rather than fill the gap with an unsupported answer.

Does AI Insights 716 guarantee AI rankings, SBA eligibility, contract awards, or business outcomes?

No.

AI Insights 716 provides evidence-based analysis, not guaranteed outcomes.

AI recommendation systems can change and their proprietary ranking or recommendation mechanisms are not fully observable.

SBA eligibility and federal contracting consequences depend on applicable rules, company circumstances, contracting decisions, and other factors. Proposed regulatory changes may also change before becoming final or may never take effect as proposed.

We therefore do not guarantee AI rankings or recommendations, SBA eligibility, contract awards, recompete outcomes, regulatory outcomes, or financial results.

Our objective is to provide better evidence for decisions—not certainty where certainty does not exist.

How is company information handled during an analysis?

AI Insights 716 is designed to begin with evidence that can be responsibly obtained and documented from appropriate external sources.

Some questions, however, cannot be answered reliably without information from the company itself. When that occurs, the finding should be identified as COMPANY CONFIRMATION REQUIRED rather than guessed.

Information supplied directly by a client should be used only as necessary to perform the agreed analysis and should not be presented as independently verified unless supporting evidence establishes it.

How do I request an analysis or report?

Start with the intelligence area that matches the question you are trying to answer.

For questions about how a company appears in AI-generated recommendations and consideration sets, begin with AI Visibility Intelligence.

For questions about how regulatory changes may affect a company's competitive position, begin with Regulatory Impact Intelligence.

From the relevant product page, you can review the analysis approach and request the appropriate company-specific analysis or report.

If you are unsure which analysis fits the question, contact AI Insights 716 at [email protected].

Begin with the decision

See what the evidence says about your business.

Start with the competitive question that matters.

Discuss your question Explore our intelligence