DarkAgent New
Context matters. Put it to work.
DarkAgent brings your customer data, the market around it and DarkMath’s identity and audience intelligence into one workspace. Ask a business question, see the evidence, and move to a supported next step with the controls you set.
DarkAgent is DarkMath’s business-facing intelligence and workflow platform. It combines your customer data, relevant market context and DarkMath’s identity and audience services in one guided workspace, where business users describe an objective, review the evidence and move to supported actions with clear controls and measurable results.
Your data tells part of the story.
Your systems know who bought, who responded and what happened inside your walls. The next opportunity often depends on what they can’t show on their own: the same customer split across four records, a market moving faster than you, or a pattern that only appears when your data meets someone else’s.
DarkAgent is built for those questions. It starts with marketing, sales and customer intelligence, with automotive as the first proving ground.
| The question | What DarkAgent returns |
|---|---|
| Are we gaining ground on our market? | A market comparison with share, trend and where the gap is concentrated |
| Which customers or prospects deserve attention now? | A ranked audience with the selection logic and the evidence behind each signal |
| What are we missing? | Households and segments your current audience doesn’t reach |
| What should we do next, and why? | A recommended action with audience, channel, timing, cost and expected impact |
| Did it work? | An outcome report tied to the original audience, recommendation and action |
How DarkAgent works
Every session follows the same six steps, so you can always see what it assumed, what it used and what it is about to do.
DarkAgent coordinates database queries, statistical methods, DarkMath services and language models behind the scenes. The interface shows you the business answer and the evidence, not the plumbing.
Understand the objective
The business question, the success measure, the geography, the time period and the constraints.
Establish the data context
Which sources are available, how fresh and complete they are, what you are permitted to use, how well identities are resolved and how each metric is defined.
Analyze and explain
Queries and models run near the data. DarkAgent shows the evidence and any material limits.
Recommend a next step
The opportunity, who qualifies, and why the recommendation follows from the evidence.
Prepare and execute
Preview the audience, destination, message, cost and consequences. Your permissions and approvals decide what runs.
You approve before anything leavesMeasure and improve
Responses and outcomes connect back to the original audience, recommendation and action.
Try it on a synthetic dealership
Pick a question. Switch modes to see how the same recommendation behaves in Analyze, Prepare and Execute.
Pick a question
Files loaded: CRM, DMS, inventory. Market context: registrations and new movers.
Choose a question to see how DarkAgent works through it: objective, data check, evidence, finding, recommendation and a prepared action.
Three modes. Your controls.
Start read-only. Turn on more as trust is earned.
Analyze
Answers, charts, market comparisons and audience counts. Nothing is staged or sent.
Prepare
DarkAgent stages audiences, messages, orders and budget changes as drafts for a named person to review.
Execute
Supported actions run within the permissions, limits and approvers you set, and every run is logged.
- Recommended, prepared and completed actions always carry different statuses
- Failures are visible, and retries never duplicate an order, a message or a charge
- Permissions apply at the data-source level and at the action level
What teams do with it
Read performance in context
Compare your results with your market, period and customer group. See whether growth is yours or the market’s.
Find the next audience
Define the people or businesses that fit an objective, with the selection logic, exclusions and count date on every list. Extend it with DarkMirror.
Clean and connect your files
Upload CRM, DMS and inventory files. DarkAgent detects, cleans, links and enriches them with DarkMatch: dedupe, household, standardize, update contacts and embed.
Give sales a stronger start
Combine product fit, timing signals, relationship paths and evidence into a short brief and a drafted message. Nothing sends without a person’s approval.
Count, order and deliver
Get counts with contact coverage on every list, such as how many records carry an email, then order, append and deliver through supported channels.
Prove what worked
Tie responses and sales back to the audience and action, with holdouts when the question calls for an experiment.
Built on DarkMath
DarkAgent is the front door. The rest of DarkMath does the specialist work behind it.
| Component | Responsibility |
|---|---|
| DarkMatch | Resolves fragmented identities and duplicates, where validated |
| DarkAgent | The business interface: analysis, recommendations and workflow coordination |
| DarkMirror | Semantic audience modeling and expansion |
| Cognitive Data Graph | Connects identities, households, attributes, events and outcomes, each with source, time, confidence and permitted use |
| DecisionLinks | Licensed reference data, industry expertise and established activation services, where relevant |
It gets sharper with use. Here is exactly how.
