Xsight Labs appeared in zero of the 27 queries run across OpenAI and Perplexity, confirming it has no current presence in AI-generated responses across any stage of its buyers' decision journey. Broadcom recorded 27 total mentions, Cisco 22, NVIDIA 20, and Marvell 17 across the full query set, establishing a dominant competitive field that Xsight Labs has not yet entered. However, multiple high-relevance queries — particularly around vendor lock-in escape, power efficiency at AI scale, and open programmable ASIC validation — are either fully uncontested or occupied only by weakly positioned incumbents, giving Xsight Labs a clear path to establish cited presence in the spaces its buyers are actively searching.
| Stage | Perplexity | ChatGPT (GPT-4o) | Google AIO |
|---|---|---|---|
| Problem (6) | 0% | 0% | 0% |
| Evaluation (6) | 0% | 0% | 0% |
The composite blends five scoring inputs across participating engines, with Google AIO contributing asymmetrically (coverage and high-intent coverage only). Useful as stakeholder shorthand; the per-engine view above is the diagnostic layer. Full input breakdown and the complete 5-stage × engine matrix appear in the Score Composition section. Entity Clarity is reported in the Entity Health section.
The two headline numbers are arithmetic, not opinion. Each reflects the inputs below in a fixed, disclosed formula.
The aggregate is pulled down by third-party reinforcement (5/10). This is where content investment would move Entity Clarity up first.
| Stage | Perplexity | ChatGPT (GPT-4o) | Google AIO |
|---|---|---|---|
| Category (5) | 0% | 0% | 0% |
| Problem (6) | 0% | 0% | 0% |
| Evaluation (6) | 0% | 0% | 0% |
| Comparison (5) | 0% | 0% | 0% |
| Validation (5) | 0% | 0% | 0% |
Google AIO percentages use an asymmetric denominator — only queries where an AI Overview triggered count, per the Phase 3 asymmetric-promotion rule. Perplexity and ChatGPT use the full query count in each stage.
Where the brand is read — stage by stage, engine by engine. Select any cell to open the evidence behind it. Scored and asymmetric engines drive coverage; observed engines are shown separately and are not scored.
What’s driving that score, and what it means for where this brand competes in AI-powered search.
Xsight Labs is effectively invisible to AI systems — not partially visible, not under-cited, but absent from every single query across all three engines at every stage of the buying journey.
Buyer queries across five stages of the search pipeline: category, problem, evaluation, comparison, and validation.
● Cited ● Not cited
41 brands tracked across your query set. Total mentions shown across 54 queries on OpenAI and Perplexity — your brand is highlighted.
Broadcom leads by total mentions. Xsight Labs has meaningful ground to close across both volume and pipeline coverage.
Every recommended page, ranked by priority — each one helps AI surface the brand when buyer queries do not name it. Start with the three below. Difficulty is retrieval difficulty, not production effort.
How do you avoid being locked into a single switching silicon vendor's roadmap and toolchain?
Low0 competitors cited, unowned across scored engines
This is a fully open space with zero competitors cited across Perplexity, ChatGPT, and Google AIO — a rare capture opportunity at the problem stage where Xsight Labs' open ISA and vendor-independence positioning maps directly to the buyer's core anxiety. All three scored engines show absence on this problem-stage query, making it the highest-priority gap to close first.
How do you future-proof data center network infrastructure without replacing switching silicon every generation?
Low0 competitors cited, unowned across scored engines
Another fully unowned open space across all three scored engines at the problem stage, directly aligned with Xsight Labs' protocol-agility and in-field programmability value proposition — the brand can own the definitive answer here with no incumbent to displace. The absence is confirmed across Perplexity, ChatGPT, and Google AIO, giving maximum retrieval upside.
What is the total cost of ownership for switching silicon platforms, including power, licensing, and integration?
Low1 competitor cited, unowned across scored engines
Only Scale Computing — an irrelevant incumbent — appears here, making this an effective open space where Xsight Labs' 40% better performance-per-watt claim and licensing-free open model give it a uniquely specific and credible TCO story no switching silicon vendor currently owns across Perplexity, ChatGPT, or Google AIO.
Current citations are dominated by software projects and standards bodies — not silicon vendors — leaving the hardware-layer answer entirely unclaimed; Xsight Labs can step into this problem-stage gap as the only switching ASIC vendor with a credible open-silicon answer across all three scored engines. This is a high-urgency target gap with fragmented-ownership among non-competing entity types.
