Recommendation presence
Measure whether your brand appears in high-intent AI answers.
London · Market-by-market evidence
AI visibility software for businesses targeting London
Ninar is remote AI visibility software for London agencies and financial or professional-services teams. It compares city-qualified and UK-wide buyer questions while retaining answers and citations by engine.
Why this market
Separate London buyer discovery from UK-wide visibility
London is an important market for financial services, agencies, SaaS, legal, and professional-services firms.
Ninar evaluates whether a brand appears when local buyers ask AI engines for recommendations, comparisons, pricing guidance, and category leaders.
London 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
Agencies first, Financial and professional-services teams second
Use case 01
London and UK comparison
Run equivalent London-qualified and UK-wide prompt groups, then compare mentions, competitors, answer language, and cited sources without treating them as the same market.
Use case 02
Inspectable agency reporting
Provide clients with the question, engine, date, response, citations, and reviewer interpretation rather than a score alone.
Inspectable example
A transparent London scan specification
Demonstration specification — not a customer result
- Primary prompt
- Which carbon accounting platforms help London companies measure their emissions?
- Comparison prompt
- Which carbon accounting platforms help UK companies measure their emissions?
- 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 London 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 London
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 London 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.
Engagement in progress
CarbonMeter.ai is measuring London and UK-wide visibility separately
CarbonMeter.ai, a carbon accounting platform serving UK companies, began working with Ninar in September 2026. The engagement runs matched London-qualified and UK-wide prompt groups so the two markets stay separate. No visibility outcome is claimed yet, because measurement has only just started.
- The engagement began in September 2026 and is ongoing.
- London-qualified and UK-wide prompts are stored as separate records and are not averaged together.
- No result, ranking change, or improvement is claimed for this engagement at this time.
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 London. City-level results are point-in-time observations and can vary by prompt, engine, user context, and date.
Questions
Using Ninar for London visibility
Does Ninar AI have an office in London?
No. Ninar AI is headquartered in Durham, North Carolina and provides its software remotely to customers targeting London.
Who is this London workflow designed for?
The primary audience is agencies, with financial and professional-services teams as a secondary use case. Other teams can use the same measurement workflow, but this page does not claim universal market fit.
Does a London prompt represent a user physically located there?
No. A prompt containing London 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 London from scan to change?
Save the prompt "Which carbon accounting platforms help London companies measure their emissions?" 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 London evidence kept separate from national results?
A London 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 London figure on this page can be traced back to a single dated run.