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Intelligence
A custom research service

Most research
tells you what
happened.
Cons(AI)nsights
tells you what
to do next.

"The value of intelligence is not in what it describes. It is in the quality of the decision it makes possible."

We are not a market research firm. We are not a data platform. We are an intelligence architecture built on the proposition that the hardest problem in business is not finding information — it is knowing which information to act on, with what confidence, and to what end.

Consulting is our process. AI is our engine. Insights are what you leave with. Three disciplines that must work together before the output deserves the word intelligence.

13,000+
Research reports
in the AI corpus
38%
Average rate at which primary data
contradicts secondary research
500+
Custom engagements
delivered
94%
Clients who changed a decision
already in motion
The problem with research in 2026
01

Information has never been
more abundant. Decision quality
has never been more at risk.

The VUCA environment — Volatile, Uncertain, Complex, Ambiguous — has not just accelerated the pace of business. It has broken the traditional relationship between research and decisions. Markets move faster than procurement cycles. Competitors act on intelligence you haven't purchased yet. Regulatory landscapes shift while your report is still in production. And the syndicated data every competitor is reading simultaneously cannot give any of you an edge. The answer is not more research. It is better intelligence — designed around your specific decision, validated against the real world, and delivered before the window closes.

V — Volatile
Markets move faster than research cycles
The competitive landscape your team studied last quarter is not the one you are operating in today. Most organisations are navigating 2026 with 2024 intelligence. The gap between those two timelines is where strategic errors compound.
U — Uncertain
Secondary data reflects the past, not the future
Syndicated market research is built from surveys fielded 12–18 months ago, analysed by teams who never spoke to your specific buyers. It describes what people decided. Primary research reveals what people are deciding. The distinction is strategic.
C — Complex
Every major decision lives at the intersection of multiple markets
An AI infrastructure decision is simultaneously a technology choice, a regulatory question, a talent bet, and a financial model. Research that reads one vertical misses the second-order effects that come from the others. Complexity demands cross-market intelligence.
A — Ambiguous
Certainty is the wrong goal. Calibrated confidence is
Data presented without confidence intervals creates false precision — the most dangerous kind of error in a boardroom. Knowing that a finding is 88% confident versus 52% changes the strategic response entirely. Ambiguity is not a problem to eliminate. It is a condition to navigate.

"The most expensive research is the kind that confirms what you already believe, on a timeline that is too slow to matter, about a market that has already moved."

Cons(AI)nsights · Founding Principle
The Intelligence Architecture

Three disciplines.
One integrated system.

The Cons(AI)nsights model is not a service catalogue. It is a structured theory of how intelligence is produced — and why most research fails to qualify as intelligence at all. Each component addresses a specific failure mode. Together, they produce something none can produce alone.

Dimension 01
C
Cons
Consulting
The process of asking the right question before answering any question
Consulting is not a style. It is a methodology. Before AI touches a single data source and before a single survey respondent is fielded, the consulting process maps your strategic question to the right research architecture. The failure mode it addresses: research that answers the question asked rather than the question that matters.
  • Strategic question architecture — translating business intent into research design
  • Decision mapping — identifying the specific choice the intelligence must inform
  • Scope calibration — determining what level of certainty the decision requires
  • Interpretation partnership — staying with you through finding to action
Dimension 02
(AI)
(AI)
Artificial Intelligence
The synthesis engine that reads across markets simultaneously
AI is the structural advantage that separates Cons(AI)nsights from both traditional research firms and pure data platforms. 13,000+ reports queried in real time. Cross-market pattern recognition that no human analyst team can replicate at speed. Confidence scoring applied automatically based on source agreement. The failure mode it addresses: research that reads one market in isolation.
  • Cross-market corpus synthesis — 13,000+ reports, multiple verticals, simultaneous
  • VUCA signal classification — every finding mapped across all four dimensions
  • Confidence scoring — 0–100 based on source agreement and recency weighting
  • Divergence detection — flagging where data sources systematically disagree
Dimension 03
I
nsights
Validated Insights
The output that earns the word intelligence by surviving contact with the real world
An insight that has not been tested against primary data is a hypothesis wearing research clothing. Cons(AI)nsights delivers findings that have been fielded to 500–750 actual decision-makers in your target market and validated or challenged by domain experts. The failure mode it addresses: intelligence that cannot be acted on with confidence.
  • Primary survey validation — 500–750 screened respondents in your exact market
  • Expert interview layer — domain practitioners who contextualise what surveys cannot
  • Divergence report — every contradiction between primary and secondary data surfaced
  • Decision brief format — built for the meeting where the decision gets made
Where the three dimensions intersect
Cons + (AI)
Structured enquiry meets scale
When the consulting methodology defines the right question and the AI engine searches for the answer across 13,000 sources simultaneously, the result is research that is both precisely targeted and comprehensively informed. Neither dimension alone produces this.
(AI) + nsights
Synthesis validated by reality
When AI synthesis identifies signals and primary research tests them against actual market participants, findings arrive with a confidence architecture. You do not receive data. You receive scored intelligence — where 38% of findings will contradict what secondary data predicted, and you will know exactly which ones.
Cons + nsights
Decision design to decision action
When the consulting process that designed the research stays present through the delivery of findings, intelligence does not stop at a PDF. The same team that scoped your question interprets the answers with you — translating validated findings into the specific next actions your organisation can take.
Designed around your role

