Verbal Identity

Voice & Tone

Four pillars govern how Latency writes.

1 — Voice

The four pillars

Latency writes like an analyst who's done the homework. Every sentence carries weight. In a sector where everyone buries the point under three qualifications, Latency starts with the point.

Pillar 01

Sharp

Every sentence carries weight. The insight lands in the first clause, not buried in a paragraph. No filler, no hedge, no 'as mentioned earlier.'

In voice

"The 70% of European mid-market companies PitchBook doesn't index aren't a gap in their product. They're the gap in your pipeline."

Out of voice

"There are many exciting opportunities available in the European private markets space that Latency can help you identify and evaluate efficiently across multiple dimensions."

Pillar 02

Authoritative

Latency speaks from data, not from opinion. It makes claims it can source. Authority here is earned through specificity, not asserted through confidence.

In voice

"12M European companies with verified financials. Not estimates. Not scraped summaries. Filings, at source, normalised."

Out of voice

"We believe European private markets represent one of the most promising areas for investment in today's global economic landscape, and we're here to help you navigate that complexity."

Pillar 03

Precise

Latency uses the exact term the professional uses, not the term that sounds good in a pitch deck. Precision is a trust signal in this sector. Vague language raises a flag.

In voice

"Filter by revenue trajectory, ownership structure, and EBITDA margin. Cross-reference against 1,200 micro-market benchmarks. One query."

Out of voice

"Our powerful and intuitive search capabilities allow you to quickly discover companies that align with your specific investment parameters across our extensive database."

Pillar 04

Direct

Latency doesn't build to a point. It starts with the point. Subject, verb, object. If a competitor has a structural problem, the brand names it. Indirectness reads as weakness to this audience.

In voice

"Three platforms, one bill, zero overlap."

Out of voice

"By consolidating your various data infrastructure requirements into a single, integrated platform solution, Latency offers the potential for significant operational efficiency gains across your research and investment workflow."

2 — Voice Spectrum

Four axes, calibrated by context

The brand sits at a fixed position on each axis. Context shifts which pillar leads and how much of the scale is expressed — but never crosses the marked boundaries. These aren't sliders. They're positions.

Formal Casual

Sits at 3.5/10 toward casual. Contractions in most contexts; formal deliverables shift left. Never buttoned-up, never colloquial.

Serious Playful

Leans serious. Dry observation is permitted when it sharpens a point and earns its place. Playfulness that doesn't serve the claim is cut. Product docs and crisis comms: fully serious, no exceptions.

Authoritative Approachable

Centres toward authoritative. Authority is earned through specificity and data, not asserted through confidence. Not warm, not paternalistic — but legible and direct. Post-purchase shifts right; outreach stays left.

Minimal Expressive

Strongly minimal. Every word is load-bearing. Sentence rhythm varies for effect — a long clause followed by three words is intentional. Repetition, rhetorical build, and emotional modulation are not in this brand's register.

3 — Tone by context

Same voice, calibrated differently

The brand's core voice stays consistent. What changes by context is where on each spectrum axis the writing lands, and which pillar leads. Awareness leads with Sharp. Consideration leads with Authoritative. Post-purchase and Support lead with Precise. Crisis turns all personality off.

Awareness

Provocative and specific

The goal is to make the reader feel the cost of their current stack before Latency offers an alternative. Don't introduce the product. Introduce the problem, precisely enough that the right reader recognises themselves.

Do

  • Name the specific competitor the audience is using and getting wrong
  • Lead with a concrete number that reframes the problem
  • Write for the reader who is slightly embarrassed by their current setup

Don't

  • Open with what Latency does
  • Use aspirational language ('imagine a world where...')
  • Claim category leadership before establishing the problem

Example

"Your PitchBook subscription covers about 30% of the European companies worth knowing. The other 70% is where the interesting deals are."

Consideration

Analytical and specific

This is where Latency earns trust by showing, not claiming. Proof before ask. The reader is evaluating whether the data quality is real and whether the AI layer holds up under scrutiny.

Do

  • Lead with specific data points, not feature names
  • Reference exact workflows: target mapping, sector deep-dives, add-on sourcing
  • Let the product speak through concrete outputs

Don't

  • Use social proof as a substitute for product proof
  • Say 'AI-powered' without explaining what the AI actually does
  • Claim to 'replace' existing workflows without acknowledging transition friction

Example

"We mapped the Spanish industrial packaging sector, 340 companies, ownership structures, 3-year revenue trajectories, and benchmark margins, in the time it took to write this sentence. The query took 4 seconds."

Purchase

Direct and frictionless

The decision has effectively been made. Don't re-sell. The only job here is to confirm the logic of the choice already forming in the reader's head and remove anything that creates hesitation.

