Vivameda

Case Studies and Methodology

How institutional teams apply Vivameda intelligence

Three representative engagements followed by the methodology that underwrites every release. Client identities and figures are anonymised. The structures, intelligence layers, and outcomes reflect the patterns of how Vivameda data is used in production analytical environments.

Representative engagements

Three institutional applications, structured end to end

Each engagement is presented in the same structure: the question asked, the intelligence layers involved, the approach taken, and the measurable outcome.

Case 01Investment diligence

Workforce trajectory as a diligence signal

Mid sized European asset manager

1,800

Private companies covered

2

Diligence flags raised

Quarterly

Refresh cadence

The question

Identify late stage private companies where engineering headcount growth diverges from reported revenue guidance.

Intelligence layers

Growth Intelligence and Workforce Structure

Approach

Quarterly company year observations were joined to the manager internal coverage universe. Engineering and product seniority distributions were compared against three year revenue claims sourced from pitch materials.

Outcome

  • Coverage extended across 1,800 private companies without manual research overhead
  • Two portfolio candidates flagged for revisited diligence based on workforce divergence
  • Quarterly refresh integrated directly into the internal research warehouse

The longitudinal structure is what made it usable. We did not need to build the historical view ourselves.

Head of Data, European long only manager, anonymised

Case 02Model training

Training a recovery pattern classifier

Enterprise AI platform

12.4M

Labelled observations

+0.18

Validation F1 gain

1998 to 2024

Training window

The question

Train a model to distinguish durable recoveries from short term rebounds across two full economic cycles.

Intelligence layers

Company Landscape Intelligence and Market Intelligence

Approach

Schema stable Parquet exports covering 1998 to 2024 were used as the training corpus. Labels were derived from sustained workforce, capability, and financial expansion windows of 36 months or more.

Outcome

  • Model trained on 12.4M labelled company year observations
  • Validation F1 improvement of 0.18 over the previous internal baseline
  • Re trained quarterly using the same delivery pipeline without schema drift

Schema stability across two decades is rare. It removed the largest source of friction from our retraining cycle.

Lead ML Engineer, enterprise AI platform, anonymised

Case 03Corporate strategy

Capability benchmarking for corporate strategy

Global consulting practice

42

Peer companies benchmarked

3

Capability gaps quantified

10 years

Comparison window

The question

Benchmark a Fortune 500 client workforce composition against direct competitors and adjacent industry peers.

Intelligence layers

Workforce Structure and AI Intelligence

Approach

A peer set of 42 companies was constructed across two industry adjacencies. Skill and capability distributions were compared at the company year level across a ten year window.

Outcome

  • Capability gaps surfaced in three functional areas with quantified peer benchmarks
  • Findings integrated into the client three year organisational design programme
  • Refresh cadence agreed for annual recalibration of the peer set

We replaced two months of manual desk research with a structured peer benchmark we could defend in the boardroom.

Partner, global strategy practice, anonymised

Methodology notes

What underwrites every release

The case studies above rest on a documented methodology. Each release is governed by the same six principles, applied identically across marketplaces, direct licences, and enterprise integrations.

Source provenance

Every observation in the archive carries a documented origin. Public registries, regulatory filings, employment signals, and capability disclosures are tracked at the source level. Nothing is inferred without an upstream record.

Longitudinal integrity

Company year records are reconciled across decades to preserve identity continuity through mergers, rebrands, spin offs, and jurisdictional moves. Historical revisions are versioned rather than overwritten.

Schema stability

Field definitions are governed under a documented schema contract. Breaking changes are deferred and grouped into annual releases so that downstream models and pipelines remain stable across refresh cycles.

Validation and review

Each release is subject to internal sampling against primary sources, cross checks against independent registries, and reconciliation against prior period observations. Validation notes are available on request.

Compliance posture

Processing operates under GDPR Article 6(1)(f), Legitimate Interest, with documented Data Processing Agreements. All data resides in EU based Tier 3 infrastructure. No individual contact data, no profiles, no outreach signals.

Aggregation level

All intelligence layers are structured at the company year level. Workforce, capability, and financial signals are aggregated. The dataset is built for institutional analysis, not for targeting individuals.

Evaluate the data on your own terms

Sample datasets are available for direct download across every intelligence layer. For institutional evaluation through Snowflake, named client references, or methodology documentation, our team can scope the right next step.

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