The 6-Week Deployment That Replaced a 3-Person Reporting Function

A 220-person logistics company was running 14 recurring reports a week. All manual. Three analysts buried in spreadsheets, stakeholders waiting days for numbers that were already stale by the time they arrived.

We mapped the whole operation in two weeks. Built and deployed in four. Here is exactly what we did and what changed.

Weeks 1 and 2: Map before you build

Before touching a single system, we audited every report. Who ordered it. Who actually read it. What decision it was meant to support. What data it pulled and how much manual cleaning happened before it went out.

What we found: four of the fourteen reports were produced weekly but only reviewed monthly. Six pulled from the same underlying dataset but formatted differently for different departments. Two required genuine judgment. The rest were mechanical repetition.

Most companies skip this step. They automate what exists instead of questioning whether it should exist. We do not skip it.

Weeks 3 and 4: The build

We built a centralised reporting infrastructure connected to their WMS, TMS, and finance platform. Data normalisation happened at ingestion. Reports were generated, formatted, and distributed automatically on schedule — the right format to the right person without anyone touching it.

• 12 of 14 reports fully automated
• 2 reports kept human ownership for strategic commentary
• Report generation time dropped from 3.5 hours average to under 4 minutes
• All stakeholders moved to a single live dashboard
• Full historical archive built and indexed for instant retrieval

Weeks 5 and 6: Deployment

Deployment is where most vendors hand over a manual and disappear. We stayed. Every stakeholder who had been receiving manual reports was walked through the new system individually. We handled the edge cases, addressed the resistance, and remained available for two full weeks post-launch.

The three analysts were not replaced. Two moved onto strategic projects that had been sitting on the backlog for over a year because there was never capacity. One stepped into a data governance role the business had needed for a long time but could not justify building.

The real cost of waiting

At fully loaded cost, the reporting function was consuming roughly €180k a year in human capital to produce outputs a well-built system generates in minutes. The deployment paid for itself in under three months.

But the harder cost to measure was this: every week, leadership was making decisions on data that was three to five days old. At scale, that lag compounds. You cannot put a number on it but it is real and it is expensive.

Six weeks. Twelve automated reports. Three people freed to do work that actually moves the business.

That is what execution looks like.

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Ryon.ai builds AI systems for large companies that need results, not slide decks. If your reporting function is a bottleneck, we can fix it. ryon.ai

How We Eliminated 40% of Operational Waste at a 180-Person Scale-Up

A 180-person company. Series B just closed. Three offices. A tech stack that had grown faster than anyone planned. The business was scaling — internally, it felt like running through concrete.

Their COO came to us not because they had a specific problem. They came because everything felt slow and no one could explain exactly why.

Two weeks of mapping

We spent the first two weeks doing nothing but mapping. Every handoff. Every approval chain. Every tool in the stack and who was actually using it and how.

Their ops team was spending an average of 11 hours a week on manual data reconciliation across three disconnected systems. Finance, project management, and CRM were all producing data in different formats. Every weekly report required someone to manually export, reformat, and re-enter information across platforms before it could be read by anyone.

The people were not underperforming. The system was designed to create friction at every step.

Three interventions

We identified three points where AI could absorb the most drag without requiring any change management on the team side.

First: automated data reconciliation. A pipeline that pulled from all three systems, normalised the data, and surfaced exceptions for human review only when something was genuinely wrong. This eliminated 8.5 hours of manual work per week immediately.

Second: their approval chain. Mid-market companies often inherit approval processes designed for much smaller teams. We mapped every approval type, identified which ones required genuine judgment, and automated the rest. 60 percent of internal approvals now moved without human involvement.

Third: reporting. An AI-generated weekly operations report, delivered every Monday at 07:00, pulling live data from all three systems. No one had to build it. No one had to check it. It was just there.

The results

• 40 percent reduction in operational overhead within eight weeks
• 8.5 hours of weekly manual reconciliation eliminated
• 60 percent of internal approvals automated
• Single source of truth dashboard live across all three offices
• Weekly ops report automated, delivered Monday 07:00 without manual input

What this actually cost them

They had the budget for a tool. They had hired people to manage the friction. What they had not done was build a system that removed the friction entirely.

The eight weeks we spent mapping and building cost less than one quarter of the manual overhead they were running annually. Most of the drag had been invisible because it was distributed across too many people to show up clearly in any single budget line.

That is almost always how it works. The cost is not a line item. It is death by a thousand manual tasks.

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Ryon.ai identifies where performance is leaking and builds the systems that stop it. No slide decks. No pilots that never ship. ryon.ai