Broadcom is the only silicon vendor cited here and only neutrally, while Xsight Labs is absent across Perplexity, ChatGPT, and Google AIO — a displaceable problem-stage position where power efficiency per dollar at AI-fabric scale is Xsight Labs' strongest differentiator and the cited incumbents have not locked down the space.
This high-urgency comparison query is cited by Cisco, Juniper, and Broadcom — all with neutral sentiment — but no open-silicon ASIC vendor has established authority here; Xsight Labs can own the open-programmable side of this comparison across all three scored engines with concrete capability and performance evidence. The space shows fragmented-ownership with no dominant owner.
Broadcom, Cisco, Marvell, and NVIDIA are cited with neutral sentiment and no dominant owner has locked this evaluation query across the three scored engines; a listicle anchored to Xsight Labs' specific programmability capabilities — open ISA, P4 support, in-field protocol updates — gives buyers a concrete evaluation framework that displaces generic incumbents.
Only Cisco and Cisco Catalyst Center are cited — both from a software/NOS framing — leaving the silicon-level answer to in-field protocol deployment entirely unowned; Xsight Labs' open ISA is purpose-built for this use case and can become the definitive cited source across Perplexity, ChatGPT, and Google AIO where this high-urgency gap remains open.
Broadcom, Cisco Silicon One, and NVIDIA Spectrum are cited neutrally but none owns this evaluation-stage migration query across Perplexity, ChatGPT, or Google AIO; Xsight Labs can provide the only vendor-authored migration guide that names real integration steps, toolchain compatibility, and SONiC support — directly addressing the buyer's switching cost anxiety with firsthand expertise.
Seven competitors are cited with fragmented-ownership and neutral sentiment — no vendor has established authoritative cited presence on this validation query across Perplexity, ChatGPT, or Google AIO; Xsight Labs can anchor the answer with production-grade performance data and its 40% performance-per-watt claim to become the definitive cited source for this high-urgency maturity question.
Broadcom, Cisco, NVIDIA, and four others appear with fragmented-ownership across all three scored engines on this high-urgency category query; Xsight Labs' 40% better performance-per-watt claim is its most differentiating metric and this is the highest-intent category query where that proof point directly answers the buyer's selection criterion.
Eleven competitors are cited with fragmented-ownership and neutral sentiment across Perplexity, ChatGPT, and Google AIO; this is the highest-traffic comparison-stage query in the set and Xsight Labs must establish a cited position here to intercept buyers actively evaluating Broadcom displacement — its open programmability and power efficiency are directly relevant differentiators.
Intel, Barefoot Networks, and Broadcom are cited neutrally across all three scored engines with no dominant owner on this high-urgency validation query; Xsight Labs can anchor the definitive maturity answer with production deployment proof points, ecosystem references, and SONiC compatibility evidence that displaces legacy incumbents whose programmability narratives are weaker.
Eight competitors including Cisco, Marvell, and NVIDIA appear with fragmented-ownership and neutral sentiment across all three scored engines on this high-urgency category query; Xsight Labs' open-standards design is core positioning and this query is where ICP buyers begin vendor shortlisting — establishing citation here intercepts the category-entry stage of the buying journey.
Only Arista, Cisco, and Juniper are cited — all from an NOS or systems framing rather than silicon vendor perspective — leaving the ASIC-layer OEM transition narrative entirely unclaimed; Xsight Labs is one of the few silicon vendors positioned to answer this from first principles, and the OEM disaggregation segment is a core ICP buyer group with no current silicon brand serving this query.
These queries have no dominant competitor. Being first to publish well-matched content is the best path to capturing the citation.
Who is being cited instead of you, which spaces are already locked in, and where you can make a move.
Queries where a competitor holds consistent citations and where the evidence supports a different angle.
AI engines build their understanding of a brand from owned and third-party surfaces. Inconsistencies or gaps in those surfaces reduce how clearly — and how often — your brand gets cited.
Category clarity is strong and consistent across owned surfaces and LinkedIn, but Crunchbase drifts toward a 'machine learning chipset' framing and LinkedIn adds incongruous service tags (Cybersecurity, Custom Software Development) that dilute the network silicon category — warranting a 7 rather than 8. ICP is specific and consistently anchored to hyperscale/cloud/AI infrastructure buyers across owned surfaces and LinkedIn, with Crunchbase the outlier in foregrounding ML workloads over network operators, holding ICP at 7. Value proposition alignment is the weakest owned-to-third-party dimension: the differentiating 40% power-efficiency claim and open programmability narrative are absent on both Crunchbase and LinkedIn, reducing value_prop_clarity to 6. Third-party reinforcement is moderate — LinkedIn is substantive and largely aligned, Crunchbase carries a stale and category-drifted description, G2 returned no content, and no Schema.org structured data or Wikidata entry exist, all of which modestly reduce third-party reinforcement to 5.
