Market footprint
Category, segments, geography, competitive alternatives, market size, and where the company fits.
Company claims + independent market evidenceCCC CUBE / RESEARCH INFRASTRUCTURE
The context harness
for AI ABM.
A persistent context model for every account. A focused context pack for every request.
CCC Cube connects company research, cluster patterns, and weighted evidence to the account, buyer, and task your agent is working on. The research stays available across your entire ABM workflow.
Your model. Your workflow. A shared research layer.
FINDING SIGNAL
ACCOUNT → CLUSTER
PARTICLES → PATTERNS → CONTEXT
BUILD WITH CCC CUBE
Put deeper context behind
your next campaign.
Request early access for your team. Tell us what you want to build.
Request early access Opens a short Google Form in a new tab.
01 / THE HARNESS
Every company gets
its own context model.
For each account in scope, connect what the company sells, how it operates, how it presents its market, and what supports a credible argument. Keep that evidence available for the next buyer, campaign, and model.
We call this the context cube.
REPEATED FOR EACH ACCOUNTHarness design / evidence requirements
Product & operating model
Products, use cases, pricing, workflows, integrations, dependencies, and technical constraints.
Product pages + documentation + case studiesGrowth & organization
Headcount observations, hiring direction, funding, expansion, and changes across dated snapshots.
Careers + announcements + available licensed dataBuyer & decision context
Relevant roles, operating stakes, adoption friction, alternatives, and the proof a decision would require.
Observed statements + separately labeled hypothesesLanguage & positioning
Vocabulary, technical depth, category framing, argument structure, and the language used to describe value.
Messaging + articles + public commentary when availableProof & quantitative standards
Metrics, denominators, baselines, benchmark methods, skepticism, counterexamples, and limits.
Evidence for the argument + evidence against itVisual & communication patterns
Diagrams, interfaces, charts, visual density, and examples that help explain a technical or economic idea.
Annotated references + separate seller brand rulesSeller-to-account relevance
The connection between an observed situation, your product’s actual mechanism, and a defensible next step.
Approved seller claims + account-specific evidenceThe prototype collects public website text, stores source-backed findings, and compiles task context. Evidence availability varies by account. Licensed enrichment, social-feed collection, automatic visual analysis, and scheduled refresh are planned. Collection and synthesis runs are explicit today.
02 / SELECT THE CONTEXT
Account signals.
Task-specific weights.
The same research supports different arguments. CCC Cube selects the evidence, vocabulary, proof requirements, and product connections that matter for the buyer and task. Your model receives a focused set of instructions for what to emphasize, what to substantiate, and what it cannot claim.
ILLUSTRATIVE WEIGHTING / OUTBOUND EMAIL
One research cube. Different priorities for each buyer.
| Research dimension | Engineering | Finance | Product |
|---|
Each column sums to 1.00. These illustrative values show how emphasis can change by buyer when evidence is available across all eight dimensions. They are not model confidence or learned parameters. Actual selection depends on the request and available evidence. Changing the task also changes the format, length, and visual guidance.
03 / CONTEXT FOR YOUR MODEL
One context model.
Different creative tasks.
Your model receives more than background research. Each task has a specific writing contract: the argument to develop, the evidence it can use, the format to return, and the claims to avoid.
EXPLORE A CONTEXT CUBE
Interactive Example
Change the buyer and task to see how the same research changes the message. Hover over a headline or highlighted phrase to inspect the reasoning and sources.
One fictional scenario: Aster sells workflow cost analytics to Northstar, a coding-agent platform. The company stays fixed so you can see what the buyer and task change.Same company. Same source research. A different argument for each buyer and communication.
model.output / preview
Example Output
Hover, focus, or tap any headline or highlighted phrase for its context report. Blue shows added wording; struck-through text belongs to the previous version.
BEFORE
AFTER
CONTEXT APPLIED
Both versions are authored illustrations, not live model outputs or a benchmark. Highlights compare wording; they do not measure quality. Weight values indicate emphasis, not confidence.
