heptaloop pricing is scoped around the intelligence system your teams need: decision-science services, usage-based data capacity, storage, compute, custom agents, integrations, and operating support
instead of a generic plan table, heptaloop starts by mapping the decision loop you want to run and the enterprise work required to keep it trusted
map priority sources, score one market problem, stand up a small agent workflow, and prove the decision path with decision-scientist support
expand acquisition, scoring, interaction, publishing, and paid activation into a governed operating loop supported by maintained agents
scale governed data, compute, custom integrations, agent orchestration, and decision-science services across a larger enterprise footprint
pricing expands with implementation intensity: how much evidence flows through the system, how often it refreshes, how many agents act on it, and how much decision-science support is needed to maintain trust
public, partner, first-party, market, social, media, census, CRM, and research sources
refresh cadence, evidence retention, model training, and high-throughput processing
analysis, interaction, publishing, paid media, routing, and next-best-action automation
CRM, BI, marketing clouds, approval flows, audit trails, compliance, and security posture
onboarding, model tuning, operating reviews, experimentation design, and advisory support
maintain the agents, translate signals, tune models, and keep business decisions defensible
scale ingestion, storage, processing, model execution, and evidence refresh by operating demand
connect the loop to CRM, BI, content ops, paid media, approvals, and executive reporting
heptaloop combines platform access with structured implementation services because consumer intelligence and activation fails when data, models, governance, and action workflows are scoped separately
same ingredients. different scale
the useful question is not “how many seats?” it is “what decision loop should exist, how much evidence must feed it, and who maintains trust in the output?”
no user access matters, but the engagement is primarily shaped by services, usage, data, storage, compute, integrations, governance, and agent workflow depth
yes many engagements begin with one decision loop, then expand once the evidence model, scoring logic, and action workflow prove useful
new data sources, faster refresh cadence, more markets, additional agent workflows, deeper integrations, larger evidence stores, and more decision-science operating support
heptaloop agents are maintained and supported by decision scientists so the system produces trusted decisions, not just automated outputs
enterprise consumer intelligence and activation varies by evidence complexity, compliance, operating model, and activation depth a fixed public table would hide the real implementation drivers
share the market problem, data surfaces, workflow owners, and decision cadence. heptaloop will return a service-first pricing architecture matched to the implementation