V
Veritas
Research Model
The scholarly research imprint.
Working papers, research snapshots, evidence-grade thesis documents. Citation discipline — every reference verified, every claim traceable.
When the work requires more than a single-shot report — structured research, integrated analytics, defensible benchmarking, audience-segmented perception, or AI-visibility auditing — OneMind Strata routes the engagement through one of five specialized models. Each has a governing SOP, dataset, a load-bearing skill discipline, a market-facing brand, backed by millions of traditional consulting data. Different in domain; identical in editorial bar.
Most strategy work fails because the right discipline is never applied. Research without citation traceability becomes opinion. Analytics without three-stream reconciliation becomes a slide. Benchmarks without comparability discipline become talking points. The five models exist so the discipline is built in — before the work begins, not negotiated after.
A single project may pass through one model or several. Veritas proves the thesis. Pareto places it against named peers. IAR reconciles three voices into one decision. Resona tests how each audience hears it. Claros measures how AI engines render it. Different inputs. Different outputs. The same editorial bar.
Working papers, research snapshots, evidence-grade thesis documents. Citation discipline — every reference verified, every claim traceable.
Three streams — interview, AI-engine, benchmark — reconciled into a single executive readout. Three-stream synthesis.
Quantitative peer-set placement: financial, operational, market metrics across named-firm cohorts. Comparability discipline.
Three audience tiers, four perception dimensions, alignment-pattern diagnostics. Audiences reported separately, never averaged.
How AI engines see the brand — visibility, authority, accuracy across frontier, search-grounded, vertical, and embedded copilot tiers. Reproducibility.
Veritas produces research that survives external scrutiny. Working papers and thesis documents where every assertion is traceable to a verified source. Used by investment committees, executive teams, and regulatory-facing functions where reputational risk attaches to claims.
A scholarly research imprint for thesis documents, working papers, and evidence-grade briefs. Built for situations where the claim must hold up.
A 30-page evidence-grade thesis paper for an investment committee — market structure, returns, exit dynamics, defensibility.
Cited working paper on a new compliance regime: timeline, exposure, comparable jurisdictions, recommended posture.
Evidence map of a disruptive technology vector: maturity curves, adoption signals, incumbent vulnerability, tracked since.
Deep research on who actually buys, signs, and renews in a target segment — titles, triggers, sources verified.
Long-form synthesis across N expert interviews and primary documents, with a citation appendix that holds up in diligence.
IAR — the Integrated Analytics Readout — reconciles three streams that strategy teams almost never reconcile cleanly: what people inside say (interviews), what AI engines and external signals show, and what the benchmark data proves. A single decision-grade readout, with the disagreements made visible — not averaged away.
Three streams reconciled into one executive readout. Built for decision moments where each stream alone tells half the story.
A 10-page board pack reconciling internal interviews, AI-engine signals, and peer benchmarks into a unified strategic picture.
Decision pack for a capital reallocation moment: where to invest, divest, hold — with the three streams shown side by side.
Six-month post-close synthesis: what's working (interviews), what the data shows (benchmarks), what the market sees (AI signals).
Yearly strategy reset reconciled across executive interviews, market data, and AI-derived perception — with disagreements surfaced.
72-hour situational readout in a strategic disruption: internal voice, market signal, peer behavior — reconciled fast.
Pareto produces benchmarks that hold up in a board meeting. Named peer cohorts. Verified metrics. Comparability rules declared up front. If the benchmark doesn't survive scrutiny, it never leaves the model. Built for situations where executives need to know, defensibly, where they actually stand.
Quantitative peer-set placement across financial, operational, and market dimensions. Built for defensibility, not directional flavor.
Cost-to-serve, throughput, cycle-time placement against a named cohort of 8–12 peers, with normalization rules disclosed.
Quartile placement on rep productivity, ramp time, win rate, ACV — against named peer set, segment-matched.
IT spend as % of revenue, cloud mix, AI adoption maturity — quartiled against named peers in your sub-sector.
Pay band, equity mix, retention, leadership bench depth — benchmarked against role-matched peers in defensible cohort.
CAC, LTV, payback, gross retention, NRR — quartile placement with disclosed cohort definitions and metric reconstructions.
Resona measures how the brand is perceived — rigorously. Customers, prospects, internal stakeholders, partners, investors are never averaged together. Each audience reported in its own voice, across four perception dimensions, with alignment patterns surfaced as diagnostics. The point is to see where audiences agree, where they don't, and what the gap means.
Three audience tiers, four perception dimensions, alignment-pattern diagnostics. Built so executives can hear each audience clearly, not as a blended mean.
How buy-side, sell-side, and existing holders perceive the equity story across four dimensions — before the roadshow, not after.
Diagnostic of where the brand promise (told to prospects) and brand experience (felt by customers) diverge. Gap heat-map.
Six-month post-rebrand: did the new positioning land with each audience tier, or did it drift back to the old narrative?
Are the CEO, CFO, and product leader telling the same strategic story externally? Voice consistency across four dimensions.
Six months post-close: how do legacy customers, new customers, and acquired-company employees experience the merged brand?
A growing share of stakeholder discovery now happens through AI — frontier LLMs, search-grounded copilots, vertical models, and embedded enterprise assistants. Claros measures visibility, authority, and factual accuracy across all four tiers, with reproducible methodology. The deliverable is a baseline, a guardrail, and a 30/60/90 roadmap to move the numbers.
Four-tier AI-engine audit (frontier, search-grounded, vertical, embedded copilot). Built so AI visibility becomes a managed strategic asset, not a guess.
Reproducible audit of how often, and how, the brand surfaces across frontier LLMs for category-defining prompts.
What AI engines say about your top 10 leaders — verified for accuracy, completeness, narrative coherence. Gaps prioritized.
How AI engines pick winners in your category: prompt-by-prompt LLM share-of-voice study, with movement tracked over time.
From baseline to action: prioritized content, schema, and authority-signal moves to lift visibility and accuracy across tiers.
For B2B SaaS: how does your product surface inside Salesforce Einstein, Microsoft Copilot, and vertical AI assistants? Fix-list.
Most strategy work leaves when the engagement ends — the slides go in a folder, the analyst leaves the room, the institutional memory fades. Stratenity reverses that. The same five models that produce engagement deliverables can be deployed inside your environment, running on your data, so strategy and execution stay in your system — and capability compounds over time.
Your strategy team owns the workflows. We embed the models, train the team, and step back. No re-hiring us to remember what we did.
Every quarterly readout, every benchmark refresh, every perception study feeds the institutional knowledge base — inside your system, not ours.
In-system deployment uses the same SOPs and editorial guardrails as engagement work. The discipline travels with the model, not the engagement.
Tell us which discipline matches the question on the table — or whether the right move is to deploy the engine layer inside your environment. We'll scope from there. Build strategy. Execute it with AI. Keep the capability.