Inserviss LabsWhat you’ll see
AI Fundraising Visibility Workflow
LP introductions, overnight work, reach, capital interest influenced — one live view.
Opportunities your agents found
Ranked by how fast you can win them
"Venture capital fund due diligence checklist for LPs"
Nobody cited yet · ~300 asks/month · you already publish this
"How to evaluate a venture capital fund's track record"
High value · a VC-commentary blog is cited instead · winnable in ~3 weeks
"[Fund] fee structure and GP commit"
Nobody cited yet · ~180 asks/month · you already disclose this
"Best venture capital funds by DPI 2026" — you slipped to #3
A peer fund refreshed its LP-facing track record page · your answer needs updating
"[Fund] co-investment rights" — cited, but marked outdated
An engine cites your prior fund’s terms · your current LPA needs reflecting
Confidential — mapped to your existing LP reporting and investor-relations programs
Your AI Visibility Score
One number. Every engine. Never blended.
0–100 for whether AI names your fund and gets track record, fees and terms right for LP due-diligence questions. Toggle three months forward.
Illustrative — a sample fund
0F
Named by AI · How often assistants name the fund at all, for LP due-diligence questions0
Vs. peer funds · Share of the answer next to the funds LPs already know0
Facts correct · Whether track record, fees and terms are right and current0
Listed where AI looks · Completeness on Crunchbase, PitchBook and NVCA/ILPA-adjacent sources0
Site AI can read · Whether LP-facing track record and terms pages are ungated and machine-readable0
Where the fund stands, engine by engine — never blended
ChatGPTgrounds on institutional LP-education sources (Cambridge Associates, NVCA, PitchBook)Not cited
Perplexityleans on LinkedIn and independent VC-commentary sitesNot cited
Gemininames a fund most often of the three, but scatters across a long tail of sourcesNot cited
Microsoft Copilotnot sampled in the published study — same measurement once it isNot cited
AI rarely names this fund for an LP due-diligence question — and when it does, fee and terms details are missing or dated. LPs are being routed to independent VC blogs and generic finance sites instead of the fund’s own LP-facing content.
Knowledge graph
The asset that compounds
Every month, AI verifies more facts about your fund — track record, fees, terms, governance, portfolio strategy — and they stay. A peer fund starting today is months behind.
Scout — verified factMaker — published answerAnchor — LP-network & analyst placement
A sample of the graph. Scout verifies the facts, Maker publishes the answers, Anchor gets the LP-network and analyst placements right — and it only ever grows. Illustrative figures.
Monthly report
One report. Every month.
Your score and its trend, citations by engine, where you stand against peer funds, what the agents did, and what needs your review.
Illustrative sample — not a real client’s data.
Monthly AI Visibility Report
Meridian Ventures
August 2026
Delivered 1 September 2026
Across 3 engines, 100 tracked LP due-diligence questions, 22 peer funds named.
AI Visibility Score ·
Meridian now gets named on roughly one in four LP due-diligence questions in its tracked set — a month ago it was almost never named. On fee-structure and due-diligence-checklist questions, an independent VC blog still answers in its place.
Key numbers
26%
Overall citation rate
▲ +14pts
5th
Share of voice, 22 named peer funds
▲ from 11th
92%
Track-record accuracy score
▲ +11pts
18
New knowledge-graph entities
▲ 112 total
Citation rate by engine — never blended
Gemini34%
ChatGPT26%
Perplexity15%
Citation share — you vs. named competitors
"Venture capital fund due diligence checklist for LPs"
"[Fund] fee structure and GP commit"
"Best venture capital funds by DPI"
Flagged for your review
FixPerplexity is citing a LinkedIn post instead of the fund’s current track record page — sent to Maker.
Gap"venture capital fund LPAC role and responsibilities" has no ungated answer page — 41 LP-education queries a month, never cited.
What your agents did in August
Scout re-sampled 100 LP due-diligence questions across ChatGPT, Gemini and Perplexity
Maker published an ungated fund fee-and-terms page with full schema
Scout flagged the Due Diligence & Red Flags cluster as still nearly unclaimed
Anchor submitted an updated Crunchbase fund profile — pending review
While the team was offline for the weekend
Scout flagged ChatGPT citing an outdated fund size figure — escalated to Maker
Maker ungated the track record page from behind a data-room login — re-cited on ChatGPT within 5 days
Anchor logged a completed LP introduction into the outreach history
Knowledge graph — your persistent asset
112
Entities tracked (+18)
460
Verified connections (+96)
92%
Average confidence score
The graph is yours and it compounds. Every month it holds more verified facts about the fund — track record, fees, terms, governance, portfolio strategy — so answers get harder to get wrong and harder for a peer fund to replicate. Stop, and it stops growing; stay, and each month starts further ahead than the last.
