01
Input
Lead and profile the perfect customer.
domainindustrysizehistory
Sales of B2B
AI agent for lead research and qualification
Company research, fit scoring and sales note prepared before contact.
Solution mapSmartCodeIT system view
WebsiteCRMPublic profiles
Research Agent
Validation, logs and exception checking
Source NoteReasonable ScoringTrader's job
What is blocking the process?
Salespeople spent time manually checking websites and CRM history.
How do we organize our work?
The agent collects context, scores fit and prepares a concise CRM note.
What does this scenario mean for the company?
AI-agent for research and lead qualification is a pattern of implementation for companies that want to remove a specific waste of time from the process: Traders wasted time checking companies, LinkedIna, web pages and contact history before the first conversation. It shows a possible way of ordering work and effect to measure: 30 s to the finished note before contact. A similar range should be started with audit, MVP or integration of one key stage.
Decision context
When does such a system make sense?
The agent should prepare the context and priority rather than prejudice the value of the company on its own. It works best on a clearly defined ICP, approved sources and scores that can be explained to the trader.
Best match
B2B sales teams that spend a lot of time on repeatable research before first contact.
Signals to be analyzed
- research of the same company is performed repeatedly
- Lead scoring depends solely on your intuition
- CRM does not contain the context needed before the interview
Solution architecture
The system is divided into clear layers of responsibility.
Each layer has its own data, rules and control points. This allows you to develop the solution in stages without mixing user interface, process logic and integration.
02
Research
Controlled context collection.
Websourcescitationsdate
03
Evaluation
Scoring with open criteria.
ICPsignalsriskscertainty
04
Sales
Note, task and feedback.
CRMbrieffollow-upscore
How can this be built technically?
Technically such a system can be built as a combination of application layer, automation and data integration. In this scenario key elements are: CRM, AI-agent, Web research, E-mail. Workflow includes: New lead goes to CRM -> Agent takes context -> Scoring evaluates potential -> Note goes to the trader. Implementation requires field mapping, validation of data, error handling, activity history, permissions and monitoring to make the process stable after production startup.
System elements
CRMAI agentWeb researchE-mailSales Panel
Scope of first implementation
AI-agent researchLead scoringNotes in CRMQuestion templatesAutomatic tasks
Operational flow
From input event to controlled result.
The process is designed so that each step has a status, owner, and predictable error handling.
- 01Stage 1New lead goes to CRM
AI-agent research
- 02Stage 2Agent downloads context
Lead scoring
- 03Stage 3Scoring evaluates potential
Notes in CRM
- 04Stage 4The note goes to the trader.
Question templates
- 05Stage 5The system creates a task
Automatic tasks
Operational change
What should change after implementation?
These are target process changes, not a guarantee of business outcome. The actual impact depends on data, scale, integration and how the team works.
Current state
Manual browsing of pages
Target state
Note with links to sources
Current state
Subjective order
Target state
Scoring with a clear justification
Current state
Contact only
Target state
Background, hypotheses and next step
Control and security
Risks are part of the project, not a post-implementation note.
Automation should stop or escalate a case when data is incomplete, integration returns an error, or a decision requires human responsibility.
Risk
Out-of-date or incorrect public data
Control mechanism
Date of download, source link and level of information certainty.
Risk
Discriminating scoring
Control mechanism
Only business criteria, review of human rules and decision.
Risk
Automatic contact without acceptance
Control mechanism
The agent prepares the material and launches the sequence is subject to authorisation.
Recommended starting point
Research for one ICP segment
We compare time of research, completeness of data, compatibility of scoring with the assessment of traders and utility of notes.
Approximate time2-4 weeks
one lead sourcea set of criterianotewriting to CRM
Related paths
Services matching this scenario
Implementation FAQ
Questions worth asking before pricing.
The answers describe a safe technical option. The exact scope depends on your company's systems, data and exceptions.
Can an agent use LinkedInn?
The scope depends on legal access, source rules and available API. The project should not be based on circumvention of platform restrictions.
Does AI scoring replace a trader?
No, he's supposed to organize the queue and show the arguments, but it's up to the team to make contact and qualify.
How to avoid hallucinations in research?
Each important information should have a source, date of download and uncertainty status, and the lack of data cannot be replaced by a guess.
Related projects
See scenarios with similar architecture.
The selection is based on common services and system elements, thanks to which subsequent examples develop the topic rather than creating a random list.
Next step
Do you have a similar process, but different systems and exceptions?
Describe the current workflow, data sources, and where the process stops. The first conversation is used to assess whether the right start is an audit, integration, MVP or a ready-made tool.
Contact
Describe the process you want to improve.
A few concrete sentences are enough for us to suggest an audit, automation, an AI agent, a web application or a systems integration.
A reply with a recommended next stepThe option to begin with an audit or a focused MVPA solution designed for security and maintainability
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