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Better decisions. Faster. With evidence.
A decision support platform that combines data analysis, predictive modeling, and causal reasoning. It doesn't replace human judgment — it augments it with evidence, scenarios, and confidence-scored recommendations.
The Challenge
Executives drown in dashboards. Bain & Company found that companies with the best decision-making practices generate returns 6% higher than competitors — yet most organizations still treat BI dashboards as the finish line instead of the starting point.
The average enterprise deploys 14 BI tools and generates 500+ dashboards. Gartner's 2024 analytics survey found that fewer than 30% of dashboards get viewed more than twice after creation. Organizations spend $20-40M annually on analytics infrastructure that produces charts nobody acts on. The problem is not data visibility. It is the gap between seeing a number and knowing what to do about it.
McKinsey found that executives spend 37% of their time making decisions — and over half that time is considered ineffective. When every choice requires assembling data from six systems, building a custom analysis, and debating assumptions in three rounds of meetings, decisions take weeks that the market gives you days. Quantexa and Aera Technology recognized this gap, but most solutions stop at pattern detection without prescribing action.
A BCG study of 250 M&A transactions found that companies completing due diligence 30% faster captured 15% higher returns. The same principle applies to pricing changes, market entry, capital allocation, and vendor selection. Speed without accuracy is reckless. But accuracy without speed is academic. Most organizations optimize for accuracy alone and lose to competitors who decided three weeks earlier.
Your BI stack tells you that Q3 revenue dropped 8% in the Midwest. It does not tell you whether to reallocate sales territory, adjust pricing, launch a regional promotion, or accept the decline and shift resources to a growing region. That gap — between "what happened" and "what should we do" — is where organizations burn analyst hours building one-off models in spreadsheets, reaching conclusions that are outdated by the time they reach the boardroom.
How It Works
Five-stage pipeline from raw business data to ranked, explainable recommendations. Every decision path auditable, every assumption testable.
Causal and correlational relationships between business variables mapped into a directed acyclic graph. Revenue drivers, cost levers, market factors, and strategic options connected with weighted edges. The graph incorporates domain expertise from subject matter experts and statistical relationships from historical data. Unlike a dashboard that shows isolated metrics, the graph captures how variables influence each other.
Latin Hypercube Sampling generates scenario variants across the parameter space — more efficient than pure Monte Carlo for high-dimensional problems. Each scenario specifies values for controllable variables (our decisions) and uncontrollable variables (market conditions, competitor actions). Constraint satisfaction ensures only feasible scenarios enter simulation. A typical analysis generates 500-5,000 scenarios in under 10 minutes.
Each scenario propagates through the decision graph using Bayesian network inference for probabilistic relationships and system dynamics modeling for feedback loops. Financial projections, risk metrics, and operational KPIs computed for each scenario at monthly granularity over the decision horizon. Simulation accounts for temporal dependencies — a Q1 pricing decision affects Q3 market share which affects Q4 revenue.
Multi-objective optimization identifies Pareto-optimal decision paths across competing objectives. NSGA-III algorithm handles 4-8 simultaneous objectives without reducing them to a single weighted score. Sensitivity analysis identifies which assumptions have the largest impact on rankings — so decision-makers know where to invest in better information versus where the answer is clear regardless of assumption changes.
Post-decision, actual outcomes feed back into the system. Brier scores and calibration curves measure how well the simulation predicted reality. Systematic over-confidence or under-confidence in specific variable categories triggers model recalibration. Decision audit trails capture the full context: what was known, what was assumed, what alternatives were considered. Over time, the system learns which types of decisions the organization predicts well and where blind spots persist.
Performance
Metrics from operational systems — not laboratory tests.
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Scenarios simulated per analysis
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Decision cycle time reduction
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Simulation time (500 scenarios)
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Forecast calibration accuracy
Applications
Each use case goes beyond descriptive analytics. These are prescriptive: the system recommends actions, simulates consequences, and tracks outcomes against predictions.
Evaluate 50+ variables — regulatory environment, competitive density, customer acquisition costs, talent availability, infrastructure requirements — across potential markets. The system generates probability-weighted outcomes for each entry strategy: organic growth, acquisition, joint venture, licensing. Not a single forecast, but a distribution of outcomes with confidence intervals.
Score acquisition targets across financial health, cultural compatibility, technology overlap, customer base complementarity, and integration risk. Simulate post-merger scenarios: best case, base case, downside. BCG found that 50% of M&A transactions destroy shareholder value — systematic evaluation against 200+ diligence factors reduces that failure rate measurably.
Distribute investment across business units, projects, and geographies using multi-objective optimization. Constraints include risk tolerance, return thresholds, strategic priorities, and regulatory capital requirements. The output: ranked allocation scenarios with projected returns, risk profiles, and sensitivity to key assumptions.
Evaluate vendors across 50+ dimensions — price, quality metrics, delivery reliability, financial stability, geographic risk, ESG scores, and contractual flexibility. Simulate supply scenarios: what happens if this vendor fails? What is the switching cost? Procurement teams using structured vendor intelligence reduce supply disruptions by 25-40%.
Model the financial and operational impact of proposed regulations before they take effect. When the EU AI Act passed, organizations that had pre-modeled compliance scenarios moved 6 months faster than those scrambling after publication. Simulate workforce, technology, process, and cost impacts across regulatory scenarios.
