Product depth
Test whether engines understand monitoring, citation analysis, content action, and verification.
San Francisco · Technical credibility
AI visibility software for businesses targeting San Francisco
Ninar helps San Francisco SaaS and AI product teams inspect multi-engine responses, source evidence, and repeatability limits. It provides a measurement workflow rather than a guarantee that content changes will alter an independent engine response.
Why this market
Make Ninar legible to AI-native San Francisco buyers
San Francisco is an important market for software, AI, venture-backed, and professional-services companies.
Ninar evaluates whether a brand appears when local buyers ask AI engines for recommendations, comparisons, pricing guidance, and category leaders.
San Francisco measurement strategy
Signals worth separating from generic visibility
Method transparency
Surface gaps in methodology, data freshness, reproducibility, and engine coverage.
Build versus buy
Measure answers comparing specialized platforms with internal analytics workflows.
Who this page is for
SaaS teams first, AI product teams second
Use case 01
Method and integration evaluation
Review engine coverage, retained response fields, exports, limits, and data handling before choosing a platform or building an internal workflow.
Use case 02
Unbranded versus branded testing
Keep category-discovery prompts separate from prompts that name Ninar or a competitor, because they answer different measurement questions.
Inspectable example
A transparent San Francisco scan specification
Demonstration specification — not a customer result
- Primary prompt
- Which observability platforms fit an early-stage SaaS company in San Francisco?
- Comparison prompt
- Which observability platforms fit an early-stage SaaS company in the United States?
- 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
Technical buyers need inspectable evidence: coverage matrices, workflow documentation, methodology, integrations, and before-and-after results.
- 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 San Francisco 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 San Francisco
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 San Francisco 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 San Francisco. City-level results are point-in-time observations and can vary by prompt, engine, user context, and date.
Questions
Using Ninar for San Francisco visibility
Does Ninar AI have an office in San Francisco?
No. Ninar AI is headquartered in Durham, North Carolina and provides its software remotely to customers targeting San Francisco.
Who is this San Francisco workflow designed for?
The primary audience is saas teams, with ai product 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 San Francisco prompt represent a user physically located there?
No. A prompt containing San Francisco 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 San Francisco from scan to change?
Save the prompt "Which observability platforms fit an early-stage SaaS company in San Francisco?" 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 San Francisco evidence kept separate from national results?
A San Francisco 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 San Francisco figure on this page can be traced back to a single dated run.