Private MVP development

Turn complex decisions into evidence-backed reasoning graphs

Hexalveo decomposes high-value business decisions into subproblems, evidence, assumptions, constraints, risks, and affected actors, then shows how each element contributes to the recommendation.

Inspect uncertainty, challenge assumptions, test counterfactuals, and maintain a complete decision record.

For strategy teamsFor investment teamsFor technical leaders

What it produces

Decision model

Inspectable graph of claims, evidence, assumptions, constraints, and risks

Calculated Confidence

A single confidence score supported by traceable evidence, contradictions, information gaps, and uncertainty factors

Recommendation record

Conditions, gaps, actors, and review trail preserved

Product concept preview

Manufacturing investment decision

This is an honest product-style mockup, not a live customer analysis.

Private MVP concept

Inputs

Decision statement

Should a manufacturer invest €1 million in predictive-maintenance infrastructure?

Key actors

COO, plant manager, finance lead, maintenance teams, line operators

Constraints

€1M capex ceiling, 18-month rollout, ERP integration, production downtime limits

Reasoning

Subproblems

Downtime reduction, sensor coverage, vendor fit, integration cost, adoption risk

Evidence

Maintenance logs, downtime history, vendor proposal, spare-parts spend, pilot-line data

Contradictions

Vendor savings estimate conflicts with internal downtime variance

Information gaps

Sensor reliability, union impact, installation schedule, model drift monitoring

Output

Recommendation

Proceed only with a staged pilot on two high-loss production lines

Change conditions

Reject if pilot uptime gain stays below 8% or integration cost exceeds plan by 20%

Confidence profile

Overall confidence: 68% — Moderate. Limited by sensor-reliability data, integration-cost uncertainty, and conflict between vendor projections and internal downtime records.

Illustrative confidence profile

Confidence is limited by incomplete pilot data, disagreement between internal records and vendor estimates, and uncertainty about integration cost.

100%

Supporting evidence

61%

Contradicting evidence

26%

Unresolved uncertainty

13%

Decision

€1M predictive maintenance investment

Evidence

Maintenance logs + vendor proposal

Risk

Integration cost and production downtime

Recommendation

Stage-gated pilot before full rollout

The problem

Critical decisions need more than an answer

Ordinary AI responses can be useful for exploration, but high-value organizational decisions need inspectable structure.

Assumptions may remain hidden.

Evidence may not be traceable.

Contradictions may be overlooked.

Confidence may be misleading.

Decisions may be difficult to review later.

Stakeholder impact may not be explicit.

Every decision session gives you

Structured artifacts you can inspect, challenge, and preserve

The product is designed around observable decision behavior, not generic AI marketing claims.

Reasoning map

Understand how the decision was broken into connected questions and supporting logic.

Evidence trail

Connects each source and input to the conclusions it supports, challenges, or leaves unresolved.

Confidence explanation

Shows the calculated confidence score and the evidence, assumptions, contradictions, and information gaps influencing it.

Information gaps

Identifies missing inputs, weak evidence, unknown dependencies, and questions requiring further investigation.

Counterfactual scenarios

Tests how the recommendation changes when costs, timing, adoption, risks, constraints, or key assumptions change.

Stakeholder impact

Shows who makes, reviews, influences, and is affected by the decision—and where their priorities may conflict.

Recommendation conditions

States what would need to be true for the recommendation to hold, change, or be rejected.

Versioned decision record

Preserves the evidence, assumptions, changes, and review context as the decision evolves.

Engine and Studio

A reasoning engine and a workspace for analysts

Hexalveo separates the decision-reasoning layer from the human review workspace.

Hexalveo Engine

The reasoning engine

The reasoning engine that decomposes decisions, verifies evidence, models uncertainty, propagates confidence, and generates structured recommendations.

Explore the Engine

Hexalveo Studio

The analyst workspace

The visual workspace where analysts inspect reasoning graphs, add evidence, challenge assumptions, run scenarios, and prepare decision reports.

Explore the Studio

How Hexalveo differs

From chat output to inspectable decision model

Hexalveo generates and exposes its own structured reasoning artifacts. It does not claim to reveal an LLM's private chain of thought.

Generic AI assistant

Hexalveo

Produces an answer

Produces an inspectable decision model

Conversation-focused

Decision-session focused

Assumptions may remain implicit

Makes assumptions and constraints explicit

Evidence links may be incomplete

Connects evidence to claims

Often gives one confidence value

Shows a calculated confidence score with its supporting and limiting factors

Difficult to reproduce as a formal process

Maintains versioned reasoning artifacts

Focused use cases

Designed for complex, high-value business decisions

The initial homepage positioning focuses on commercial, operational, and technical decision workflows.

Technology investment

Capital allocation

Vendor evaluation

Market entry

Product strategy

Operational risk

Manufacturing investment

Public sector decisions

Design partners

Built with design partners, not behind closed doors

Hexalveo is currently in private MVP development. We are seeking a small number of organizations with complex decision workflows to help validate the product, domain models, and evaluation framework.

Strategy teamsAnalystsInvestment teamsRisk teamsTechnical leaders