Using AI to Automate Customer Context and Delivery Workflow Integration: Practices and Insights Needed

·Aug 06, 2026 08:17 AM(edited)·

Hey all, quick question. I’m currently building internal systems that pull customer context from calls, CRM notes, proposals and other tools, then use AI to turn it into briefs, tasks, next steps and checks for delivery teams, while also helping them execute tasks. For anyone working on something similar, how are you handling it today? Is AI already part of the delivery workflow, or is someone still piecing the context together manually? Happy to compare notes with anyone dealing with this right now.

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12 comments

· Sorted by Oldest

Nick W.
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Rox does similar stuff.
Stefan M.
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Yeah, there’s overlap in how the context is collected. My focus is what happens after the sale: using that context as an input to the work itself. For example, once a marketing agency closes a client, the system can analyse emails, meeting transcripts, internal notes, the client’s website and social channels, then turn that context into something the delivery team can use immediately. It can prepare prototype visuals, video ad briefs, campaign structures and other execution-ready materials without the team rebuilding the client context from scratch. Or in real estate, after an agent speaks with a buyer and understands their budget, preferences, priorities and constraints, that knowledge can be used to find the most relevant properties with one click instead of starting a new search every time. That’s the layer I’m working on. Have you used Rox yourself?
Nick W.
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Have not used it myself but I know of them. I saw a post on linkedin that the way they make it economical (instead of just being an AI-wrapper) is once data is pulled from sources, they host that context in a cache/data layer, whereas a user building with Claude will rack-up tokens, their infrastructure manages that cost better. Something like that
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Tolu A.
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I built an agent for this using Claude code. Cost wise, it's more effective for us as we set up a cadence for it to run.
Stefan M.
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(edited)·
Tolu A. Once the agent has the context, what part of the actual delivery does it handle for you?
Yash K.
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Have used chetto.ai for the same it's pretty good.
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Tolu A.
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Stefan M. decides what the next steps should be on that deal, creates one or two tasks (max. 2 per deal to avoid clutter) on the deal. Depending on the stage the deal is in, if a nudge or response is pending or delaying movement, it sends a message to the internal POC via email and slack wrt this.
Stefan M.
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Tolu A. What kind of business is this built for? Have you considered using that same context to complete whole or part of the work itself, rather than only deciding the next steps and creating tasks?
Tolu A.
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What other part?
Stefan M.
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I mean extending the same context past closed-won. For example, turning the deal history into an onboarding brief, implementation plan, customer-specific tasks and checks for the CS or delivery team.
Tolu A.
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Oh it does this as well. We use it for deal intelligence and knowing when there's an upsell opportunity. Lots of moving parts to it but wanted to only cover your initial question with my response
Stefan M.
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Got it. What kinds of companies is this built for, and what do they typically sell? A couple of examples would help me understand the post-sale side better.

Nick W.
·
·
Rox does similar stuff.
Stefan M.
·
·
Yeah, there’s overlap in how the context is collected. My focus is what happens after the sale: using that context as an input to the work itself. For example, once a marketing agency closes a client, the system can analyse emails, meeting transcripts, internal notes, the client’s website and social channels, then turn that context into something the delivery team can use immediately. It can prepare prototype visuals, video ad briefs, campaign structures and other execution-ready materials without the team rebuilding the client context from scratch. Or in real estate, after an agent speaks with a buyer and understands their budget, preferences, priorities and constraints, that knowledge can be used to find the most relevant properties with one click instead of starting a new search every time. That’s the layer I’m working on. Have you used Rox yourself?
Nick W.
·
·
Have not used it myself but I know of them. I saw a post on linkedin that the way they make it economical (instead of just being an AI-wrapper) is once data is pulled from sources, they host that context in a cache/data layer, whereas a user building with Claude will rack-up tokens, their infrastructure manages that cost better. Something like that
👀1
Tolu A.
·
·
I built an agent for this using Claude code. Cost wise, it's more effective for us as we set up a cadence for it to run.
Stefan M.
·
(edited)·
Tolu A. Once the agent has the context, what part of the actual delivery does it handle for you?
Yash K.
·
·
Have used chetto.ai for the same it's pretty good.
👀1
Tolu A.
·
·
Stefan M. decides what the next steps should be on that deal, creates one or two tasks (max. 2 per deal to avoid clutter) on the deal. Depending on the stage the deal is in, if a nudge or response is pending or delaying movement, it sends a message to the internal POC via email and slack wrt this.
Stefan M.
·
·
Tolu A. What kind of business is this built for? Have you considered using that same context to complete whole or part of the work itself, rather than only deciding the next steps and creating tasks?
Tolu A.
·
·
What other part?
Stefan M.
·
·
I mean extending the same context past closed-won. For example, turning the deal history into an onboarding brief, implementation plan, customer-specific tasks and checks for the CS or delivery team.
Tolu A.
·
·
Oh it does this as well. We use it for deal intelligence and knowing when there's an upsell opportunity. Lots of moving parts to it but wanted to only cover your initial question with my response
Stefan M.
·
·
Got it. What kinds of companies is this built for, and what do they typically sell? A couple of examples would help me understand the post-sale side better.