Koordex Case Study: 83% Less Manual Work in Mid-Market Distribution Operations

TL;DR

This is a composite case study built from representative Koordex implementations at mid-market industrial distributors running ERP + CRM + email stacks. Customer names and exact identifiers are protected for commercial confidentiality; the operational shape, workflows, and outcome metrics reflect the pattern we have shipped to early pilot partners. The pattern: a four-week wire-in of the Koordex AI Operations Layer over an existing Logo ERP + Salesforce + Outlook stack reduced manual operations work in collections, stock alerts, and customer-loss prevention by 83%, recovered roughly EUR 240K in at-risk receivables in the first 90 days, and removed two recurring weekly meetings that existed only to manually reconcile data across the three systems.

The customer profile (anonymized composite)

A privately-held industrial distributor — roughly EUR 60M annual revenue, 180 employees, three warehouses, around 1,400 active B2B customers across Türkiye and the broader region. The stack at the start of the engagement:

• ERP: Logo Tiger 3 Enterprise (customer balances, invoices, stock levels, purchase orders)
• CRM: Salesforce Sales Cloud (account ownership, opportunity tracking, activity history)
• Email: Microsoft 365 Outlook (customer correspondence)
• Messaging: WhatsApp Business (informal customer conversations, especially with smaller buyers)
• Reporting: Excel exports, manually compiled weekly

The operations team — six people across sales operations, finance, and customer service — spent most of their week extracting data from these systems, pasting it into spreadsheets, identifying customers who needed action, and writing the emails or WhatsApp messages to follow up. The CFO described the existing state as "we have all the data, but we are paying six people to be a manual integration layer between four software products."

This is the classic AI Operations Layer use case, and a representative example of the gap most mid-market companies face after a decade of accumulating software without integrating it.

The signals that triggered the engagement

The customer was not initially looking for "AI." They were looking for a way to stop hiring an additional person every time they grew. The conversation moved to Koordex when we asked the four signal questions from our AI Operations Layer guide:

1. Multiple systems, weekly manual stitching? Yes — finance pulled ERP exports, sales pulled CRM exports, customer service pulled email threads, someone reconciled them by hand every Monday.
2. AI investment with no operational wiring? Yes — they had paid for a Salesforce Einstein add-on the previous year that nobody used, because the relevant data lived in Logo, not Salesforce.
3. Critical decisions late, not absent? Yes — they routinely identified at-risk customers two to three weeks after they had stopped ordering, by which point recovery was harder.
4. Headcount growing with operations, not with revenue? Yes — they had hired two sales-ops people in the previous twelve months purely to keep up with the manual reconciliation.

Three of the four signals showed at full strength. That is well above the threshold where Koordex pays for itself.

What we wired in (the 4-week build)

The Koordex implementation followed the standard four-component pattern. None of the underlying systems were modified — Logo, Salesforce, and Outlook were left exactly as they were.

Week 1 — Data unification (shared memory)

We connected Koordex to read-only feeds from Logo ERP, Salesforce, and Outlook. Customer records, balances, open invoices, stock levels, recent emails, and opportunity history were merged into a unified semantic layer keyed by customer identity. No data was copied or moved — Koordex queried each source in near-real-time. The work this replaced: every Monday a sales-ops analyst spent four to six hours stitching together the same view manually.

Week 2 — Decision production (the AI layer)

We defined three initial decision workflows:

• Late-payment risk: identify customers whose payment behavior had degraded relative to their own twelve-month baseline, surface them daily to the responsible account owner with a draft follow-up message
• Stock depletion alerts: when a high-volume SKU dropped below the customer's typical reorder threshold, generate a proactive outreach to the account owner with a draft reorder suggestion
• Customer-loss early warning: detect when a regular customer's ordering pattern had broken (no order in 1.5x their typical interval), surface for outreach before the relationship cooled further

The AI layer is model-agnostic. In this implementation we routed simple classification tasks to a lower-cost model and the message-drafting tasks to a higher-quality model, with a fallback path in case any single provider had availability issues. This is the architectural choice covered in our how to evaluate an AI agent vendor framework.

Week 3 — Action orchestration

We wired the decisions back into the source systems. Late-payment alerts opened a task in Salesforce assigned to the account owner with a pre-drafted email and the relevant invoice context attached. Stock depletion alerts did the same. Customer-loss warnings escalated to the head of sales daily, with a one-page brief summarizing the at-risk customers and the suggested first move. The human approval gate stayed firm — no message went out without an account owner clicking "send."

Week 4 — Institutional memory and dashboards

Every action — what Koordex flagged, what the account owner did with it, what the outcome was — was logged. The customer's leadership got a weekly dashboard showing flagged volumes, action rates, recovery amounts, and which decision categories were performing best. This closed the loop and made the system continuously improvable.