Five functions.
Five different
definitions of value.

Intelligence is not one-size-fits-all. A Chief Strategy Officer making a market entry bet and a CMO designing a go-to-market motion need fundamentally different outputs from the same engagement. Select your role to see the questions, objectives, and measurable outcomes specific to your function.

01
Strategy &
Corporate Development
CSO · VP Strategy · Corporate Dev
02
Research, Product
& Innovation
CIO · VP Product · Head of R&D
03
Product Management
& Market Intelligence
VP Product · CI Lead · Product Strategist
04
Marketing, GTM
& Growth
CMO · VP Marketing · Head of GTM
05
C-Suite &
Corporate Leadership
CEO · CFO · COO · Board
Strategy & Corporate Development
Capital allocation is the most irreversible act in business. It demands intelligence, not confidence.

You are accountable for the decisions that define where the organisation competes next — market entries, acquisitions, portfolio restructuring, disruption response. Every bet you make commits resources that cannot be reallocated without cost. The intelligence you act on must be validated against reality, not borrowed from analyst consensus.

Chief Strategy Officer VP Strategy Director – Corporate Development Strategy Consultant
Your measurable objectives
  • Validate new market entry opportunities with primary buyer demand confirmation before capital commitment
  • Screen M&A targets against verified market viability — not headline revenue multiples alone
  • Produce foresight that reaches the board agenda before disruptions become visible in results
  • Build growth strategies grounded in external evidence, not internal financial model optimism
Short term
"Where are new high-growth markets emerging — and are we positioned to enter before the window closes?"
Measurable outcome: A ranked, survey-validated shortlist of market entry candidates with addressable market sizing and primary buyer demand confirmation — specific enough to bring to a board strategy session.
Medium term
"Which adjacencies align with our core capabilities — and which M&A targets or partnership paths get us there fastest?"
Measurable outcome: A screened acquisition landscape with market viability scoring, synergy mapping, and competitive position data — reducing due diligence time substantially and reducing the risk of acting on incomplete information.
Long term
"What new business models will reshape our industry — and how do we sustain competitive edge in a disrupted ecosystem?"
Measurable outcome: A 3-scenario disruption readiness report with expert-validated probability weighting and a prioritised strategic response playbook per scenario — enabling pre-emptive positioning rather than reactive pivoting.
Engagements:
Market Entry Blueprint
Diversification Opportunity Analysis
M&A Target Identification & Screening
Foresight & Scenario Planning
Disruption Readiness Report
Research, Product & Innovation
The technology is ready. But is the market? And are you timing the bet correctly?

You allocate finite R&D capital against bets that play out over years. The failure mode is not building the wrong product — it is building the right product at the wrong moment, for a market that has moved while you were in development. Intelligence that separates technical readiness from commercial readiness determines whether your innovation becomes a category or a case study.

Chief Innovation Officer VP Product Head of R&D Innovation Manager Product Development Director
Your measurable objectives
  • Prioritise R&D investments with adoption curve data validated by enterprise practitioners — not modelled projections
  • Identify emerging technologies before they reach the mainstream procurement cycle
  • Compress go-to-market timing by knowing when the market is ready, not just when the product is
  • Identify the right startups and co-innovation partners before your competitors formalise relationships
Short term
"Which innovations are most likely to disrupt our industry — and how can we shorten development cycles with better market timing foresight?"
Measurable outcome: A ranked disruption radar with technology maturity scores and time-to-mainstream adoption estimates, validated by enterprise practitioners making purchasing decisions in your category — specific enough to reprioritise your pipeline.
Medium term
"What unmet customer needs will shape the next generation of product — and when is the right moment to bring a new technology to market?"
Measurable outcome: A validated product-market fit analysis that maps unmet needs to technology readiness — with primary survey evidence from actual target buyers, not analyst extrapolation from adjacent categories.
Long term
"How do I translate R&D into profitable commercialisation — and who are the right partners to co-innovate with at scale?"
Measurable outcome: A commercialisation feasibility study with a partner and startup scouting landscape — each candidate scored by strategic fit, market velocity, and integration complexity to replace the informal network-based process.
Engagements:
Emerging Technology Impact Assessment
Innovation Pipeline Validation
Partner & Startup Scouting Report
Product-Market Fit Analysis
Commercialisation Feasibility Study
Product Management & Market Intelligence
A roadmap built on assumption has its failure mode already embedded before a line of code is written.

