AI Use Case
Designed reference workflow. This guide explains how Krastor would scope and govern the use case. It is not presented as a live client result.
Written and reviewed by Greyson Jones · August 22, 2026
Recognition points
The production workflow
01
Collect the inquiry once, preserve first-touch and last-touch attribution, normalize the company and contact fields, and create or update one durable lead record. Duplicate rules should prefer a known record instead of creating a second sales history.
02
Use approved qualification criteria to identify the stated operational problem, implementation intent, probable urgency, and missing information. The model recommends a classification; deterministic rules control required fields, routing boundaries, and prohibited outcomes.
03
Assemble an answer from approved service language, availability, proof, and next-step policy. AI drafts in the company voice. High-risk claims, pricing commitments, contract terms, and regulated advice stay outside the autonomous path.
04
Assign the lead to the correct owner based on need, geography, account relationship, capacity, or expertise. The owner sees the source, summary, transcript, recommended response, and the reason for the route—not an unexplained score.
05
Create the next task and time boundary after every interaction. Follow-up can pause when the prospect replies, escalate when a high-intent opportunity is idle, and stop when consent, status, or owner policy requires it.
06
Connect booked meetings, qualified opportunities, losses, and closed work back to the original query, channel, page, and qualification pattern. Use outcomes to improve routing and copy; do not silently train on private communications without an approved data policy.
Connected systems
Controls
Success measures
Implementation path
Document every inbound source, owner, status, response rule, exception, and handoff. A production design starts with the current failure modes rather than a chatbot demo.
Start with one channel and one buyer journey—for example, website diagnostic requests—so response quality, delivery, attribution, and escalation can be proved before expansion.
Provide service definitions, fit criteria, operating hours, proof, next-step rules, and examples. Define who owns each source and how updates reach the workflow.
Compare AI recommendations with human decisions before allowing any message to send. Move low-risk steps to automation only after acceptance criteria are met.
Monitor response quality, delivery failures, routing errors, consent, and business outcomes. Review exceptions with sales leadership and expand only where the measured path is reliable.
Questions owners and implementation teams ask
It can automate bounded, approved responses, but it should not make unrestricted pricing, legal, suitability, or contractual commitments. The safe boundary depends on the business, the channel, and the consequence of being wrong.
Usually no. The implementation should make the CRM more reliable by creating one record, updating ownership and stages, and attaching the conversation and next action. Replacement is considered only when the current system is the actual constraint.
It needs approved qualification criteria, services, policies, proof, voice, routing rules, availability, and access to the minimum customer and workflow context required for the task. It does not need unrestricted access to every company system.
Start where response delay and follow-up loss affect valuable demand and where one accountable owner can review outcomes. One inbound path with clear success measures is a better first production use case than every channel at once.
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