Inspect the example’s research and full model input
SELECTED CONTEXTCOMPILED MODEL INPUT
WHAT WOULD MAKE THIS CREDIBLE
WHEN THIS ARGUMENT BREAKS
EXPRESSION / WITH REASONSHow the argument should sound.
How the argument should sound.
What the visual should explain.
Audience conventions inform the explanation. The seller keeps its own voice and identity.
USE THE ACCOUNT’S VOCABULARY
Authored diagram concept. No captured brand assets or measured results.
PROVENANCE / SCOPE / LIMITSEvery argument has a source.
Every argument has a source.
Every source has a boundary.
All excerpts below were authored for this fictional example. They demonstrate attribution, not real company research.
STILL UNKNOWN
WHAT YOUR MODEL RECEIVES / JSON
This is the research and writing guidance attached to the model’s request. It combines this account’s evidence, relevant cluster patterns, buyer assumptions, language, proof requirements, and claim limits. The model uses it to create the communication. JSON is simply the structured format that carries those instructions between tools.
INSTRUCTION SENT WITH THE RESEARCHFictional example. Your chosen model generates the final communication.
Seller truth stays fixed Inferences stay labeled Unknowns stay visible Claims carry limits
Fictional seller, accounts, excerpts, and buyer hypotheses. The controls compose example packs locally; they do not query a live cube or run a model. The schema illustrates the intended contract. Current retrieval uses task-weighted lexical matching and source diversity; configurable buyer weighting and automatic visual analysis remain in development.
Connect through MCP or structured context
The prototype supports read-only retrieval through a local MCP stdio server and JSON / Markdown exports. Hosted workspaces, remote MCP, and API access are planned.
source_library_context
{
"cube_id": "CUBE_ID",
"cluster_id": "CLUSTER_ID",
"task": "outreach",
"objective": "Evaluate lower operating cost",
"angle": "Cost per successful workflow",
"channel": "email",
"audience_role": "Engineering leader",
"buying_stage": "evaluation",
"max_chars": 12000
}CONTEXT RESPONSE
Seller truth · relevant evidence · audience findings · claim rules · sources · coverage
Example arguments for an implemented local tool. Replace IDs with values from your workspace. This example does not make a request.
Research compounds
Account evidence stays available across buyers, channels, and future requests.
Specificity survives
A cluster pattern never becomes a fact about every company in the cluster.
The argument is inspectable
Connect the situation, product mechanism, possible benefit, and proof required.
Your model gets a contract
Give it the context to reason with and clear requirements for the output.
04 / MEASURED IN THE PROTOTYPE
Better context.
A stronger first draft.
Astra with CCC Cube context was preferred in 41 of 48 ad and email comparisons against the same model with ordinary campaign inputs.
Read the test design85%
of paired drafts favored cube-assisted output
41 cube wins4 ties3 order-sensitive
Internal, same-model review across 12 accounts and two hypothesized buyer roles. Baseline had no browsing. Draft preference is not a measure of campaign lift.
Method, results, and limits
One account was selected per cluster before generation. Both conditions used gpt-6-astra, the same seller facts, assignment, output format, and model settings. The cube condition added existing cluster packs and indexed account passages. First drafts were retained.
Reviews hid condition labels and reversed candidate order. A win required at least two total points across specificity, buyer relevance, argument quality, and clarity in both orderings. Major unsupported claims vetoed quality wins.
| Format | Cube wins | Baseline wins | Ties | Order-sensitive |
|---|---|---|---|---|
| Ads | 17 | 0 | 4 | 3 |
| Emails | 24 | 0 | 0 | 0 |
The 48 comparisons are related observations from 12 accounts. This test does not isolate clustering from account research, compare a browsing research agent, or establish performance across other models. The cube used about 9.5 times as many generation input tokens. Independent review and campaign testing remain ahead. Evaluation date: 30 October 2026.
CCC CUBE / PRODUCT PREVIEW
Build on a market
your model can understand.
Explore how company evidence becomes focused context for your marketing agents.
Request early access Opens a short Google Form in a new tab.