You make the decisions that determine what gets built, for whom, and when it ships. Every priority on that roadmap is an investment. Investments made on unvalidated assumptions produce outcomes that only become clearly wrong 18 months later — when the cost of changing course is highest. The intelligence that prevents this is not better analysis of historical data. It is validated knowledge of current buyer behaviour.

VP Product Director of Product Management Market Intelligence Manager Competitive Intelligence Lead Product Strategist
Your measurable objectives
  • Validate roadmap priorities against actual buyer behaviour before engineering resources are committed
  • Track competitor positioning and feature moves in real time — not 6 months after they have taken effect
  • Identify which market segments carry the highest ROI potential for your specific product
  • Optimise pricing against real willingness-to-pay data, not analyst benchmarks from adjacent categories
Short term
"How are competitors repositioning — and which features are genuinely driving adoption among our target buyers, as verified by those buyers?"
Measurable outcome: A competitive intelligence brief with feature benchmarking, survey-validated adoption drivers from actual target users, and a ranked gap analysis — specific enough to change the next sprint planning conversation.
Medium term
"Which segments show the highest ROI potential — and what is the right pricing and positioning mix to scale faster than competitors?"
Measurable outcome: A segment prioritisation model with market sizing, willingness-to-pay data, and pricing benchmarks from actual enterprise buyers — replacing the internal assumptions that optimistically overstate every addressable market.
Long term
"How will customer expectations evolve over the next decade — and which macro trends could redefine our entire product portfolio?"
Measurable outcome: A 3-horizon portfolio map with macro trend integration, customer persona evolution modelling, and expert-validated scenario planning for portfolio restructuring before the market forces it.
Engagements:
Competitive Intelligence Report
Product Benchmarking Dashboard
Customer Persona Research
Market Sizing & Forecast Models
Opportunity Landscape Report
Marketing, GTM & Growth
Targeting without validated demand signals is expensive guessing dressed as strategy.

You direct budget, messaging, and sales motion toward market segments and buyer personas. Misread one of those variables and every downstream metric suffers — pipeline quality, conversion rate, cost per acquisition, retention. The cost compounds quietly and invisibly until it shows up in a quarterly review. The intelligence that prevents this is not better analytics on what your existing buyers have done. It is validated knowledge of what prospective buyers are currently deciding.

Chief Marketing Officer VP Marketing Director of Product Marketing Head of GTM Market Research Manager
Your measurable objectives
  • Build GTM strategies anchored in verified demand signals — eliminating the internal bias that makes every segment look equally attractive
  • Understand how buying patterns are shifting before the mismatch shows up in declining pipeline quality
  • Validate message resonance with the actual audience before campaign budgets are committed
  • Identify brand positioning gaps against competitors from the buyer's perspective — not the marketing team's
Short term
"Which segments should we target first — and which messages will resonate, as confirmed by our actual target buyers before campaign launch?"
Measurable outcome: A segment targeting brief with demand concentration data and message resonance scores from primary survey — specific enough to reallocate campaign budget with evidence rather than assumption.
Medium term
"How are competitors positioning in new regions — and what role will digital channels play in influencing B2B buyers over the next 18 months?"
Measurable outcome: A channel effectiveness study with competitive positioning mapping and B2B buyer journey intelligence — showing exactly where attention and intent are shifting before your competitors act on it first.
Long term
"Which trends will redefine buyer perception over the next 5 years — and how do we build a permanently insight-driven GTM function?"
Measurable outcome: A buyer perception evolution report with persona trajectory modelling and a structured roadmap for building institutional market intelligence — so strategic decisions are never again based on last year's buyer data.
Engagements:
GTM Planning Study
Customer Segmentation & Persona Development
Content Intelligence & Messaging Validation
Brand & Positioning Benchmarking
Demand Forecast & Channel Effectiveness
C-Suite & Corporate Leadership
At the executive level, every decision you make sets the ceiling for every decision made below it.

