Use AI to resolve draw requests
Automate the repeat path and keep human handoff clear.
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What it handles
Works with
Why it matters
The practical reason to use it.
Manually handling resolve draw requests in WhatsApp is slow, inconsistent, and hard to scale.
How it works
A step-by-step look at the workflow.
Step 1
A visitor starts a conversation in WhatsApp — the agent identifies the intent and begins collecting handoff readiness, data quality, and ownership.
Step 2
The agent checks your knowledge base and Project schedules, Safety checklists, Subcontractor records to determine the right next step.
Step 3
Once enough context is gathered, the agent resolves draw requests while following your policies and approval logic.
Step 4
If the request falls outside the agent's scope, InsertChat escalates to a human via WhatsApp threads with the full conversation summary attached.
Step 5
You review which resolve draw requests conversations resolved end-to-end, where escalation happened, and what rules to tighten for better throughput on the.
Task flow
How the assistant handles repeat work.
Resolve Draw Requests
The agent resolves draw requests in WhatsApp by collecting handoff readiness, data quality, and ownership for resolve draw requests.
WhatsApp coverage
Deploy the same workflow across WhatsApp threads for customers who prefer messaging over forms and portals, so the task starts where users.
Policy-first decisions
Ground responses in approved sources, thresholds, and escalation rules before the agent takes the next step.
System actions and handoff
Once the conversation is ready, InsertChat can move draw requests into the next approved step without manual copy-paste or extra triage.
Accuracy controls
How answers stay accurate.
Grounded in your sources
Responses stay tied to the docs, policies, and structured data your team already trusts for resolve draw requests.
Rules before replies
Use approval logic, routing thresholds, and business rules before the workflow changes status or triggers downstream actions.
Human review when needed
InsertChat hands off the edge cases, exceptions, and judgment calls instead of pretending every conversation should be fully automated.
Visible automation performance
Track which conversations resolved end-to-end, where escalation happened, and what to tighten next for better throughput.
Add next
Useful next automations.
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Extend the workflow beyond site inspections so teams can keep related work moving without rebuilding context in a separate queue.
Handle permit requests
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Process change orders
Extend the workflow beyond change orders so teams can keep related work moving without rebuilding context in a separate queue.
Track subcontractor updates
Extend the workflow beyond subcontractor updates so teams can keep related work moving without rebuilding context in a separate queue.
What you get
The changes teams should notice first.
- Less manual work on repetitive conversations
- Faster resolution without human bottlenecks
- Consistent execution every time, at any scale
- Clear visibility into what gets automated and what doesn't
The facts do the selling
Plan facts, platform capabilities, and worked examples — every claim here is checkable, not a pitch.
White-label included — never a paid add-on. Copyright removal from $98/mo. Full white-label — custom domain, branded portal, your-domain emails — from $198/mo.
The white-label wedge
Platform fact
Training runs on your sitemap, PDFs, docs, and YouTube transcripts. Answers cite the source pages they came from.
Trained on your content
Platform fact
Five clients at $300/mo on a $198/mo Agency plan is $1,300+ of monthly margin before usage.
A 5-client agency on one flat plan
Worked example
Try the FAQ like a visitor.
Open product, pricing, security, integration, and free-tool questions in the same chat your visitors use.
InsertChat
Interactive FAQ
Hey. Pick a question below and see how InsertChat turns FAQs into clear, source-backed answers.
Use AI to resolve draw requests FAQ
Can an AI agent resolve draw requests without human approval?
Yes — you configure exactly which resolve draw requests actions the agent takes autonomously and which require human review. For example, the agent can resolve draw requests while following your policies and approval logic on its own, but escalate edge cases based on thresholds you set. Routine resolve draw requests cases resolve end-to-end while exceptions get flagged for a person to review.
How does the agent know how to resolve draw requests correctly?
The agent is grounded in your knowledge base and Project schedules, Safety checklists, Subcontractor records. It collects handoff readiness, data quality, and ownership for resolve draw requests. The agent should preserve owner, context, and the next approved step before handing anything off. before deciding the next step, and it can move draw requests into the next approved step without manual copy-paste or extra triage. The result should land in the system of record instead of a loose inbox or chat thread. once enough context is gathered. It never improvises — it follows the sources and logic you configure, then keeps the next owner in the loop when the workflow needs a handoff.
What happens when the agent can't handle a resolve draw requests request?
InsertChat hands the conversation to a human via WhatsApp threads with the full context already attached — the user doesn't repeat themselves. You configure when handoff triggers based on confidence thresholds, request complexity, or handoff readiness, data quality, and ownership for resolve draw requests. The agent should preserve owner, context, and the next approved step before handing anything off. that falls outside the agent's scope. The result is a cleaner escalation instead of a dead-end chat.
Does resolve draw requests automation work in WhatsApp?
Yes. The agent resolves draw requests across WhatsApp threads for customers who prefer messaging over forms and portals. The same workflow, knowledge base, and escalation rules apply regardless of where the conversation starts, so the task execution stays consistent at any scale and across every channel you enable.
How do teams measure whether resolve draw requests automation is working?
Teams usually measure resolution time, handoff quality, and how many conversations finish without manual re-entry. If those numbers improve, the workflow is doing real work instead of just deflecting messages. That makes it easier to expand the automation into adjacent steps once the first path is reliable.
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