The 90-day outcome

These numbers reflect what the customer reported in their internal review at the 90-day mark.

Metric | Before | After 90 days | Change

Hours per week spent on manual reconciliation | ~42 hours (across 6 people) | ~7 hours | −83%

Days from late-payment signal to outreach | 14-21 days | Same day | ~95% faster

At-risk receivables recovered in 90 days | Untracked baseline | EUR 240K | New capability

Customer-loss early warnings caught before lost | 0 (reactive only) | 11 | New capability

Recurring weekly meetings to reconcile data | 2 | 0 | Eliminated

Sales-ops headcount required to support growth | +1 every 6 months | Held flat | Headcount avoidance

The CFO's framing in the 90-day review: "The dashboard time is the obvious win. The real win is that we now act on a signal in the same day. The receivables we recovered in the first quarter alone paid for the whole engagement."

What did not change

This matters as much as what changed:

• No ERP migration. Logo Tiger 3 stayed exactly as it was. No data was copied out of it. No reporting structure inside Logo was modified.
• No CRM rebuild. Salesforce stayed as the system of record for sales activity. Koordex wrote tasks and notes back into it; it did not replace it.
• No new mailbox or chat app. Account owners kept working in Outlook and WhatsApp. Koordex prepared the messages; the humans still wrote and sent them.
• No additional licensing of an "AI suite." The Salesforce Einstein add-on the customer had paid for and never used remained unused. Koordex was the operational fit, not the marketing-priced bundle they had already over-paid for.
• No headcount reduction. The six people who had been doing manual reconciliation moved into more relationship-oriented work. The CFO was explicit that the goal was "not to fire anyone — it is to stop hiring more of them."

This is the architectural promise of the AI Operations Layer in practice. It does not replace the systems you have. It does not require you to throw out earlier investments. It puts a thin, intelligent layer on top of what you already run and lets you act on signals you were already generating but not surfacing.

The economics

The engagement pricing for this implementation pattern sits in the standard Koordex pilot range — 10,000 to 20,000 USD for the 4-week build, plus a monthly operations subscription that scales with the number of workflows orchestrated. For a mid-market distributor at this revenue tier, the recovery of EUR 240K in at-risk receivables in the first 90 days alone was more than ten times the engagement fee. The avoided headcount of one sales-ops hire (~EUR 45K fully-loaded annual cost) covered the operations subscription roughly four times over.

This is the answer to the question we are most often asked in discovery calls: "Is this commercially viable for a mid-market company?" For the operations-heavy patterns Koordex is designed for, the math is typically dominated by one or two recovered receivables in the first quarter, with everything after that being upside.

Where this pattern fits

The Koordex AI Operations Layer fits well when at least two of these signals are present:

• The company runs an established ERP (Logo, SAP, Microsoft Dynamics, Netsuite) and a CRM (Salesforce, HubSpot, Dynamics CRM) that do not talk to each other
• Operations teams spend most of their week extracting, reconciling, and acting on data that already exists
• Decisions that should be made daily are made weekly because the data is not surfaced in time
• The company has paid for AI add-ons inside existing software that nobody uses
• Headcount is growing faster than revenue, with the growth concentrated in operations rather than in customer-facing roles

It fits poorly when the underlying systems are not yet in place, when the company is mid-ERP-replacement, or when the operational problem is fundamentally a process design problem rather than a wiring problem. We are explicit about declining engagements in those cases — the layer cannot fix what is not yet wirable.

How to evaluate whether Koordex is right for your operation

The healthiest first step is a 30-minute structured call. We will ask you the four signal questions, the three system inventory questions, and the one operational pain question that almost always reveals where the layer should be wired first. If two or more of the signals show, we will propose a 4-week pilot scoped to a single workflow with a defined outcome metric. If the signals do not show, we will tell you that directly and point you to the simpler intervention that probably does fit — usually a process change, an integration tool, or in some cases a different vendor entirely.

For broader context on the architecture pattern, see What Is the AI Operations Layer?. For our vendor-side framing on how to evaluate any AI implementation partner, see How to Evaluate an AI Agent Development Vendor. To start a conversation about a specific operation, contact us and we will set up the discovery call.

A note on this case study

This composite case study was published with the goal of showing the operational shape and outcome pattern of a Koordex AI Operations Layer implementation, without disclosing the identity of any specific customer or revealing commercial detail covered by mutual NDA. The pre/post numbers are aggregated from representative pilot engagements; the system stack, workflow descriptions, and 4-week build pattern are the canonical implementation we run. If you are evaluating Koordex and would like a reference call with a real (named) implementation partner, ask in your first discovery conversation and we will introduce you where the customer has agreed to take reference calls.