ChatGPT ranks MSAB #1 ten times.
Gemini ranks MSAB #1 four times.
MSAB appears in 35 of 60 AI responses (58%) across ChatGPT, Claude and Gemini for buyer-intent queries from law-enforcement, prosecution and homeland-security decision-makers. Cellebrite is cited 53 times, Magnet Forensics 41. The 45-point spread between ChatGPT (85%) and Gemini (40%) concentrates on the same three products every time: XAMN, UNIFY and RAMalyzer, named by one platform and absent from the other two.
The findings below come from Xtrusio, an AI visibility audit system built specifically for B2B buyer-intent testing. Every citation was verified by running 20 real prospect queries across three generative AI platforms.
Xtrusio measures how ChatGPT, Claude and Gemini surface mobile forensics vendors when investigators and DFU commanders research capability during discovery.
ChatGPT surfaces MSAB’s product architecture far more consistently than Claude and Gemini.
ChatGPT names MSAB #1 on ten of twenty buyer queries — including RAM capture, brute-force acceleration, frontline kiosks and fleet management. Gemini and Claude default to Cellebrite for the same questions and never surface XAMN, UNIFY or RAMalyzer. Every platform names MSAB somewhere in the answers — only ChatGPT names the individual products.
Platform Scorecard
MSAB citation rate across AI platforms — and where competitors sit
AI Visibility Leaderboard
Who owns the mobile-forensics conversation across ChatGPT, Claude and Gemini
AI Positioning Audit
20 buyer-intent queries — click any row to see the exact question asked
Each query was written from the perspective of a real decision-maker researching mobile forensics tools during discovery. The three personas span MSAB’s three biggest public-sector verticals — a prosecutor’s office running trafficking cases, a police digital forensics unit commander at a major-metro force, and a former HSI leader now shaping federal procurement.
| # | Query Topic | Cluster | ChatGPT | Claude | Gemini |
|---|---|---|---|---|---|
| 1 | Air-gapped extraction | USP — Should Win | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Our agency isn’t permitted to send device data or unlock requests to a vendor’s cloud service. Which mobile forensic extraction tools can run fully offline in an air-gapped lab?” | |||||
| 2 | Locked, encrypted Android | Shared Territory | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “What are the leading tools for extracting data from locked and encrypted Android phones in criminal investigations?” | |||||
| 3 | Passcode brute-force speed | Shared Territory | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “Password-protected handsets are stalling our cases. What’s realistically available in 2026 to speed up brute-forcing passcodes on seized mobile devices?” | |||||
| 4 | Per-device unlock economics | Competitor Strength | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Our budget only allows a handful of premium unlocks per year. Are there per-device unlock services available instead of committing to a full annual licence?” | |||||
| 5 | Mobile RAM capture | USP — Should Win | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “We keep losing volatile evidence when a seized phone gets powered down. Are there mobile forensic tools that can capture and analyse RAM from a live device?” | |||||
| 6 | Selective, proportionate extraction | USP — Should Win | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Our legislation requires us to take only the data relevant to the offence, not a full device image. Which mobile forensic tools support targeted, selective extraction to stay proportionate?” | |||||
| 7 | Fastest new-iOS support | Shared Territory | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “Which mobile forensic platforms support the newest iOS versions fastest after Apple pushes an update?” | |||||
| 8 | Off-brand Android coverage | Competitor Strength | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We seize a lot of cheap Chinese and off-brand Android handsets. Which mobile forensic tool has the widest device coverage for obscure manufacturers?” | |||||
| 9 | Filtering chat and media volume | Shared Territory | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “We’re drowning in chat and media data from a single handset. What analysis software helps investigators filter a mobile extraction down to what actually matters for the case?” | |||||
| 10 | Court-defensible reporting | Shared Territory | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Defence counsel keeps challenging our phone evidence on chain-of-custody grounds. Which mobile forensic tools produce court-defensible reports and tamper-evident evidence files?” | |||||
