AI Inventory Automation

Inventory automation services that starts with whether your stock data is accurate

Our inventory automation services begin with stock accuracy and replenishment rules. We assess the available history, lead times and exceptions before automating reordering or recommending inventory changes.

Free 30-minute consultation
Fixed-scope pilots
U.S.-based team
Custom, not off-the-shelf
SOC 2-aligned practices
ROI tracked in writing
What is AI inventory automation?

AI inventory automation manages stock levels and replenishment using demand patterns, lead times and variability rather than fixed reorder points. It covers automated purchase order generation, safety stock optimization, stock accuracy improvement and exception handling for supply disruptions.

7+
Years building AI systems
240+
Projects delivered
4.8
Avg. months to payback
38
U.S. states served
The Problem

Automating reorders on stock numbers that are wrong

Most inventory systems record a quantity that does not match what is physically on the shelf. Shrinkage, receiving errors, unrecorded movements and returns all cause drift, and cycle counting rarely keeps pace.

Automating replenishment on top of that makes the errors compound faster. The system orders confidently against a number that was wrong.

Our Approach

Establish accuracy, then automate

We measure record-to-physical accuracy before automating anything, and where it is poor we address that first through targeted cycle counting and root cause analysis on the discrepancies.

Then replenishment automation on a foundation that reflects reality, with demand variability driving safety stock rather than a uniform rule applied to every item.

Diagram of five connected workflow steps
Diagram of five connected workflow steps
Service Overview

Inventory automation services: scope and deliverables

Stock accuracy first, because everything downstream depends on it. Measuring record-to-physical variance, finding where discrepancies originate, and targeting counting effort at the items where accuracy actually matters.

Then replenishment: reorder points and quantities driven by demand variability and lead time variability per item rather than a uniform policy that over-stocks predictable items and under-stocks volatile ones.

Exception handling: supplier delays, demand spikes and stockout risks surfaced with recommended action rather than discovered when the shelf is empty.

And obsolescence, which is the cost nobody tracks until a write-off.

  • Record-to-physical accuracy measurement and root cause analysis
  • Targeted cycle counting focused where accuracy matters most
  • Item-level reorder points driven by actual demand and lead time variability
  • Automated purchase order generation with approval thresholds
  • Exception alerting on supplier delays, demand spikes and stockout risk
  • Obsolescence and slow-moving stock identification before write-off
Right Fit

Who this suits

Businesses carrying enough inventory that the working capital trade-off is material, and where stockouts have measurable cost in lost sales or production downtime.

And operations where replenishment is currently managed by rule of thumb or by one experienced person’s judgement, which is both effective and fragile.

  • Businesses with significant inventory investment and stockout costs
  • Operations with many SKUs where per-item management is impractical
  • Companies where safety stock is set by a uniform rule of thumb
  • Businesses whose stock records diverge from physical reality
  • Operations dependent on one person’s replenishment judgement
  • Companies with recurring obsolescence write-offs
Benefits

Benefits of inventory automation

Accuracy established first

Record-to-physical variance measured before automating, because automating on wrong numbers compounds the error.

Working capital released

Safety stock set per item against actual variability rather than a uniform buffer that over-stocks the predictable.

Fewer stockouts on volatile items

Items with genuinely variable demand get appropriate buffers instead of the same rule as steady ones.

Counting effort targeted

Cycle counting focused where accuracy matters most rather than spread evenly across every SKU.

Exceptions surfaced early

Supplier delays and demand spikes flagged with recommended action before they become stockouts.

Knowledge out of one head

Replenishment judgement encoded rather than depending on one experienced person remaining.

Problems We Solve

Business challenges this solves

01

Stock records not matching reality

Automation compounding errors. Accuracy measured and addressed before automating.

02

Uniform safety stock

Same buffer for predictable and volatile items. Per-item variability-driven levels.

03

Stockouts on the wrong items

Volatile demand under-buffered. Variability-aware reorder points fix it.

04

Counting spread evenly

Effort wasted on low-value items. Targeted counting where accuracy matters.

05

One person managing replenishment

Judgement resident in one head. Encoded logic removes the dependency.

06

Obsolescence found at write-off

Slow-moving stock unnoticed. Early identification enables action.