You commit capital, structure, and organisational energy around strategic bets that define the business for years. The intelligence that reaches you must be validated — not compiled. Every function beneath you optimises around the strategic direction you set. An error at the top is not a departmental problem. It is an organisational one. The cost of wrong intelligence at the C-suite is not a missed quarter. It is a compounding strategic misalignment.

CEO CFO COO Chief Growth Officer Board Strategy Team
Your measurable objectives
  • Validate market potential with primary data before capital allocation — replacing internal projections that have never been tested externally
  • Anticipate disruption across business units before it becomes visible in financial results
  • Receive intelligence in a format built for the boardroom — scored, sourced, and designed to withstand a CFO's question
  • Align the organisation around strategic direction grounded in external evidence, not consensus-building
Short term
"Is this market large enough to justify our investment — and what are the early signals of disruption we cannot afford to miss?"
Measurable outcome: A market opportunity and risk assessment with primary-validated sizing, a disruption signal dashboard, and a board-ready brief designed for the 20-minute executive slot — not the analyst team's 80-page version.
Medium term
"Which business units will drive next-phase growth — and what is the real ROI of entering adjacent sectors, validated externally?"
Measurable outcome: A global growth opportunity map with ROI modelling by unit, adjacency scoring, and expert-validated growth assumptions — replacing the internal financial model that has never been tested against what external buyers and competitors actually believe.
Long term
"How do we sustain competitive advantage for the next decade — and which technologies could make our current model obsolete before we see it coming?"
Measurable outcome: A competitive and disruption readiness report with technology obsolescence risk scoring, 3-scenario strategic foresight, and a capital reallocation recommendation grounded in evidence rather than internal consensus.
Engagements:
Market Opportunity & Risk Assessment
Competitive & Disruption Readiness Report
Global Growth Opportunity Map
Investment Validation & ROI Models
Board-ready Strategic Foresight Deck
The research architecture

Six stages. Each one
addresses a failure mode
the previous one cannot.

The intelligence architecture of Cons(AI)nsights is not a workflow diagram. It is a cumulative quality system — where each stage makes the next more reliable, and the final output is structurally different from anything a single-method process can produce.

#
Stage
What happens
Why this stage exists
01
Strategic brief & decision mapping
Consulting — the first layer
The consulting process translates your strategic question into a research architecture. We map the specific decision you are making, the confidence threshold it requires, and the stakeholders who need to act on the output.
Research that is not anchored to a specific decision produces findings that are interesting but not actionable. This stage is the difference between intelligence and information.
02
AI corpus synthesis
Artificial intelligence — the engine
13,000+ Consainsights reports queried in real time across every relevant vertical. AI identifies cross-market patterns, signals, and second-order connections that isolated research cannot surface.
Most strategic decisions require reading multiple markets simultaneously. A human analyst team reading one vertical in depth will miss the signal visible only when Technology, Healthcare, Finance, and Regulatory reports are read together.
03
VUCA signal classification
Analytical layer
Every finding is classified as Volatile, Uncertain, Complex, or Ambiguous, with relevance weighting to your specific decision. Findings that look similar can require very different strategic responses depending on which dimension they occupy.
A market opportunity and a regulatory risk both appear as "signals" in undifferentiated research. The VUCA lens determines whether a finding demands immediate action, structural monitoring, scenario planning, or deeper primary investigation.
04
Primary survey validation
Validation layer — 500–750 respondents
Screened respondents — actual decision-makers in your target market — are fielded with questions designed around your specific hypotheses. Results validate, contradict, or reframe what AI synthesis identified from secondary sources.
On average, 38% of primary findings diverge from secondary data. These divergences are not measurement error. They are the gaps between what the market looked like when research was commissioned and what it looks like now.
05
Expert interview validation
Contextualisation layer
Domain experts — former executives, category practitioners, regulatory specialists — are interviewed to contextualise divergences and validate findings that surveys can quantify but cannot explain.
Surveys tell you what people decide. Expert interviews tell you why — and whether the underlying dynamic is structural or temporary. This distinction determines whether a signal justifies a bet or a monitoring posture.
06
Confidence scoring & decision brief
Output layer
Every finding is scored 0–100 based on agreement between AI synthesis, primary survey, and expert testimony. Divergences are explicitly flagged with implications for each scenario. The output is a decision brief, not a research report.
Intelligence without a confidence architecture forces decision-makers to guess how much weight to put on each finding. The scoring system makes that judgement explicit — so you know exactly what to act on decisively and what to monitor.
The confidence scoreA 0–100 rating per finding based on the agreement between AI corpus synthesis, primary survey results, and expert interview testimony. Above 80 — act with conviction. 60–80 — verify before committing. Below 60 — the divergence itself is the intelligence.
The divergence reportA dedicated section of every engagement that surfaces every instance where primary data contradicts what secondary research says. On average, 38% of findings fall here. These are the most valuable pages in every brief — and the ones you cannot buy from a standard research subscription.
The decision brief formatThe output of every engagement is designed for the specific meeting where your decision will be made — whether that is a board strategy session, a product roadmap review, or a capital allocation committee. Not a research archive. A decision instrument.
Provocations

Questions every business
leader should be able
to answer — but most cannot.

