From 2 Months to 2 Weeks: Building an Internal Ops Tool with AI Assistance
What we accomplished: By leveraging Claude to kickstart low-code integrations, we automated an audit-sensitive, high-risk process end-to-end with our ops team — without taking a single backend engineer off of roadmap projects.
We put off automating one of the most painful internal processes we had: securely packaging and shipping signed-off files to external partners. It was cross-functional, brittle, and compliance-critical. We knew we should automate it all along — but the cost in time made it off-limits.
That all shifted once we approached AI not as a replacement for coders, but as a bandwidth unlock. Claude helped our ops team create secure, auditable automation in two weeks — not months. No specialized engineers. No ticket backlog. Just one concentrated sprint.
The Challenge: High-Stakes Data, Broken Systems, All-Hands Chaos
We send out “control-version” assets on a regular basis: certified 3D models, signed reports, and finalized documentation. They are passed on to customers and suppliers. They should be the correct version, securely delivered and fully traceable.
The process was as follows:
Find and confirm the most current approved file
Route it through multiple stakeholder sign-offs across compliance, legal, tech, and accounting — typically 3 to 5 approvals, sometimes more
Decrypt the internal copy
Re-encrypt with a recipient-compatible encryption
Deliver it externally, logging every action for compliance
Each step existed in a different environment. Each handoff was a risk. And although we had the technical capability to automate it, we lacked the bandwidth to create and maintain brittle glue code against incompatible APIs.
Why We Waited — And Why We Couldn’t Keep Waiting
We had known automation was the correct long-term bet. But we couldn’t afford the short-term expense:
The APIs were poorly documented and inconsistent
Approvals continued to evolve
Scripts tend to break on small schema changes
We lacked backend resources dedicated to ops tooling
This was not a skills gap but a capacity issue. Historically, the cost-benefit calculation never worked out.
What Changed: AI-Augmented Development of Internal Tools
This year we’ve begun to utilize Claude to jumpstart internal automation initiatives. Not to supplant developers — but to empower time-strapped ops and IT people to create working solutions from scratch.
What that looked like:
Simplifying descriptions of workflows and generating code scaffolds that can be run
Rapidly generating API connectors and transforming messy outputs into clean inputs
Fast debugging through code-aware conversation loops
Empowering non-engineers to prototype safely, test, and iterate
AI did not take away the work. It took away the drag. Overnight, we could approach this automation as a two-week sprint — not a two-month epic.
What We’ve Built: A Fully Automated Audit-Ready Delivery Process
Image generated using AI (DALL·E via ChatGPT)
Here is what the new system does:
🔍 Control Version Detection
Scripts recognize a correct file by its metadata, version labels, and timestamps and mark mismatches prior to review.
✅ Approval Routing
Each shipment triggers an in-line approval flow across the relevant stakeholders. Legal, compliance, and technical leads can review and sign off directly within our internal systems, with full logging and timestamps for audit readiness.
🔐 Encryption & Repackaging
After approval, the document is decrypted internally and re-encrypted into a universal standard we share with our partners. This eliminated all issues related to format compatibility.
📦 Secure Delivery with End-to-End Logging
The process sends the final package through our outbound file gateway. Each step — from version ID to encryption scheme to approver name — is recorded to ensure compliance.
Results
Time to delivery decreased by 80%
The process is fully automated end-to-end
Human errors in versioning and approval are eliminated almost entirely
Compliance audits take minutes these days
We didn’t deploy a new platform. We didn’t pull in a dev team. We just found a way to make better use of the tools — and people — we already had.
Key Lessons to Teams in the Same Position
If you’re sitting on:
A process with high stakes that involves multiple teams and tools
A backlog of automation ideas that never get prioritized
Skilled ops or IT staff with too much to do to “just build it”
…then AI-facilitated development can be your key.
We did not solve this due to AI.
We solved it because AI made the work small enough that we could finally act.
For Leaders: The Strategic Case for AI-Augmented Ops
For ops and product leads who are juggling scattered processes and limited engineering bandwidth, the project revealed an overarching truth: Automation frequently fails not because it’s difficult — but because integration overhead kills momentum.
AI helpers reversed that equation. They provided our ops team with what was needed to deliver secure, compliant automation through simple-language specifications and iterative prototypes. No tickets, no delay.
Begin with whatever is on your bench if you do not have time to repair it. Tooling can finally be moved since it is light enough.
Better > Perfect
This was not an ideal system. It was simply the first one we really shipped. AI was what helped break the inertia. The rest was simply structured iteration.
If your internal tools backlog has started to feel permanent, maybe this is your moment to break it.