“Learning” means three different things, and they deserve different rules.
Business memory
Your approved definitions, preferences, constraints and past decisions: what counts as a customer, your market area, your fiscal calendar.
Workflow feedback
Corrections, accepted recommendations, execution results and business outcomes, captured as you work.
Model improvement
Evaluated updates to models or configurations, tested on held-out cases and reversible.
What learning is not. A conversation does not automatically retrain a model, and an accepted recommendation is not proof of a result. Outcomes are measured before anything is called a win.
Your advantage stays yours. Client-specific definitions, relationships, feedback and derived assets remain under your ownership and agreed usage rights. Nothing is reused across clients or used to train shared models without an explicit agreement.
Bring the AI to the data.
DarkAgent runs queries and processing next to the data, then reasons over bounded results. That is how it answers questions across tables with millions or billions of rows without moving them.
Behind the answers sits licensed reference data operated by DecisionLinks, including about 1.0 billion persistent IDs, 1.1 billion emails, 500 million phones and 310 million VINs, used only where your question needs it and your use is permitted.
Defined for every deployment
- Tenant separation, encryption, retention and deletion
- How each model provider may handle your data
- Processing locations and audit records
- Hosted, in your environment, or dedicated clean room
- Cost measured per completed task, not per token
Where DarkAgent fits
Plenty of tools answer questions about data you already have. DarkAgent is built for questions that need identity you haven’t resolved and context you don’t hold yet. Many teams use it alongside the tools below.
| Analytics agents | CRM and CDP agents | DarkAgent | |
|---|---|---|---|
| Starts from | Your warehouse tables | Your CRM or CDP profiles | Your data, resolved by DarkMatch |
| Identity | As stored | Profile merge rules | Semantic resolution with confidence and sources |
| Outside context | Bring your own | Connected apps | Licensed reference and market data, where permitted |
| Audiences | Query results | Segments inside the platform | Counts, contact coverage, orders and delivery |
| Regulated data | Depends on your setup | Depends on your setup | Handled under FCRA, DPPA and state privacy rules by a registered data broker |
Watch the workspace
A two-minute walkthrough of the beta: upload, link records, review confidence, then ask questions in plain language.
DarkMath agentic platform demo (beta), about two minutes.
DarkAgent questions
What is DarkAgent?
DarkAgent is DarkMath’s business-facing intelligence and workflow platform. It brings your customer data, relevant market context and DarkMath’s identity and audience services into one workspace where you describe an objective, review the evidence and move to supported actions with clear controls.
Do we need DecisionLinks data?
No. Start with the data relevant to your use case. DecisionLinks and other licensed sources can add enrichment or market context where a question needs it and your use is permitted.
Will DarkAgent replace our existing systems?
No. DarkAgent works with your CRM, DMS, CDP, warehouse and activation tools. Data access, integrations and supported actions are scoped for each engagement.
Can it take actions automatically?
Only the actions you allow. DarkAgent runs in three modes: Analyze, Prepare and Execute. You define which actions are supported, the limits that apply and who approves them. A recommended action and a completed action always carry different statuses.
Does DarkAgent learn from our data? Does it train shared models?
It improves through approved business definitions, your feedback and measured outcomes. A conversation does not retrain a model. Your definitions, feedback and derived assets stay under your ownership, and nothing trains a shared model without an explicit agreement.
How much data can it work with?
DarkAgent runs queries and processing next to the data and reasons over the results, which is how it works across tables with millions or billions of rows. We confirm volumes, response times and costs for your workload during evaluation.
Which AI models does it use?
DarkAgent is model-agnostic. It routes each task to a suitable model, uses code for math and counts, and measures cost per completed task rather than raw token use. Specialized or fine-tuned open models are used where evaluation shows they help.
How do we get started?
Bring a business question. We set up a focused demonstration or evaluation around the data it needs, the output you expect and the measure of success.
Bring us a question your team needs answered.
We will set up a focused walkthrough around the data it needs, the output you expect and how you will measure success.