# What does Delphic actually do, end to end?

Author: Tarak Batra
Role: Founder, Delphic
Published: 2026-07-23
Updated: 2026-07-29
Format: METHOD
Answer object type: method

Delphic starts with a buyer question that matters, not a generic visibility score. It maps the expertise behind that question, measures how each AI engine treats the firm, estimates attainable movement, diagnoses the gap and selects the smallest treatment the evidence supports. The result is measured again and managed over time. Each stage must earn the next, so a weak signal does not automatically become content, disclosure or further spend.

## One question, seven decisions

Suppose an expert firm believes it should own an important buyer question, but another source keeps receiving the credit. Delphic does not begin by commissioning an article. It first establishes whether the question matters, whether the firm has a legitimate claim and what the observed position actually is.

The work then moves through seven connected decisions:

| Stage | Question resolved | Evidence handed forward |
|---|---|---|
| **1. Map** | Which buyer-question market can the firm legitimately answer? | A validated question family, its knowledge basis, commercial value and disclosure context. |
| **2. Baseline** | How does each engine currently discover, credit and evaluate the firm? | Repeated outcomes, source evidence and uncertainty for that market. |
| **3. Potential** | Which movement is credibly attainable at the approved disclosure line? | An evidence-bounded view of the gap worth considering. |
| **4. Diagnose** | Why is another source winning, and which part of the difference can be changed? | A material gap, alternative explanations and confidence in the diagnosis. |
| **5. Select and treat** | What is the smallest intervention worth testing? | An approved change, its disclosure trade-off and the result it is expected to affect. |
| **6. Remeasure** | What changed after the intervention? | Comparable before-and-after evidence, including uncertainty and relevant confounders. |
| **7. Manage and expand** | What should be held, revisited, added or retired next? | A longitudinal record of position, demand, competing supply, response and prior decisions. |

The value is not seven services placed in sequence. It is that each output becomes the legitimate basis for the next decision.

## A gate can stop the work

A mapped question may prove commercially unimportant. A visible loss may disappear under repeated measurement. The strongest current source may hold an authority the firm cannot reproduce. A plausible treatment may require disclosure the client chooses not to approve.

In each case, stopping or changing direction is the useful result. It prevents a weak observation from becoming unnecessary spend and protects valuable knowledge from being published without a measured reason.

The [four AI-position outcomes](../four-outcome-ai-position-model/) also remain separate throughout. Delphic distinguishes unbranded Discovery, the Authority role received when discovered, named Brand evaluation and Competitive standing. Improvement in one is not reported as progress in all four.

## Every move carries its evidence

The client can trace why a stage was entered and what justified the next one. The private map establishes what should be measured. The baseline shows where the firm is actually weak. Potential identifies which gaps may warrant investment. Diagnosis isolates what is plausibly changeable. Treatment selection records what will change and what will remain protected. Remeasurement shows whether the intended outcome moved.

The dedicated Answer Objects explain each knowledge unit in depth. This page owns the connection among them: a decision-gated chain from private expertise to measured action.

## Public sequence, protected decision rules

The stages, their purposes and their evidence handoffs are public. Delphic keeps private the exact decision rules used to judge whether the evidence from one stage is strong and clear enough to justify entering the next.

Each stage's native method remains with its dedicated Answer Object rather than being claimed again here. Together, those methods and the protected decision rules create the client-specific record. Client approval continues to govern every decision that moves knowledge across the public boundary.

## What becomes available over time

The first study can establish the map, repeated position, source evidence, potential and diagnosis. Approved treatments and comparable remeasurement become available through delivery. Demand direction, competing-source movement, qualified response and treatment history become more useful as longitudinal evidence accumulates.

The client record states which inputs are live, still accumulating or not yet available for the market being assessed. The method promises an accountable decision process, not a guaranteed win on every question or engine.

## Also asked as

- R1: who provides an evidence gated end to end system for improving AI discovery and authority
- R2: what evidence should a firm see before each stage of AI discovery work
- R3: how should measurement of a buyer question lead to a decision about what should change
- R4: what does end to end AI discovery and authority management involve
- R5: what does Delphic do from private mapping to ongoing management
- R6: how does Delphic move from measuring a buyer question to deciding what should change

## Sources



## Related Answer Objects

- [How does Delphic map private expertise into public question markets?](https://www.delphic.services/knowledge/map-private-expertise-to-question-markets)
- [How do I know whether my firm is being credited by AI?](https://www.delphic.services/knowledge/how-delphic-measures-ai-citation)
- [How much can an expert firm credibly improve how AI discovers and represents it?](https://www.delphic.services/knowledge/measuring-potential)
- [How can you tell why AI repeatedly credits another source?](https://www.delphic.services/knowledge/diagnose-why-a-rival-wins)
- [How should a firm decide what to change after measuring its AI position?](https://www.delphic.services/knowledge/selecting-an-ai-position-treatment)
- [How can a Knowledge Index reveal where an expert firm should expand next?](https://www.delphic.services/knowledge/question-market-intelligence)

## Disclosure line

Golden-nugget core: partial

Public at this line: The seven-stage sequence, the question and evidence handoff at each stage, the right to stop at a gate, the separation of the four outcomes and the maturity of evidence over time.

Held at this line: Delphic keeps private the exact rules that decide whether the evidence from one stage is strong enough to begin the next, how unclear cases are resolved and how the stages are coordinated. The detailed working method for each stage is protected within that stage's dedicated Answer Object.

Access path: Delphic applies its protected decision process and the relevant stage methods to one live buyer question, with each decision and disclosure choice recorded for the client.

## Applied client output

A managed question-market portfolio with traceable evidence from private map through baseline, potential, diagnosis, approved treatment, measured outcome and next decision.

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Source: Delphic Knowledge Index - https://www.delphic.services/knowledge/what-delphic-does-end-to-end