What's Included

Features and deliverables

Everything below is in scope on a standard engagement. Nothing here is an upsell discovered halfway through the build.

01

Stock accuracy assessment

Record-to-physical variance measured by category, with root cause analysis on where discrepancies originate.

02

Targeted cycle counting

Counting frequency driven by item value, movement rate and historical variance rather than applied uniformly.

03

Demand variability analysis

Per-item demand pattern and variability characterized, distinguishing genuinely volatile items from steady ones.

04

Reorder point optimization

Item-level reorder points and quantities calculated against demand variability, lead time variability and service targets.

05

Automated purchase orders

PO generation with approval thresholds, supplier selection rules and consolidation logic.

06

Exception alerting

Supplier delays, demand anomalies and projected stockouts surfaced with recommended action.

07

Obsolescence detection

Slow-moving and at-risk stock identified early enough for action rather than at write-off.

08

ERP and WMS integration

Built into your existing inventory, warehouse and purchasing systems rather than replacing them.

Technology Stack

Technologies we use for inventory automation

We are not tied to one vendor. Model and infrastructure choices are made on accuracy, cost per task, latency, and where your data is allowed to live.

Language Models
C
Claude (Anthropic)
G
GPT (OpenAI)
G
Gemini (Google)
L
Llama
M
Mistral
A
Azure OpenAI Service
Data & Backend
P
Python
T
TypeScript / Node.js
P
PostgreSQL
S
Snowflake
d
dbt
A
Apache Airflow
Cloud & Infrastructure
A
AWS Bedrock
G
Google Vertex AI
M
Microsoft Azure
D
Docker
K
Kubernetes
T
Terraform
Business Systems
S
Salesforce
H
HubSpot
N
NetSuite
M
Microsoft 365
S
Slack
Z
Zapier / Make
How We Work

Our AI development process

The same five stages on every engagement, so you always know what happens next and what you get at the end of it.

01

Discovery

We interview the people doing the work, map the workflow end to end, and audit the systems and data behind it.

02

AI Strategy

Every opportunity gets scored on cost to build, time to value, and annual savings, then ranked.

03

Pilot Build

We ship the top-ranked automation as a fixed-scope pilot so you see real output before committing further budget.

04

Implementation

Integration with your live systems, staff training, human-in-the-loop review gates, and a documented rollback path.

05

Optimization

Monthly accuracy reviews, prompt and retrieval tuning, and a written report on hours and dollars saved.

Timeline

How long it takes

A typical first engagement, week by week. Complex integrations and regulated environments extend this, and we say so during discovery rather than after.

Weeks 1 to 2

Discovery and scoping

Process observation, systems audit, data review, and a written estimate of cost and expected saving before anything is built.

Week 3

Design sign-off

Architecture, data handling rules, review thresholds and success measures agreed in writing.

Weeks 4 to 7

Build and integration

Development against your real data, connected to your live systems, with weekly demos rather than a single reveal.

Week 8

Parallel run and testing

The system runs alongside the existing process so accuracy can be compared directly before anyone depends on it.

Weeks 9 to 10

Launch and handover

Cutover with a rollback path, staff training, full documentation, then 30 days of included tuning.

Who We Work With

Industries we deliver inventory automation for

Retail & E-commerce

Product data enrichment, demand forecasting, support deflection, and personalized merchandising.

Manufacturing

Quality inspection, maintenance prediction, supplier communication, and production scheduling.

Logistics & Supply Chain

Document processing, carrier communication, exception handling, and inventory rebalancing.

Construction

Bid takeoffs, submittal review, RFI drafting, and field-report summarization.

Healthcare

Intake, prior authorization, clinical documentation, and revenue-cycle workflows built to respect HIPAA boundaries.

Professional Services

Proposal drafting, timesheet capture, research synthesis, and client reporting at scale.

Financial Services

Document extraction, reconciliation, KYC support, and audit-ready reporting with full traceability.

Insurance

First-notice-of-loss intake, claims triage, policy Q&A, and fraud signal detection.

Use Cases

Real-world use cases

01

Automated replenishment

Purchase orders generated against demand-driven reorder points with approval thresholds.

02

Safety stock optimization

Buffers set per item against actual variability, releasing capital from over-stocked predictable items.