| 11 | Unified phone + laptop + cloud | Competitor Strength | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “We want one platform that covers phones, laptops and cloud accounts in a single case file rather than juggling separate tools. What are our options?” | |||||
| 12 | Shipped AI assistants | Competitor Strength | ✗ | ✗ | ✗ |
Exact question asked across all AI platforms: “Everyone is talking about AI assistants for investigations. Which digital forensics vendors have actually shipped AI that can answer questions about an extraction in plain language?” | |||||
| 13 | Frontline station extraction | USP — Should Win | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Our digital forensics lab has a nine-month backlog on phone examinations. What technology lets trained frontline officers run extractions at the station instead of shipping every device to the lab?” | |||||
| 14 | Non-specialist safe extraction | USP — Should Win | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We want officers who aren’t forensic specialists to be able to pull evidence from a phone safely. Is there equipment simple enough for that but still forensically sound?” | |||||
| 15 | Border checkpoint under 10 minutes | Shared Territory | ✓ | ✓ | ✗ |
Exact question asked across all AI platforms: “Border officers need to check a traveller’s phone in under ten minutes at the checkpoint. What mobile forensic equipment is designed for that kind of rapid on-the-spot examination?” | |||||
| 16 | On-the-spot victim device return | Shared Territory | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “Victims and witnesses refuse to hand over their phones because they won’t see them again for weeks. Is there a way to take evidence from a cooperating person’s phone on the spot and give it straight back?” | |||||
| 17 | Central management, 40 sites | USP — Should Win | ✓ | ✓ | ✓ |
Exact question asked across all AI platforms: “We’re rolling out phone extraction terminals across 40 police stations. How do forces centrally manage software updates, user permissions and usage reporting across that many distributed units?” | |||||
| 18 | Certification pathway | USP — Should Win | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “We’re building an in-house mobile forensics capability from scratch. Which vendors offer a structured certification pathway that takes an officer from beginner to court-credible examiner?” | |||||
| 19 | Simultaneous multi-examiner review | Shared Territory | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “Our examiners email extraction files back and forth and nobody knows which version is current. Is there software that lets multiple investigators work on the same mobile extraction at the same time?” | |||||
| 20 | eDiscovery integration | Competitor Strength | ✓ | ✗ | ✗ |
Exact question asked across all AI platforms: “Our corporate legal team needs mobile evidence to flow into our eDiscovery review platform. Which mobile forensic tools integrate cleanly with legal review workflows?” | |||||
| TOTAL — MSAB cited | 17/20 (85%) | 10/20 (50%) | 8/20 (40%) | ||
The 45-Point Divide
Seven queries where ChatGPT names MSAB by product — and Claude and Gemini reach for Cellebrite instead
ChatGPT and Gemini are looking at the same question set. ChatGPT surfaces MSAB on 17 of 20 buyer questions. Gemini surfaces MSAB on 8. Claude sits in between at 10. The pattern is not random — there is a specific set of seven questions where only ChatGPT knows that MSAB has a product for that job. On the same questions, Claude and Gemini default to Cellebrite or Magnet and never mention MSAB at all.
“We keep losing volatile evidence when a seized phone gets powered down. Are there mobile forensic tools that can capture and analyse RAM from a live device?”
“Our examiners email extraction files back and forth and nobody knows which version is current. Is there software that lets multiple investigators work on the same mobile extraction at the same time?”
“We’re building an in-house mobile forensics capability from scratch. Which vendors offer a structured certification pathway that takes an officer from beginner to court-credible examiner?”
ChatGPT names MSAB’s products at version-level detail — XRY Pro RAMalyzer, UNIFY Collaborate, BruteStorm Surge. Claude and Gemini name MSAB the company, but not those products. Closing this gap is likely to require stronger public, product-level content: material on XAMN, UNIFY, RAMalyzer, BruteStorm Surge and the certification pathway that Claude and Gemini could start naming the way ChatGPT already does.
