three ways to start

start with one loop. scale with the work

choose the starting shape that matches your operating reality. then size the data, agents, integrations, and decision-science support required to run it well

request pricing architecture compare starting shapes
01 choose a shape 02 size the scope 03 receive architecture
01 choose your starting shape

three starting shapes. every detail stays in view

scroll through all three paths. each uses the same structure so the difference in operating scale is easy to understand

01
launch blueprint

prove the first decision loop

one question → governed evidence → a working first loop

start with one decision
map
01 blueprint loop source → score → action
validate
leave with working proof
one workflow priority sources scoring setup rollout path
focused implementation + validation cadence
02
operating loop

run recurring intelligence

live signals → maintained agents → weekly action

start with live signals
refresh
02 operating loop analyze → route → activate
maintain
leave with weekly action
data refresh maintained agents routing + reviews recurring decisions
recurring operation + maintained agents
03
enterprise command

scale markets and teams

brands + markets → governed orchestration → shared control

start with brands + markets
govern
03 enterprise command connect → control → expand
orchestrate
leave with shared control
enterprise data custom agents governance executive view
enterprise governance + custom orchestration
02 size the scope

five levers turn the shape into a real engagement

after the starting shape is clear, pricing expands with the evidence volume, refresh cadence, agent workload, enterprise controls, and support required to maintain trust

01 data surface

source count, type, access

02 compute + storage

refresh speed, retention, throughput

03 agent execution

workflow count, run volume

04 integrations + governance

systems, approvals, controls

05 decision science services

setup, tuning, operating cadence

you bring one decision to improve

the market question, current evidence, workflow owners, and the cadence at which the decision must move

we map shape + scope levers

the starting shape, data capacity, agents, integrations, governance, and decision-science operating support

you receive a written pricing architecture

implementation scope, operating model, usage assumptions, responsibilities, expansion path, and commercial structure

03 receive your architecture

leave the conversation knowing exactly what happens next

heptaloop returns a concrete architecture that connects platform access, implementation work, usage, governance, and operating support—without hiding the real drivers behind a generic seat price

request pricing architecture
clear answers

what teams ask before choosing a starting shape

the useful question is not “how many seats?” it is “which decision loop should exist first, what must feed it, and what operating support keeps it trusted?”

is heptaloop priced per seat?

no user access matters, but the engagement is primarily shaped by services, usage, data, storage, compute, integrations, governance, and agent workflow depth

can we start with one loop?

yes many engagements begin with one decision loop, then expand once the evidence model, scoring logic, and action workflow prove useful

what expands scope?

new data sources, faster refresh cadence, more markets, additional agent workflows, deeper integrations, larger evidence stores, and more decision-science operating support

why include decision scientists?

heptaloop agents are maintained and supported by decision scientists so the system produces trusted decisions, not just automated outputs

why no fixed public price table?

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

next step

bring one decision loop. we will map the scope

share the market problem, data surfaces, workflow owners, and decision cadence. heptaloop will return a service-first pricing architecture matched to the implementation

request pricing architecture explore platform loop