Capability example · AI Agents & Automation
Illustrative build · not a client engagement
From scattered prospect research to a sales intelligence system
An illustrative build, not a client engagement. It shows how Stallwart approaches AI lead intelligence: an agentic system that discovers businesses, researches them, detects buying signals, and produces sales-ready intelligence a team can act on.
- 01Business discovery
- 02Web research
- 03AI classification
- 04Signal detection
- 05Enrichment & scoring
- 06CRM-ready output
- Domain
- B2B services and commercial intelligence
- Problem
- Prospect research is manual, shallow, and inconsistent, so reps act on thin signals.
- Approach
- An agentic pipeline: discover, research, enrich, classify, score, and prepare CRM-ready output.
The problem
Most prospecting runs on a rep with browser tabs open. They find a company, skim the site, guess at fit, and move on. The research is shallow because it is manual, and it is inconsistent because every rep does it differently. Buying signals that would change the priority of an account go unseen because nobody has time to look for them at scale.
The result is a pipeline ranked by whoever was researched most recently rather than by who is actually worth a conversation. An illustrative system shows how that work becomes an automated, consistent process.
What was assumed
Better outreach needs more reps.
What Stallwart asked
What if research and scoring ran as a system, and reps spent their time on the accounts it surfaced?
What Stallwart built
This illustrative system discovers businesses that match a target profile, then researches each one's public digital presence: what it does, how it positions itself, and what it has changed recently. AI classification turns that unstructured research into structured attributes, and signal detection flags the events that matter, such as hiring, launches, or shifts in positioning.
Prospects are enriched and scored against the profile, so the pipeline is ranked by fit and timing rather than by recency. The output is prepared as CRM-ready intelligence with the context a rep needs to open a relevant conversation, including a first draft of personalized outreach for a human to approve.
- 01
Make research structured, not anecdotal
Unstructured web research is only useful at scale once it is turned into structured attributes an engine can rank. AI classification does that consistently across every account, so two prospects are compared on the same basis.
- 02
Score on fit and timing, not recency
A signal like a recent hire or launch changes an account's priority. Combining fit with detected signals ranks the pipeline by who is worth a conversation now, rather than who happened to be researched last.
- 03
Keep a human on anything that is sent
The system prepares outreach; it does not send it. A rep approves or edits, so personalization stays accountable and the brand's voice stays intact.
The pipeline
Discovery to CRM-ready intelligence.
- 01
Business discovery
Companies matching the target profile
- 02
Web research
Public digital presence, read per account
- 03
AI classification
Unstructured research into structured attributes
- 04
Signal detection
Hiring, launches, positioning changes
- 05
Enrichment & scoring
Ranked by fit and timing
- 06
CRM-ready output
Context + drafted outreach for human approval
An illustrative architecture. Each stage is automated; a human approves outreach before anything is sent.
Engineering decisions
Agentic research with tool use, not a single prompt
Discovering and researching a company is a sequence of steps and lookups, which suits an agent that calls tools, not one model call. It makes the research auditable and each step replaceable.
The trade-off
More moving parts to run and observe than a single call, which is why evaluation and logging are part of the build.
Human-in-the-loop on outreach
Automated sending at scale is how a domain's reputation gets burned. Preparing drafts for approval keeps the throughput while a person owns what goes out.
The trade-off
A rep stays in the loop, so this augments outreach rather than fully replacing it.
This work connects to
- AI Agents & Automation
- AI sales automation
- Lead enrichment and scoring
- Agentic AI
- Workflow automation
Frequently asked
What is AI lead intelligence?
AI lead intelligence is the use of AI agents to discover businesses, research their public digital presence, detect buying signals, and enrich and score prospects, turning manual prospect research into a consistent, ranked, CRM-ready process.
How is this different from buying a contact list?
A list gives you contacts. A lead-intelligence system gives you researched, scored accounts with the context and signals behind the ranking, so reps know why an account matters and when to reach out, not just who to email.
Does the system send outreach automatically?
In this illustrative design, no. It prepares personalized outreach and the supporting context, but a person approves or edits before anything is sent. That keeps throughput high without putting the brand's reputation on autopilot.
Is this a real client case study?
No. This is an illustrative capability example that shows how Stallwart approaches AI lead intelligence and sales automation. It is not a completed client engagement, and it contains no client results.
Want prospect research to run as a system, not a browser tab?
Tell us your target profile and your CRM. We will scope the pipeline that discovers, scores, and prepares the accounts worth a conversation.
Last updated: October 13, 2026