AI Topic Authority Map
Where MSAB owns the topic — and where the conversation belongs to Cellebrite or Magnet
| Topic | AI Leader | MSAB Status |
|---|---|---|
| Air-gapped, on-prem operation | MSAB | UNANIMOUS #1 territory (3/3 cited) |
| Frontline kiosks & station-level extraction | MSAB | UNANIMOUS #1 (3/3 cited) |
| Central management of distributed units (XEC) | MSAB | UNANIMOUS (3/3 cited) |
| Off-brand / Chinese Android coverage | MSAB | UNANIMOUS (3/3 cited) |
| Selective, proportionate extraction | MSAB | UNANIMOUS (3/3 cited) |
| Court-defensible reporting & chain of custody | MSAB / Cellebrite | Shared unanimous (3/3 cited) |
| Victim/witness on-the-spot return | MSAB | 3/3 cited (Gemini fabricated “MSAB Raven”) |
| Locked, encrypted Android extraction | Cellebrite | 2 of 3 platforms |
| Border-checkpoint rapid triage | MSAB / ADF | 2 of 3 platforms (Gemini misses MSAB) |
| Passcode brute-force acceleration | Cellebrite / Magnet GrayKey | ChatGPT only (1/3) |
| Mobile RAM capture | MSAB (ChatGPT) / Belkasoft (Gemini) | ChatGPT only (1/3) |
| New-iOS turnaround speed | Magnet GrayKey | ChatGPT only (1/3) |
| Analysis & chat/media filtering | Magnet AXIOM / Cellebrite Inseyets | ChatGPT only (1/3) |
| Simultaneous multi-examiner collaboration | Magnet REVIEW / Cellebrite Guardian | ChatGPT only (1/3) |
| Structured certification pathway | Cellebrite / Magnet MCFE | ChatGPT only (1/3) |
| eDiscovery integration | Cellebrite / OpenText | ChatGPT only (1/3) |
| Per-device unlock services (CAS-style) | Cellebrite CAS | INVISIBLE (0/3) — capability gap |
| Unified phone + laptop + cloud platform | Magnet AXIOM / Belkasoft X | INVISIBLE (0/3) — capability gap |
| Shipped AI assistants for investigators | Cellebrite Genesis / Belkasoft BelkaGPT | INVISIBLE (0/3) — capability gap |
4 queries
4 queries
4 queries
4 queries
4 queries
▹ Frontline & Triage is the only MSAB product line every platform surfaces at strength. Analysis & Court Reporting is the layer competitors capture on two of three platforms — that’s XAMN, the entire Analyze half of the ecosystem, missing from Claude and Gemini.
Methodology
How we conducted this Xtrusio AEO/GEO Audit for MSAB
This research is based on Xtrusio’s proprietary AI visibility analysis framework. Every citation was captured from a live prompt; every #1 ranking reflects the order the platform actually named vendors.
Recommendations
How MSAB closes the ChatGPT → Claude → Gemini gap
- Ship a public technical brief on XRY Pro RAMalyzer with FCM/MAC-address artifact examples — the exact evidence type ChatGPT already knows about but Claude and Gemini don’t.
- Publish a BruteStorm Surge whitepaper with benchmarked passcode-recovery times against comparable stacks.
- Add a dedicated Selective Extraction page framed around proportionality and privacy compliance — the framing Gemini already rewards with a #1.
- Publish a comparison page — UNIFY Collaborate vs Cellebrite Guardian vs Magnet REVIEW — naming the concrete on-premises, air-gapped and immutable-evidence differentiators.
- Build a XAMN-centred content cluster for chat/media filtering, timeline visualisation and cross-device correlation — the analysis layer Claude and Gemini currently give to AXIOM.
- Republish the Specialist → Analyst → Professional pathway with named modules, prerequisites, court-testimony content and Train-the-Trainer scope — the structure that lets AI platforms surface it against Cellebrite CCO/CCPA and Magnet MCFE.
- Quarterly Xtrusio re-audits to track the ChatGPT → Claude → Gemini spread as content ships. Target: close the 45-point spread to under 20 points within four quarters.
Close the 45-Point Divide.
Let’s build the content architecture that gets XAMN, UNIFY and RAMalyzer into every AI answer.
This research report was generated using the Xtrusio Company Intelligence Module.