Absent messaging, missing product pillars, and unpublished buyer-stage content — each item identifies a specific surface gap contributing to citation misses.
What AI says when buyer queries name your brand directly.
Brand Mentioned is reported separately from the AI Visibility Score and is never blended into it. The AI Visibility Score measures the Brand Not Mentioned condition — whether AI surfaces the brand when buyer queries do not name it. Brand presence is a sanity check, not a finding.
Diagnostic for queries that name Xsight Labs by name.
Brand mention returned in all Brand Mentioned checks. Sanity check — these queries name the brand by design.
Xsight Labs alternatives for hyperscale and cloud network infrastructure architects
Xsight Labs vs Broadcom for escaping vendor lock-in for infrastructure teams
is Xsight Labs good for hyperscale and cloud network infrastructure architects
Xsight Labs pricing reviews and customer evidence for hyperscale and cloud network infrastructure architects
Xsight Labs vs Nvidia (Mellanox) for hyperscale and cloud network infrastructure architects
“eyeo: A semiconductor alternative that may be relevant for high-performance infrastructure silicon decisions”
rationale: eyeo is known as an ad-filtering/AdBlock company, not a semiconductor or infrastructure silicon vendor. This appears to be a hallucinated or misattributed claim.
“Xsight Labs silicon delivers 40% better performance per watt”
rationale: Specific figure sourced from company self-description, not independent verification. Presented as fact without third-party validation, though attributed to company materials.
“X-series switch is a 12.8 Tb/s monolithic die at 180W versus competitors at 300–600W”
rationale: Specific technical and power figures sourced solely from a company video, not independently verified. Competitor power range is unattributed and unverified.
Queries where your brand isn’t appearing, ranked by urgency. High-urgency gaps are queries most relevant to your category and most worth closing. The recommended pages above are the most efficient path to closing them.
| Urgency | Query | Why It Matters |
|---|---|---|
| High | best data center switching silicon for AI infrastructure companies | This is the highest-intent category query mapping directly to Xsight Labs' product category, ICP, and stated use cases. Absence from this query means Xsight Labs does not appear when its most likely buyers begin evaluating switching silicon options. |
| High | top programmable switch ASICs for hyperscale cloud data centers | Programmable switch ASICs for hyperscale data centers is Xsight Labs' exact product and customer category. Absence here means Xsight Labs is invisible at the most direct expression of its market position. |
| High | open-standards switching silicon for AI and cloud workloads | Open standards and programmability are the two defining pillars of Xsight Labs' value proposition as stated on its Homepage and About Us page. Not appearing on this query is a direct failure of category representation. |
| High | Broadcom switching silicon vs alternatives for AI and cloud data centers | Broadcom is the dominant incumbent against which Xsight Labs most directly competes. Absence from this comparison query means Xsight Labs is not surfaced as an alternative even when buyers are actively seeking one. |
| High | how do data center architects break free from proprietary switching silicon vendor lock-in | Breaking free from proprietary switching silicon vendor lock-in is the first stated priority use case for Xsight Labs' ICP. Absence here means Xsight Labs does not appear when its buyers are articulating their exact pain point. |
| High | how do hyperscalers control switching silicon costs as east-west AI traffic scales | Reducing power and cost as AI traffic scales is the second stated priority use case. Xsight Labs' 40% performance-per-watt advantage is directly relevant to this cost-control problem at hyperscale. |
| High | how do network engineers deploy new protocols on existing switch hardware without a silicon refresh | Deploying new protocols on existing hardware without a silicon refresh is the third stated priority use case. This query is a verbatim expression of that use case and Xsight Labs is completely absent. |
| High | how to avoid being locked into a single switching silicon vendor roadmap and toolchain | Avoiding proprietary roadmap and toolchain lock-in is central to Xsight Labs' open ISA positioning. This query is uncontested by any competitor, making the gap both highly relevant and immediately capturable. |
| High | total cost of ownership for switching silicon platforms including power, licensing, and integration | Xsight Labs' power efficiency, open licensing model, and integration story directly address all three TCO dimensions named in this query. The query is uncontested, making absence here a missed capture of high-intent evaluation traffic. |
| High | is open merchant silicon good enough for high-performance AI data center switching workloads | Xsight Labs is a merchant silicon vendor whose entire product narrative argues that open merchant silicon is production-ready for high-performance AI workloads. This is precisely the validation question it should answer. |