On research quality
"The last time you made a major strategic decision, what percentage of the intelligence you relied on had been validated against primary data from your actual target market?"
If the honest answer is zero — and for most organisations it is — then every strategic decision in your organisation has been made on the assumption that secondary research accurately predicts current buyer behaviour. The 38% average divergence rate suggests this assumption is wrong about a third of the time. Over a portfolio of strategic decisions, that failure rate is structural.
On competitive intelligence
"What does your competitive intelligence tell you about where your competitors are moving — or only where they have been?"
Most competitive intelligence is forensic — it analyses published data, quarterly reports, and press releases that describe decisions already made and markets already entered. The competitive intelligence that matters is anticipatory. It identifies where consideration is shifting in your target segment before competitors formalise their positioning. By the time a move appears in a report, the window to respond has often already closed.
On decision confidence
"If someone in your last board strategy session had asked for the confidence interval on the market size figure in your presentation, what would you have said?"
Most market size figures presented in strategic contexts carry no confidence interval. They are point estimates derived from models built on historical data, presented with a precision that implies certainty the underlying methodology cannot support. The CFO in the room knows this. The question is whether the intelligence you bring to strategic decisions is built to withstand that scrutiny — or to avoid it.
On market timing
"Is the market timing assumption in your current product or market strategy based on when you think the market will be ready — or on evidence from buyers who are making timing decisions right now?"
Market timing errors — entering too early or too late — are among the most expensive strategic mistakes an organisation can make. They are also among the most preventable, because the buyers and practitioners who determine market readiness are accessible, willing to be surveyed, and have strong, differentiated opinions about timing that secondary research systematically averages away.

Who gets the most from
validated intelligence

Not every organisation is ready for this. The honest distinction between organisations that extract maximum value from Cons(AI)nsights and those that do not.

✦
Organisations that extract maximum value
✦
Make decisions where being wrong costs significantly more than the research itself — where the intelligence budget is not the constraint
✦
Have a specific strategic question that drives the engagement — not a broad desire for "market knowledge"
✦
Are willing to act on findings that challenge internal assumptions — including the assumptions of the leader who commissioned the research
✦
Operate in markets where buyer behaviour is shifting faster than internal models can track
✦
Need intelligence that can survive board-level scrutiny — not just consensus among the strategy team
—
Organisations that should not engage yet
—
Are looking to confirm a decision already made — where the research is a post-hoc justification exercise rather than a genuine input
—
Cannot identify the specific decision the intelligence will inform, or the specific audience who will act on it
—
Treat intelligence as a compliance requirement — a slide in a deck rather than a driver of strategic behaviour
—
Are unwilling to share the strategic context required to design the right research — treating every brief as a generic market study
—
Are optimising for the cheapest possible research product rather than the highest possible decision quality

The right brief
is three sentences.
The right decision
is worth far more.

We do not need a detailed research specification. We need to understand the decision you are facing — the bet you are about to make, the stakeholder who needs to act, and the question that, if answered with confidence, changes what you do next.

  • 01
    We scope to your decision, not a research category
    Every engagement begins with a consulting conversation — not a form. We translate your strategic question into the right research architecture before a single data source is touched.
  • 02
    Primary validation is included by default
    We do not deliver intelligence built entirely on secondary sources. Every engagement includes a primary research layer — because we know where secondary data fails, and we build for the exception, not the average.
  • 03
    Every finding arrives with a confidence score
    The output is not a list of findings. It is a scored intelligence brief — where you know exactly what to act on decisively, what to verify further, and where the conventional wisdom is structurally wrong.
  • 04
    The consulting relationship extends through interpretation
    The same team that designed the research presents the findings — because interpretation without context is how intelligence gets misused. We stay present through the decision, not just the delivery.
Brief us on the decision

Three minutes. One form. We respond with a scoped research proposal and the analytical framework we would apply to your question.

01
Strategy & Corporate Development
02
Research, Product & Innovation
03
Product Management & MI
04
Marketing, GTM & Growth
05
C-Suite & Corporate Leadership
No sales calls · No spam · Response is a scoped research proposal · Your brief is confidential