Do

  • Confirm what they're getting, specifically and completely
  • Use the same vocabulary they used during the sales process
  • Make the next step obvious and one click away

Don't

  • Introduce new product claims not part of the conversation
  • Get enthusiastic ('we're so excited to have you')
  • Ask them to do more than one thing

Example

"To confirm: full platform access, all +52M entities, the proprietary taxonomy, and your team onboarded in one session. We'll send the contract now and be ready to start Thursday."

Post-purchase & support

Functional, precise, gets out of the way

The user is trying to do a job. The brand's role is to help them do it faster, not to remind them they made a good choice. If something is wrong, say it's wrong and say when it'll be fixed.

Do

  • Use product terminology exactly as it appears in the UI
  • Give the direct answer first, context second
  • Reference the specific data source behind the answer

Don't

  • Say 'congratulations' or 'welcome aboard'
  • Use emotional language about the relationship
  • Over-apologise or use softening language

Post-purchase example

"Your taxonomy filters are pre-loaded for your sector. Start with a market scan (Markets tab, left nav) or go straight to a company deep-dive. If you're building a target list for a specific thesis, the Screener is the fastest path."

Support example

"The 18-month gap on this company's financials comes from the Spanish Registro Mercantil filing cycle, not a platform issue. The last confirmed filing is dated Q2 2023 and linked directly in the company profile. Our forecast model is in the sidebar."

Crisis

Factual, neutral, and immediate

All personality disappears. No dry wit, no competitive framing, no confidence. One voice, one channel, one message at a time. State facts, cause, and resolution timeline. Update on a fixed schedule even if there's nothing new to say.

Example

"On March 11, a processing error affected financial data for approximately 4,200 Spanish registry entries. The affected records have been identified and flagged in the platform. Corrected data will be available by March 13, 18:00 CET."


Channel overrides

Cold outreach — PE / VC / Growth

Lead with the intelligence gap, not the product. The frame is access, not efficiency. This audience pays for edge; the outreach should feel like it's offering something most people don't get to see. One specific market claim, one concrete proof point, one ask. Never more than 120 words.

Cold outreach — B2B sales

Velocity and completeness. The frame is time-to-insight and market coverage, not depth of analysis. Get to the value claim in the first sentence. Lead with a number (+52M companies, 1,200 micro-markets, 4 seconds) and follow with what it means for their pipeline. A commercial prospector doesn't need to feel exclusive; they need to feel fast. CTA is explicit, single, and near the top.

Product documentation

No marketing language. At all. Written for a user trying to accomplish a task, not a prospect evaluating a purchase. Use exact UI terminology. Write in imperative mood for instructions, indicative for explanations. Dense, functional, precise.

LinkedIn

Short, provocative, no preamble. Sentence fragments are fine. Starting with a number is fine. No corporate language, no 'we're thrilled to announce,' no thought leadership tone. The brand's character here is the person at the conference who says the thing everyone was thinking but nobody said.

4 — Messaging

Taglines & elevator pitch

Primary tagline — locked

Decoding private markets.

Outcome framing · slight provocation

The market knows. Now you do.

Declarative · geographic anchor

Europe's private economy, mapped.

Direct audience aspiration · no hollow verbs

Find the companies others miss.


Elevator pitch

Short — 1 sentence

Latency maps Europe's private economy at source level and applies an agentic AI layer that finds the companies worth finding before your competitors do.

Medium — 2–3 sentences

Most private market platforms tell you about companies you already know. Latency finds the ones you don't. We've normalised +52M European entities across every major registry, built a proprietary taxonomy across 1,200 micro-markets, and built an AI system that maps your investment thesis onto that data and surfaces what fits, continuously and without manual queries.

Long — full paragraph

The infrastructure serving European private markets was built for a different era and, largely, for a different continent. The result is a market where analysts routinely run three platforms in parallel, financial data arrives 18 months after the fact, and the most interesting companies stay effectively invisible. Latency was built to solve the architecture problem that makes this normal: starting from source-level European registries across all major jurisdictions, linking legal entities to the commercial brands they actually operate, and layering a genuinely deterministic AI intelligence system on top. The firms using Latency today arrive at targets before they're mapped by the consensus, run market deep-dives in minutes not weeks, and run their investment thesis as a systematic query rather than a senior analyst's institutional memory.


Key messages by audience

Alpha Seeker

Message

The firms with a genuine sourcing edge in European mid-market aren't working harder than their competitors. They're seeing a different dataset. Latency gives you access to the 70% of the market that doesn't surface through standard coverage, filtered through your thesis, not a generic taxonomy.

Proof: +52M normalised European entities, entity resolution across holding structures, 1,200 micro-market taxonomy. Early adopters have run sector deep-dives in under 10 minutes that previously required three weeks of analyst time.