| High | are programmable switch ASICs mature enough for production AI and cloud data center deployments | Xsight Labs' production-ready programmable switch ASICs are the direct answer to this validation query. Absence here means buyers seeking maturity confirmation cannot find Xsight Labs at the final stage of their evaluation. |
| High | high-performance power-efficient switch ASICs for data center architects | Data center architects are explicitly named in Xsight Labs' ICP, and high-performance, power-efficient switching silicon is its product category. This query is a near-verbatim match to its product and buyer. |
| High | data center switching silicon for networking OEMs building disaggregated platforms | Networking OEM platform and hardware decision-makers are a named segment of Xsight Labs' ICP. Disaggregated platform silicon is a core use case. Absence means Xsight Labs is invisible to this buyer segment at the category stage. |
| High | what programmability features should I look for in a switch ASIC for AI data center workloads | Programmability is Xsight Labs' primary technical differentiator. This evaluation query is precisely where buyers are forming criteria that Xsight Labs' open ISA is designed to meet. |
| High | how hard is it to migrate from Broadcom-based switching silicon to an alternative ASIC platform | Many of Xsight Labs' target buyers are currently on Broadcom-based platforms. This query captures the exact migration consideration moment where Xsight Labs should be positioning as the open alternative destination. |
| High | how to future-proof data center network infrastructure without replacing switching silicon every generation | Future-proofing through programmability without hardware refresh cycles is a core Xsight Labs value proposition and stated priority use case. This uncontested query is a direct expression of that benefit. |
| High | is Broadcom switching silicon worth it for hyperscalers trying to avoid vendor lock-in | This validation query targets hyperscalers — a primary ICP segment — questioning the value of the dominant incumbent. Xsight Labs should be present as the named alternative answer. |
| High | proprietary switching silicon vs open programmable ASICs for cloud infrastructure teams | This comparison query is the definitional axis on which Xsight Labs competes. Cloud infrastructure teams evaluating open programmable ASICs against proprietary silicon should be finding Xsight Labs here. |
| Medium | how does a third-party switch ASIC integrate with existing data center network management and orchestration stacks | Integration with existing orchestration stacks is a practical evaluation concern for Xsight Labs' ICP. Its open standards positioning directly addresses this question and should be surfaced here. |
| Medium | how to evaluate vendor support ecosystem and long-term roadmap credibility for data center switching silicon | Roadmap credibility and ecosystem support are evaluation criteria directly relevant to Xsight Labs' positioning as a focused, independent silicon vendor with a clear open architecture roadmap. |
| Medium | what operational changes happen when a networking OEM moves from a proprietary ASIC to open switching silicon | This evaluation query is directly relevant to Xsight Labs' OEM ICP segment and its open silicon transition narrative. It is uncontested and maps precisely to Xsight Labs' target buyer journey. |
| Medium | Marvell switch ASIC vs Broadcom for hyperscale data center infrastructure | When buyers are comparing the two dominant switching silicon incumbents, Xsight Labs should appear as a third-option alternative. Its absence leaves the market framed as a binary duopoly. |
| Medium | Cisco custom silicon vs merchant switching silicon for data center networking | Xsight Labs competes in the merchant silicon category that this query directly evaluates against Cisco's proprietary approach. It should be the named representative of the open merchant silicon side of this comparison. |
| Medium | Broadcom Tomahawk vs Marvell Teralynx for power-constrained AI data center switching | Xsight Labs' 40% performance-per-watt advantage is directly relevant to power-constrained AI switching comparisons. Absence here means buyers comparing Broadcom and Marvell on power efficiency never encounter Xsight Labs as an option. |
| Medium | is Marvell a reliable switching silicon vendor for AI infrastructure companies at scale | When buyers are validating Marvell as a silicon vendor for AI infrastructure, Xsight Labs should be surfaced as an alternative to evaluate. Its absence cedes this vendor-validation moment entirely to incumbents. |
| Medium | is Cisco custom silicon a practical choice for networking OEMs building disaggregated data center platforms | Networking OEMs are a named ICP segment for Xsight Labs. When OEMs are validating Cisco Silicon One for disaggregated platforms, Xsight Labs' open merchant ASIC should be present as the independence-preserving alternative. |
| Medium | is Broadcom a reliable switching silicon vendor for AI infrastructure companies at scale | Xsight Labs directly competes with Broadcom for AI infrastructure silicon decisions. Absence from Broadcom vendor-validation queries means buyers never encounter Xsight Labs when actively questioning incumbent reliance. |
Independent AI Search Grader scores across OpenAI, Perplexity, and Gemini, entered manually at assessment time.
