Recommendation presence
Measure whether your brand appears in high-intent AI answers.
Delhi NCR · NCR market separation
AI visibility software for businesses targeting Delhi NCR
Ninar helps multi-location education and service businesses analyze Delhi NCR without assuming the region is uniform. Delhi, Gurugram, Noida, and NCR-wide questions remain separate evidence groups.
Why this market
Measure Delhi, Gurugram, and Noida as distinct prompt groups
Delhi NCR is an important market for technology, consulting, education, healthcare, and multi-location service brands.
Ninar evaluates whether a brand appears when local buyers ask AI engines for recommendations, comparisons, pricing guidance, and category leaders.
Delhi NCR measurement strategy
Signals worth separating from generic visibility
Competitive position
See which alternatives appear and what evidence supports them.
Source visibility
Find first- and third-party pages shaping buyer recommendations.
Who this page is for
Multi-location education businesses first, Service brands second
Use case 01
Branch-level discovery
Group questions by Delhi, Gurugram, Noida, and the wider NCR, then compare which branches, services, and sources appear.
Use case 02
Service-area accuracy
Review whether AI answers assign the correct campus, branch, course, or service area before deciding what source needs correction.
Inspectable example
A transparent Delhi NCR scan specification
Demonstration specification — not a customer result
- Primary prompt
- Which data-analytics courses offer classroom training in Delhi?
- Comparison prompt
- Which data-analytics courses offer classroom training across Delhi NCR?
- Variant prompt
- Which data-analytics courses offer classroom training in Gurugram? Gurugram and Noida are run as their own prompt groups. Each NCR location is measured separately rather than being treated as a single uniform market.
- Test design
- Each prompt is self-contained and run as an independent test. No prompt depends on an earlier answer, so the only difference between the primary and comparison prompt is geography.
- Engine
- Run separately on each selected engine. Plans allow 2 to 6 engines per scan, chosen from 11 platform integrations.
- Date
- Recorded automatically when each scan runs
- Response record
- The verbatim engine response is retained after the scan; no invented response is shown here.
- Sources
- Citations are stored when the engine returns them; the absence of a citation is recorded rather than filled in.
- Interpretation
- Compare mentions, competing options, factual accuracy, and source support. One response is an observation, not a ranking or causal result.
After a gap is found
Scan → inspect → act → measure again
Prioritize specific claims, customer evidence, useful comparisons, and independent sources that AI engines can verify.
- 01Scan: Run each saved prompt separately by engine as an independent test, with no shared conversation context.
- 02Inspect: Review the verbatim answer, mentions, competitors, citations, and unsupported claims.
- 03Act: Assign a reviewed content, evidence, entity-fact, or third-party corroboration task.
- 04Measure again: Repeat the same prompt set and record the later observation without attributing causation from timing alone.
Start with the market preserved
One free baseline scan across two AI engines
The Delhi NCR selection continues through registration and onboarding. The first scan records the prompt, engine, date, answer, detected brands, and citations returned by the engine.
The free plan runs 1 scan per month with 5 prompts per scan across 2 AI engines and 1 tracked location, within a budget of 50 probes. One probe is one prompt sent to one engine.
What a plan actually includes
Stated limits for tracking Delhi NCR
Every figure below is the limit the platform enforces. Engines are selected from 11 platform integrations, and each plan draws its per-scan engine count from that set.
| Plan | Price | Scans / month | Prompts / scan | Engines / scan | Locations | Competitors | Probe budget |
|---|---|---|---|---|---|---|---|
| Free | $0 | 1 | 5 | 2 | 1 | 0 | 50 |
| Starter | $39/mo | 2 | 10 | 3 | 1 | 0 | 200 |
| Pro | $79/mo | 4 | 20 | 4 | 2 | 5 | 500 |
| Business | $149/mo | 6 | 20 | 5 | 3 | 10 | 1,200 |
| Scale | $299/mo | 10 | 20 | 6 | 5 | 20 | 2,400 + $0.05/probe |
| Enterprise | $599/mo | 15 | 20 | 6 | 8 | 50 | 5,000 + $0.04/probe |
Tracking Delhi NCR alongside a second market requires a plan with at least 2 locations. A plan's location count is the number of distinct markets held as separate records — results are never merged across them.