Precision Prospector

Message

Your prospecting tools tell you who exists. Latency tells you which markets are actually moving, which companies within them fit your criteria exactly, and where the 80% of your total addressable market that isn't on Apollo or LinkedIn actually sits.

Proof: Real-time aggregation across +52M companies. Market scans that took days now run in seconds. B2B sales teams using Latency reach accounts that weren't in any existing database.

Press

Message

European private markets have a data infrastructure problem that every fund manager knows about and nobody has solved until now. Latency is the first platform built specifically for European market complexity, starting from source-level registries rather than retrofitting US-first architecture, with an AI layer that does actual analysis rather than pattern-matching on top of unreliable data.

Proof: Founded in 2023 by investors and technologists who ran the process themselves. +52M European companies normalised across all major registries. Proprietary taxonomy of 1,200+ micro-markets. Early commercial traction with PE funds and B2B sales teams within its first GTM quarter.

Investors & partners

Message

The private market data infrastructure market is a €8.2B TAM dominated by US-first platforms with structural coverage gaps in the geography that's growing fastest. Latency has solved the hardest part (source-level European data normalisation at scale) and is in GTM acceleration with validated PMF across two distinct use cases.

Proof: GTM launched Q4 2025. 96 leads contacted, 18 paying customers, 15 active trials within the first quarter. €1M ARR target on the path to €10M. Co-founders Raúl Díaz and Joaquin Duran with direct operating experience in the problem Latency solves.

5 — Vocabulary

Approved & forbidden

Precise language is a trust signal in this sector. Vague language raises a flag. The list below is not exhaustive; it reflects the pattern of what's in and out of voice.

Approved — use these

source-level thesis-driven entity resolution micro-market deal velocity proprietary taxonomy coverage gap IC memo origination blind spot financial filings corporate structure holding entity market dynamics agentic traceable target mapping deal sourcing proprietary dealflow sector thesis pipeline quality comparable companies

Forbidden — delete on sight

AI-powered data-driven leverage ecosystem seamless game-changing cutting-edge disruptive paradigm shift transformative scalable solution empower unlock comprehensive robust next-level end-to-end

Boilerplate copy

Short (~50 words); social bio, app store, about section

Latency decodes European private markets. We've normalised +52M companies across every major registry, built a proprietary taxonomy of 1,200 micro-markets, and layered an agentic AI system that maps your investment thesis onto the data and finds what fits. One platform. No Excel bridges. Source-level, traceable, European-first.

Medium (~100 words); website about page

European private markets have a structural data problem. The platforms built to navigate them were designed for US venture and retrofitted for European complexity, with all the coverage gaps, mismatched taxonomies, and unverifiable AI outputs that implies. Latency was built differently: starting from source-level European registries, normalising +52M companies across all major jurisdictions, and building an intelligence layer that links legal entities to the commercial brands they actually operate. The result is a single platform where investment funds and B2B sales teams run thesis-driven market scans in seconds, find the companies they didn't know they were looking for, and arrive with context no competitor has.

Long (~200 words); press release boilerplate

Latency is the agentic intelligence platform for European private markets. Founded in 2023 by a team of investors and technology experts who experienced the problem directly, the company has built the most complete primary-source dataset for European private companies: +52M normalised entities across all major registries, 12M with full verified financials, and a proprietary taxonomy spanning 1,200 micro-markets across sectors that competing platforms fail to index adequately.

Where legacy platforms aggregate data from third-party sources and apply generic AI on top, Latency ingests directly from official European registries, resolves legal entities against the commercial brands they operate, and runs a deterministic analytical layer that connects market structure to the user's investment thesis. Latency reached early commercial traction across two primary use cases: deal sourcing at private equity firms and B2B sales prospecting at mid-market companies. For more information, visit latencydata.com.

Agent layer

Machine-readable.

Voice pillars, tone matrix, channel rules, and vocabulary lists are available as structured YAML and JSON in voice-tone.md. Use the forbidden terms array to validate any content output.

voice-tone.md — voice pillars
yaml
voice_pillars:
  - id: sharp
    rule: insight in the first clause
    in_voice: "70% of European mid-market
      companies PitchBook doesn't index
      aren't a gap in their product.
      They're the gap in your pipeline."

  - id: authoritative
    rule: claims from data, not opinion

  - id: precise
    rule: exact professional vocabulary
    example:
      wrong: "speed of transactions"
      right: "deal velocity"

  - id: direct
    rule: subject, verb, object — point first
voice-tone.md — forbidden terms
json
{
  "forbidden_terms": [
    "AI-powered",
    "data-driven",
    "leverage (as verb)",
    "ecosystem",
    "seamless",
    "game-changing",
    "cutting-edge",
    "disruptive",
    "paradigm shift",
    "transformative",
    "scalable solution",
    "empower",
    "unlock",
    "comprehensive",
    "robust",
    "next-level",
    "end-to-end"
  ]
}
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