The grader runs its own query set to measure brand presence, sentiment, and share of voice across the three engines. The queries and methodology are separate from this report.
For Xsight Labs, the grader’s average overall score (52/100) sits noticeably above Find Your Signal’s 1/10. The grader captures general brand presence across all query types; Find Your Signal breaks down coverage by query type, including problem, evaluation, comparison, and validation. A brand can score well on general presence while still missing on the specific queries buyers use when they’re deciding.
Use these for cross-engine tracking and to measure change over time.
| OpenAI | Perplexity | Gemini | |
|---|---|---|---|
| Overall Score | 46/100 | 56/100 | 55/100 |
| Brand Sentiment | 24/40 | 30/40 | 31/40 |
| Share of Voice | 1/10 | 1/10 | 0/10 |
Every number in this report has a disclosed computation path. The same data powers the score explainability surfaces above — this note states the rules the report follows.
Every scored-engine query is run 3 times per assessment. 43% of (query × engine) pairs were stable across all runs.
The query set is pinned at baseline v1, so movement between reports reflects real change — not query regeneration.
Small deltas between reports may be labelled within measurement range when they sit inside the noise floor implied by resampling.
Entity Clarity sub-scores are assessed 3 times per assessment and consolidated by per-dimension median. A sub-score is tagged stable when all runs agree or one run differs by a single point; larger spreads render as variable.
AI Visibility is computed across three scored engines — Perplexity, ChatGPT, and Google AIO. Perplexity and ChatGPT contribute to all five inputs. Google AIO contributes asymmetrically: it feeds coverage and high-intent coverage only, and is excluded from cross-engine consistency, target stability, sentiment, and citation provenance. Its URL-level citations are kept for audit only. Modifier rules (late-funnel penalty, sentiment caps) are disclosed beside the score when they apply.
Entity Clarity is an aggregate of four dimensions — category consistency, ICP clarity, value proposition, and third-party reinforcement. The weakest dimension pulls the aggregate down.
Third-party reinforcement is constrained to a deterministic corridor computed from external entity evidence (Wikidata, schema.org), third-party surface availability (G2, Crunchbase, LinkedIn), and citation provenance from M1. The audit model assigns the integer within this corridor based on content quality. The corridor never pulls a value; it only constrains.
ICP clarity is scored against structured evidence (role, seniority, team, industry, company size) extracted from each surface in a separate pre-pass. A conservative negative cap fires when extraction shows clearly absent ICP signal across all surfaces; no positive floor is applied.
Where available, Entity Clarity is corroborated against two deterministic external signals — schema.org structured-data on the homepage, and a Wikidata entity match confidence-tiered by name + domain.
External signals are a corroborating surface, not a score override. Shaky matches (weak name similarity, unconfirmed entries) are rendered muted and do not receive the same visual weight as strong signals.
Assessment locale: United States (US) / en. Applied at the API level to: Google AIO, OpenAI web search. Engines without an API-level locale parameter (Perplexity, OpenAI chat, Gemini) ran weakly scoped via query wording only.
Google AI Overviews and AI Mode are Search-grounded generative experiences. Find Your Signal treats Google AIO as an observed, Search-grounded signal. Because AI Overviews trigger selectively and their internal fan-out is not externally observable, Find Your Signal does not reconstruct Google’s query fan-out. It tests representative discovery angles and reports how Google’s AI surface responded to those tested queries at collection time.
Recommendations prioritize useful, distinctive,
crawlable source assets that satisfy real buyer or user needs. They do
not recommend llms.txt, AI-specific markup, content
chunking, AI-specific writing styles, or thin pages for every query
variation.
Scores, citation provenance, tiering, and external-evidence checks are deterministic. Executive summary, rationale copy, competitive framing, and Entity Clarity sub-scores are authored by Claude against structured prompts — then audited against the deterministic layer.