step 1
select.
day 1
choose the workflow with the highest owner pain and the lowest trust risk. if the workflow is vague, it gets reduced until it is buildable.
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thailand / bangkok / ai ops sprint
ai ops sprint thailand.
a 5 to 10 day implementation sprint for one real business workflow: inbox triage, line oa routing, crm follow-up, finance admin, dashboards, or internal tooling.
an ai ops sprint is a short implementation engagement that maps one business workflow, builds the first working automation, documents the handoff, and leaves the owner with a maintainable system. it is not an ai workshop, not a slide deck, and not a chatbot demo. dlvx builds the workflow into the tools thailand businesses already use: line oa, email, google workspace, microsoft 365, hubspot, pipedrive, xero, flowaccount, shopify, shopee, and internal spreadsheets.
map one workflow01 . the wedge
thailand has ai awareness. the gap is implementation.
Most businesses in Thailand have seen the ai demo already. The owner has a ChatGPT or Claude account, the team has screenshots from a roadshow, and the tools are still disconnected on Monday morning. Leads sit in Line OA, invoice questions sit in email, the crm is stale, reporting lives in spreadsheets, and the owner is still the router.
An ai ops sprint closes that specific gap. It takes one workflow that already burns hours and turns it into a supervised operating layer. The sprint does not try to automate the whole company. It builds the first useful automation, makes the approval points explicit, and leaves documentation that a real team can run after handoff.
The first workflow is chosen for leverage, not novelty. Strong fits include inquiry triage, quote follow-up, stale lead nudges, customer support drafting, daily exception reports, finance admin prep, supplier inbox routing, and turning a messy spreadsheet into a lightweight internal app.
The point is production proof. Every sprint ends with a working workflow, a data flow map, an approval policy, a rollback path, and a next-workflow list. If the first workflow does not justify wider automation, the sprint stops there.
02 . sprint outputs
what gets built in the sprint.
01
workflow map. the current path from inbound signal to owner decision, with every manual handoff, approval point, tool, and failure state made visible before code starts. see automation context→
02
automation layer. one working layer that drafts, routes, classifies, enriches, or summarizes operational work inside the tools already used by the business. see ai integration→
03
approval queue. a human approval checkpoint for customer-facing replies, payments, pricing, legal language, and anything that can damage trust. see custom apps→
04
business dashboard. a small dashboard or report view showing what the automation saw, what it drafted, what was approved, and what still needs an owner. see build work→
05
governance pack. pdpa-aware data map, prompt boundaries, retention notes, escalation rules, and a practical handoff document for the team. see pdpa page→
06
next workflow backlog. a ranked list of the next automations to build, based on observed time saved and risk, not vendor hype. see service→
03 . proof
this is built from live operating system work.
04 . method
five steps, one workflow, no theatre.
step 2
map.
day 1 to 2
document trigger, data sources, decision points, exception states, approval rules, and the tools involved.
step 3
build.
day 2 to 6
wire the automation, prompts, storage, dashboard, approval path, and notifications into the existing stack.
step 4
harden.
day 6 to 9
test edge cases, add logs, make rollback clear, tighten prompts, and write the operating notes.
step 5
handoff.
day 10
train the owner or team on the workflow, measure first savings, and rank the next workflow backlog.
05 . questions
what owners ask before the first sprint.
01
What is an ai ops sprint?
An ai ops sprint is a short build engagement that turns one real business workflow into a supervised automation. It includes mapping, implementation, approval rules, logs, and handoff documentation.
02
How is this different from an ai workshop?
A workshop teaches concepts. A sprint ships one working workflow. The deliverable is not a deck. It is a usable automation inside the tools the business already runs.
03
Which workflow should go first?
The best first workflow is repetitive, frequent, easy to verify, and painful for the owner. Lead triage, quote follow-up, support drafting, reporting, and finance admin prep are strong first candidates.
04
Can this work with Line OA?
Yes. Line OA is one of the highest-value Thailand channels because many customer conversations already start there. The sprint can classify, draft, route, and log Line conversations while keeping a human approval step.
05
Is this safe under PDPA?
The sprint includes a practical data flow map, retention notes, prompt boundaries, and human approval checkpoints. Sensitive data is minimized, and customer-facing messages stay under human control.
06
What happens after the first workflow?
The sprint ends with a ranked backlog of the next automations. If the first one saves time and behaves reliably, the next sprint can expand the system without rebuilding from scratch.
06 . related
build the operating layer around it.
one workflow is enough to start.
map the first ai ops sprint.
send the workflow that keeps landing back on the owner. dlvx turns it into a scoped sprint, builds the first working automation, and leaves the system documented enough to run without theatre.