Go beyond A/B testing individual price points. Simulate entire pricing architectures — bundles, tiers, volume discounts, geographic variations — against demand elasticity models, competitor response patterns, and margin targets. The difference between descriptive pricing analytics and prescriptive pricing intelligence is the difference between knowing your margin dropped and knowing which price lever to pull.
Model restructuring scenarios — headcount changes, role consolidations, office closures, outsourcing transitions — against productivity projections, severance costs, rehiring timelines, and institutional knowledge loss. Every scenario scored on 12-month and 36-month financial impact, execution risk, and employee retention probability.
Evaluate which products to invest in, maintain, sunset, or divest using contribution margin analysis, market growth trajectories, cannibalization modeling, and strategic fit scoring. Procter & Gamble's 2014 brand divestiture — shedding 100+ brands to focus on 65 — generated $10B in savings. The same analysis framework applies at any scale.
Score investment opportunities — real estate, private equity, venture, infrastructure — on risk-adjusted return using Monte Carlo simulation across 1,000+ scenarios. Each scenario varies interest rates, cap rates, occupancy, exit timing, and macroeconomic conditions. The output is not a single IRR projection but a probability distribution of outcomes.
Industry Applications
Specific applications across operating environments — not generic industry labels.
Applied Intelligence
Deployment
We deploy where your operations live — cloud, on-premise, or at the edge. The architecture serves your governance and latency needs, not the other way around.
Managed deployment on your preferred cloud provider. Rapid scaling, minimal infrastructure overhead.
Full deployment within your data center. Complete data sovereignty and infrastructure control.
Processing at the data source for latency-sensitive applications. Sub-second response times.
Frequently Asked
Business intelligence answers "what happened?" Decision intelligence answers "what should we do about it?" BI takes the same data, summarizes it into dashboards and reports — useful for understanding the past, insufficient for shaping the future. Decision intelligence adds causal modeling, scenario simulation, and prescriptive optimization. It tests strategies against thousands of scenarios before you commit resources. Quantexa focuses on entity resolution and network analytics; Aera Technology on cognitive automation; Peak.ai on supply chain decisioning. This platform spans all strategic decision types — M&A, capital allocation, market entry, pricing — with domain-agnostic scenario simulation.
Keep them. Tableau and Power BI are excellent at descriptive and diagnostic analytics — showing what happened and why. Decision intelligence starts where they stop. When your Tableau dashboard shows Q3 revenue dropped 8% in the Midwest, the next question is: do we adjust pricing, reallocate sales territory, launch a regional promotion, or accept the decline? That question requires scenario simulation, not another chart. Decision intelligence consumes data from your existing BI stack and produces actionable recommendations with quantified trade-offs.
Predictive Analytics generates probability estimates — this customer has a 73% churn risk, this equipment has a 14-day failure window, demand next quarter will be 5,200 units. Decision Intelligence consumes those predictions as inputs. Given that churn risk, what intervention maximizes retention ROI? Given that failure prediction, should we schedule emergency maintenance, stock replacement parts, or redistribute load to redundant equipment? Prediction tells you what will happen. Decision intelligence tells you what to do about it. The two systems compound each other's value.
Every strategic decision involves incomplete data — that is what makes it strategic instead of operational. The system handles uncertainty explicitly. Unknown variables get probability distributions instead of point estimates. Sensitivity analysis shows which unknowns matter and which do not. If the best decision is the same across 80% of scenarios regardless of a particular assumption, you do not need to spend three weeks researching that assumption. If a single variable flips the recommendation, the system highlights it: get better data here before deciding.
Pre-built decision templates — M&A evaluation, market entry, capital allocation, vendor selection — take 1-2 weeks to configure with your data sources and organizational constraints. Custom decision models for novel strategic questions take 4-8 weeks. The initial setup involves mapping your data landscape, defining decision variables, and calibrating the causal graph with domain experts. Once configured, new analyses on the same decision type run in hours, not weeks.
AI Agents handle the data assembly and analysis preparation that traditionally consumes analyst time. An agent monitors market conditions continuously, flags when a decision trigger is reached (competitor price change, regulatory announcement, threshold metric breach), automatically updates the decision graph with new data, and drafts initial scenario parameters. The human still makes the decision. The agent ensures the decision model is current when that moment arrives — instead of scrambling to build an analysis after the fact. Agents reduce the time from trigger event to decision-ready analysis from weeks to hours.
No. The math scales down. A 50-person company making a $2M market entry decision benefits from scenario simulation just as much as a Fortune 500 company making a $200M acquisition. The decision framework is the same — define options, model uncertainties, simulate outcomes, rank trade-offs. What changes is the data complexity and the number of stakeholders. Mid-market companies often extract more value per dollar because they lack the 20-person strategy teams that large enterprises use to build these analyses manually.
Three metrics. First, decision cycle time: how many days from question to commitment? Organizations typically see a 50-60% reduction. Second, forecast calibration: when the system predicted an 80% probability outcome, did it actually occur roughly 80% of the time? Brier scores track this rigorously. Third, outcome variance: are actual results closer to projections than they were under the previous decision process? This takes 6-12 months to measure meaningfully. The system logs every prediction and every decision, creating a feedback loop that most organizations have never had — and that makes every subsequent decision better informed than the last.
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Page reviewed: March 2026