Focus for September
Close the due-diligence-checklist content gap — 41 LP-education queries a month, unanswered
Push citation share on "best venture capital funds by DPI" past the current peer-fund lead
Complete the PitchBook profile refresh and the next LP introduction call
FAQ
The detail, if you want it
When an LP asks AI to evaluate a fund's track record, check its fee structure, or run a due-diligence checklist, most funds simply aren't named — our own directional study of 100 real LP due-diligence questions found 64% got no fund named on any engine at all. Inserviss runs that same measurement against your fund specifically, then works both sides: Maker builds and ungates the LP-facing track record, fee and terms pages AI can actually cite, and Anchor gets your Crunchbase, PitchBook and Wikidata fund profile complete and current. Structural work, not a one-off press release.
Three ways, visible in our own published data. The silent cluster: LPs ask about due diligence and red flags, and on the best-performing engine only 1 of 17 such questions names any fund at all — practically the whole category is unclaimed. The blog referral: instead of your own track record page, Perplexity cites LinkedIn or an independent VC-commentary blog. The invisible emerging manager: with no long history to fall back on, a first-time fund has the most to lose from simply not existing in an LP's AI-assisted research — and the most to gain from being the first well-structured answer in an empty category.
They diverge hard, and we never blend them into one score. ChatGPT grounds on institutional LP-education sources — Cambridge Associates, the NVCA, CB Insights, PitchBook. Perplexity leans on LinkedIn (more citations than any other engine’s top domain) plus a cluster of independent VC-commentary sites — social and opinion content, not institutional sources. Gemini names a fund most often of the three (34 of 100 answers) but scatters across a long tail of small newsletters and blogs, no single domain cited more than twice. A single-engine approach structurally can’t cover that spread.
One system, three jobs. Scout samples the LP due-diligence questions that matter for your fund across ChatGPT, Gemini and Perplexity, tracking exactly which are unclaimed. Maker builds and ungates the pages an LP’s research actually needs — track record with real DPI/TVPI, fee and terms disclosure, due-diligence-ready governance detail — structured the way engines already reward. Anchor works the off-domain layer: Crunchbase, PitchBook and Wikidata fund-profile completeness, and genuine presence where LPs and allocators actually look.
No — it’s complementary. The work is making the facts your IR team already discloses to LPs directly — track record, fees, terms, governance — structured and visible where an LP’s own AI research actually happens, not a substitute for the relationship your team and placement agent build.
Most of those tools optimize one engine with one tactic. ChatGPT’s institutional-source grounding, Perplexity’s social/community lean, and Gemini’s long-tail scatter are three different citation logics on the same LP question — a single-engine, single-tactic tool structurally can’t cover all three.
That’s the highest-leverage case, not a dead end. Scout flags a cluster like this — 1 of 17 questions answered on the best engine in our own published measurement — as open ground, and Maker/Anchor prioritize establishing the first well-structured, well-cited answer before a peer fund locks up the category.
It’s a thesis about where AI-mediated discovery is headed, not a shipped product — see the full explanation at /ai-service-infrastructure-os. schema.org and JSON-LD tell a search engine what a page is about; they were never built to carry what an autonomous agent actually needs to act on an LP’s behalf — verified track record, current fee terms, real fund status, and where each claim came from. Applied to venture capital, a fund’s Service Object would be the same structured, confidence-tiered record our own LP due-diligence study measures the absence of today: track record (measured vs. self-reported vs. inferred), fee terms, GP commit, key-person provisions — described the same way for every fund, in a form an LP’s own AI agent could query directly instead of piecing together from a pitch deck and a handful of scattered mentions. What’s live today is Beacon: measuring and fixing whether a fund is named and correctly represented when an LP asks AI right now. The Service Object layer is the direction that work is headed, not a claim about what exists.
No date, and nobody should claim one. The honest sequencing, same as the wider AI Service Infrastructure OS thesis: get a fund’s facts right and visible first (what Beacon does today), get those facts formally structured as a Service Object next, and agent-to-agent interaction — an LP’s own AI agent pulling a fund’s structured due-diligence record directly — is the layer after that, once there’s a real counterparty actually asking for it. Given how much LP due diligence already runs through data rooms, quarterly PDFs and one-off calls, venture capital is a plausible early candidate for that later stage — but it isn’t built, and AI-to-AI rails are explicitly a later stage of this product’s build plan, not something this page is claiming exists.
See where your fund stands with LPs’ AI
One diagnostic, three engines, an LP-ready read of exactly where you stand.
Confidential — mapped to your existing LP reporting and investor-relations programs