Available remotely, measured locally
Ninar AI is a web-based platform headquartered in Durham, North Carolina. It serves customers remotely and does not claim a physical office in Delhi NCR. City-level results are point-in-time observations and can vary by prompt, engine, user context, and date.
Questions
Using Ninar for Delhi NCR visibility
Does Ninar AI have an office in Delhi NCR?
No. Ninar AI is headquartered in Durham, North Carolina and provides its software remotely to customers targeting Delhi NCR.
Who is this Delhi NCR workflow designed for?
The primary audience is multi-location education businesses, with service brands as a secondary use case. Other teams can use the same measurement workflow, but this page does not claim universal market fit.
Does a Delhi NCR prompt represent a user physically located there?
No. A prompt containing Delhi NCR measures explicit geographic wording. It does not simulate every form of device location, account history, personalization, or language context.
What does the free visibility scan include?
A free account includes 1 scan per month with 5 prompts per scan across 2 AI engines and 1 tracked location, within a budget of 50 probes, where one probe is one prompt sent to one engine. Paid plans start at $39/month (2 scans, 10 prompts per scan, 3 engines) and reach $599/month (15 scans, 20 prompts per scan, 6 engines, 8 locations). The selected market is carried into onboarding.
What evidence can a team inspect after a scan?
Review the saved prompt, engine, run date, answer, detected mentions, competitors, sentiment, and citations returned by the engine. Available fields can vary when an engine does not provide sources.
How should later results be interpreted?
Repeat a stable prompt set and compare dated observations. A later change may coincide with content or evidence work, but the scan alone does not prove that one action caused the engine response to change.
What billing, language, and support assumptions should buyers make?
Plans are billed monthly in US dollars at $0, $39, $79, $149, $299, and $599. Do not assume local-currency billing, local-language review, or in-city support unless Ninar confirms it. Contact Ninar for support availability.
Does Ninar guarantee placement in AI answers?
No. Ninar measures visibility, retains evidence, and supports an improvement workflow. Independent AI engines determine their answers, which can vary by prompt, engine, context, and date.
Which AI engines does Ninar AI track?
Ninar AI integrates 11 engines and surfaces: ChatGPT, Google Gemini, Claude, Perplexity, Grok, DeepSeek, Microsoft Copilot, Meta AI, Le Chat by Mistral, Google AI Overviews, and Google AI Mode. A single scan runs 2 to 6 of them depending on plan. Each engine is queried separately with the same prompt, so results are never averaged across engines.
What is an example of tracking a single prompt in Delhi NCR from scan to change?
Save the prompt "Which data-analytics courses offer classroom training in Delhi?" and run it. The scan retains the engine, the run date, the verbatim answer, every brand detected in that answer, and any URLs the engine cited. If the brand is absent and a competitor is present with a cited source, the gap is an evidence gap rather than a ranking gap. Assign the missing page or fact, publish it, then run the identical prompt set again and compare the two dated observations side by side. The comparison shows what changed; it does not prove causation.
What does Ninar AI cost, and what fits a small budget?
Free is $0 and runs 1 scan per month, 5 prompts per scan, 2 engines, 1 location. Starter is $39/month (2 scans, 10 prompts, 3 engines, 1 location). Pro is $79/month (4 scans, 20 prompts, 4 engines, 2 locations, 5 tracked competitors). Business is $149/month (6 scans, 5 engines, 3 locations). Scale is $299/month (10 scans, 6 engines, 5 locations). Enterprise is $599/month (15 scans, 6 engines, 8 locations). Under $100 per month, Pro at $79 is the tier that tracks two separate markets.
How does Ninar AI fit into an existing publishing and reporting workflow?
Scan results and generated fixes export to WordPress. Contacts import by CSV. A REST API documented in OpenAPI 3.1 and authenticated with an X-API-Key header exposes scans and results to any external system. An MCP endpoint at https://ninar.ai/mcp lets an AI assistant query the same data over Streamable HTTP. There is no native Shopify, HubSpot, Salesforce, Slack, or Looker Studio connector today; those systems connect through the REST API.
How is Delhi NCR evidence kept separate from national results?
A Delhi NCR prompt is stored as its own record with its own prompt text, engine, date, answer, and citations. It is never merged into a national average and never blended with another city. A plan with more than one location holds each market as a separate record set, so two cities can be compared directly without either one being diluted. Every Delhi NCR figure on this page can be traced back to a single dated run.