We are developing a coordinated analytical system that carries a business question through data preparation, analysis, verification, and recommendations. It is not yet publicly available. Today, our practitioners deliver expert services.
Intelligence directed by each participant’s objectives, executed through coordinated agents, supported by inspectable evidence, and governed by explicit permissions.
Owners define weighted goals, non-negotiable constraints, permissions and approval thresholds. Alternative strategies and decisions are recorded, with reversal where supported. A supervising agent does not replace legal management or human governance responsibilities.
Specialized agents would prepare, analyze and report under explicit mandates. Independent checks may include deterministic reconciliation, statistical validation, policy constraints and human review. Multiple agents do not guarantee correctness.
All 36 modules are roadmap intentions, not a claim of working software. Commonsent Signal is a separately deployed experimental pilot linked to the Listen direction; professional analytics services are available today.
Future Research: independent businesses keep separate objective functions, privacy boundaries, constraints and decision rights. A coordination agent proposes bounded actions; no central agent may commit all members. Procurement and other collective structures require legal review.
46 proposed internal modules: 4 Direct + 36 Engine + 6 Coordinate. These are distinct from the nine customer-facing analytical service families. These modules describe planned development, not current availability.
Planned: detect a change, check data quality, compare explanations, and present evidence for review.
Planned: prepare cohorts, evaluate churn and value models, validate findings, and prioritize retention hypotheses.
Planned: compare forecast and media scenarios with explicit assumptions, uncertainty, and approval requirements.
Planned: coordinate reporting, diagnostics, monitoring, and documentation across reusable workflows.
These are intended applications, not deployed self-service modules. Availability and scope depend on research, validation, and development.
Choose a business problem, explore a sample dataset, investigate performance, review analytical findings, examine supporting evidence, and preview a client-ready deliverable.
For agencies: preview branded executive dashboards, digital audits, media summaries, and evidence using fictional clients.
Filter synthetic dashboards, inspect KPIs, and explore business trends.
Segment a synthetic funnel and inspect conversion and abandonment counts.
Compare synthetic campaign metrics, illustrative response curves, and budget allocations.
Working now: deterministic calculations in your browser using local synthetic datasets. No live AI agents, authorized client connections, uploads, or validated causal findings. Planned: agent planning, execution, independent validation, analytical summaries, and reporting through authorized APIs. The analytics platform is intended as an empirical proving ground for Commonsent Lab’s broader agent-coordination research.
The planned Agency Intelligence Platform would give agency partners an agent-operated, multi-client analytics environment. The workspace and all capabilities below are in development, not currently available.
Planned: organize client accounts with tenant isolation and explicit boundaries between each client's data, assets, and analytical context.
Planned: generate client-ready reports and presentation materials under agency branding, with evidence and review requirements attached.
Planned: coordinate specialist agents across preparation, analysis, and independent validation, with human authorization for consequential actions.
Planned: reuse analytical methods and workflow definitions across engagements while keeping client data and permissions separate.
Planned: define source access, approval gates, consent requirements, and review responsibilities per client.
Planned: view analytical work across client accounts, identify bottlenecks, and monitor workload and exception handling.
Planned: track workflow usage, operating costs, validation results, and delivery quality against agreed measures. No productivity or margin outcomes are guaranteed.
These are product intentions, not operational features or validated security claims. Access depends on development and evaluation; no public availability date is committed.
These are intended security and governance principles, not a certification or a claim that all controls are operational. Data handling and retention requirements must be agreed before any pilot.
Future decision records would connect the original objective to evidence, authorization and observed outcomes. Independent verification is an engineering objective, not a guarantee of correctness.
Separate participants retain separate mandates and permissions. A single weighted score does not settle uncertain outcomes or conflicting stakeholder interests.
90–99%Automation of eligible routine analytical work.
Up to 95%Human-effort reduction hypothesis for equivalent validated deliverables.
Engineering and research objectives only. Neither target is an achieved or verified operational result. They refer to different measures and are not promises of client savings. Eligibility excludes tasks requiring judgment, novel problem framing, or consequential human authorization.
Future evaluations would measure autonomous task-completion rate, human intervention hours, numerical accuracy, statistical validity, execution time, AI and infrastructure costs, cost per accepted analytical deliverable, human QA effort, reproducibility, and business outcome improvement. Lower human labor does not imply an equivalent reduction in total cost. Comparisons require equivalent inputs and acceptance standards against human analytical workflows.
The 90–99% automation objective and up to 95% human-effort reduction hypothesis remain unverified targets. No simulated live performance numbers are presented. Benchmarks must include supervision, validation, exceptions, and rework, with predefined eligibility, practitioner baselines, quality gates, and transparent limitations.
This product progression is separate from current Commonsent Analytics consulting services. Later stages are not completed development or legally cleared collective purchasing structures. Consumer and household agents belong to separate future Commonsent Lab research.
Commonsent Analytics is the commercial starting point for a broader research vision.
Commonsent Lab investigates federated, participant-controlled AI agents capable of coordinating around individual and collective objectives while maintaining human agency, consent, transparency, and accountability.
The analytics platform can serve as a practical proving ground for agent-to-agent coordination and its potential efficiency benefits. The Lab's long-term research vision is distinct from our operational commercial services and the platform currently in development.
Explore Commonsent Lab →Tell us which analytical workflows matter to you. This registers interest in future evaluation or pilot opportunities; it does not create a platform account.
Organic conversation or coordinated activity? Help PR, crisis communications and brand teams inspect patterns, supporting evidence and counter-evidence in public-conversation captures.
Explore Commonsent Signal →Revenue versus profit, acquisition versus retention, advertising return versus cash, reach versus brand integrity, efficiency versus service quality, and short-term performance versus long-term value require explicit tradeoffs.
Two fictional businesses explore compatible packaging procurement under separate mandates. A shared agent cannot bind either participant. No confidential member preferences are released to the joint view.