Harmonic: AI Analytics That Understands Your Business. Bring enterprise business rules to AI Chatbot seamlessly.
Kevin Lin, Technology Lead, Data Melody
One of the most dangerous AI answers is not an obviously broken answer.
It is a polished, plausible answer built on the wrong business definition.
A database can tell Chat AI that a column is named hcp_responded. It cannot tell the AI about the meeting where your team decided that response rate should count only completed activities.
Business Context does not give Chat AI more data. It gives the data the meaning your team already agreed on.
THE SAME QUESTION. LESS PROMPT ENGINEERING.
Business Context is not what makes Harmonic usable. Without it, Chat AI can still answer analytical questions — but the user has to carry the relevant business rules into the prompt. The difference is where those rules live.
Without Business Context — Harmonic can still answer, but the prompt must supply the metric definition, time scope, exclusions, counting rule, and expected output.
Once that definition is published as Business Context, it becomes shared team knowledge. The user no longer needs to restate the method in every chat; the prompt can focus on the business question.
For 2025, which three activity channels had the highest HCP response rate? Show the numerator, denominator, and rate.
With Business Context — the same analytical intent fits into one sentence, while Harmonic applies the shared definition and returns a comparable, explainable result.
The analytical capability exists in both cases. Business Context removes repeated prompt engineering, reduces variation between users, and turns a definition buried in individual prompts into a reviewable, versioned team standard.
WHAT CHANGED ACROSS THE BENCHMARK?
We evaluated representative Sales Data scenarios across achievement rate, response rate, market share, ROI, and revenue by HCO city.
The team, datasource, model, prompt wording, and Web Search setting stayed the same. Each scenario started in a fresh chat. The baseline used the team’s previous near-empty published context; the treatment used Business Context v2.
Answer success improved by 40 percentage points.
Semantic-rule accuracy improved by 15 percentage points.
Exact numeric validity improved by 20 percentage points.
Explicit method disclosure improved by 80 percentage points.
Median response time increased by 3.1 seconds. In this internal benchmark, better governance improved completion, semantic consistency, and explainability with a modest latency trade-off.
Figure 2 — Internal paired benchmark: completion, semantic checks, exact results, and method disclosure.
BUSINESS CONTEXT IS MORE THAN A LONGER PROMPT
Business Context creates a governed path from the language people use to the data Chat AI queries.
A team administrator writes familiar business definitions: what a KPI means, which period to use, what the unit is, and which records belong in the numerator and denominator.
During Check, Data Melody compiles and validates that language against accessible schema and verified Data Labels. During Publish, the checked result becomes a versioned team contract. Draft edits never silently change live answers.
Only the active published version reaches Chat AI.
Figure 3 — Business Context lifecycle: Author → Map → Check → Publish → Answer.
WITHOUT CONTEXT VS. WITH CONTEXT
Without Business Context
Terminology is inferred from column names and sample values. KPI formulas and join paths are generated case by case. Units may be guessed. The final number can look polished even when the definition is wrong.
With Business Context
Approved team language guides the formula, date range, aggregation grain, denominator, unit, and join logic. The answer becomes easier to reproduce and easier to challenge.
The goal is not to make AI sound more certain. The goal is to give it the same definitions that analysts, sales teams, and decision-makers already use.
START WITH THE QUESTIONS THAT CREATE ARGUMENTS
A team does not need to document its entire business on day one.
Start with one metric that regularly creates disagreement:
What exactly counts as a response?
Is ROI a percentage or a multiple?
Which date defines the reporting period?
Should unmatched records be excluded or disclosed?
Does “revenue” mean booked revenue, invoiced revenue, or client sales value?
Write that definition, run Check, and compare the same question before and after publishing.
Your schema describes how data is stored. Business Context describes how your organization thinks.