03

Stock accuracy programme

Measuring and improving record-to-physical accuracy with targeted counting.

04

Stockout prevention

Projected stockouts flagged with lead time in hand to act.

05

Obsolescence management

Slow-moving stock identified early enough to discount, return or redeploy.

06

Multi-location balancing

Stock rebalanced between locations rather than reordering where inventory already exists elsewhere.

Why DevSolutionsAI

Why choose DevSolutionsAI for inventory automation

Business case before build

Every recommendation carries an estimated cost, timeline, and annual savings figure. If the math does not work, we say so before you spend.

Vendor-neutral by design

We resell nothing and take no platform commissions. Model and infrastructure choices are made on fit, cost, and your data-residency rules.

Fixed-scope pilots

The first engagement is a defined deliverable at a defined price, not an open-ended retainer that quietly grows each quarter.

Built for handover

You own the code, the prompts, the infrastructure, and the documentation. No lock-in to a proprietary wrapper you cannot leave.

Human-in-the-loop where it counts

Anything customer-facing, clinical, financial, or legal gets a review gate, a confidence threshold, and a logged audit trail.

Security reviewed early

Data flow diagrams, retention rules, and access boundaries are agreed in week one, not retrofitted after your security team objects.

Get Started

Find out what inventory automation would cost you, before you commit to anything

Every engagement is quoted after a short discovery, so you get a fixed written price built around your actual volumes rather than a rate card that assumes someone else’s business.

The first call is thirty minutes and free. Bring one workflow. We will tell you what it is likely costing you each year, roughly what automating it would take, and whether we think it is worth doing at all.

  • A written savings estimate before any paid work
  • Fixed scope and fixed price, agreed up front
  • Full ownership of everything we build for you
  • An honest recommendation when the numbers do not work
What clients typically see
Across recent projects
Staff hours saved each week
31
Months to payback
4.8
Client retention
94%
Response to enquiries
4 hrs

Figures are internal measurements across recent engagements, reported to every client monthly in writing.

Illustrative project scenario

Illustrative project scenario

Industrial distributor · 34,000 SKUs

Finding the stock records were wrong before automating anything

Challenge. A distributor wanted automated replenishment across 34,000 SKUs. Their existing process used a uniform safety stock rule and one experienced planner’s judgement on important items. They had budgeted for the automation build.

What we built. The assessment measured record-to-physical accuracy first and found it was materially worse than assumed, concentrated in a specific warehouse zone with a receiving process problem. We recommended fixing that and running targeted cycle counting before automating, then built replenishment on the corrected foundation with item-level variability driving safety stock.

Outcome. The receiving process fix and targeted counting brought accuracy to a usable level within two months. Replenishment automation then released working capital from over-stocked predictable items while improving availability on volatile ones. The planner moved to strategic and volatile items with automation handling the long tail.

34,000
SKUs covered
2 months
Accuracy remediation first
Per item
Safety stock, not uniform
1 zone
Root cause of inaccuracy

Illustrative project scenario. The figures demonstrate how a project could be scoped and evaluated; they are not verified client results or an audited average.

Client Feedback

What clients say about working with us

31
Avg. staff hours saved weekly
4.8
Avg. months to payback
94%
Client retention
4
Hour response to enquiries
Common Questions

Inventory Automation FAQs

Because automated replenishment orders confidently against whatever number the system holds. If that number does not match physical reality, automation makes the errors compound faster rather than fixing them. On a recent engagement we found accuracy was materially worse than assumed and traced it to a receiving process problem in one warehouse zone, fixing that first was the difference between automation working and automation amplifying a data problem.

It over-stocks predictable items and under-stocks volatile ones simultaneously. Two items with the same average demand can have completely different variability, and applying the same buffer to both ties up capital on one while causing stockouts on the other. Item-level safety stock driven by actual demand and lead time variability addresses both at once.

Usually it changes what they focus on. An experienced planner frequently outperforms automation on a few hundred important or strategic items and cannot scale beyond that. The effective pattern is automation handling the long tail while the planner concentrates on the items where judgement genuinely adds value.

Then some of this is not viable yet and we will say so. Automated replenishment needs demand history with stockout periods identifiable, otherwise the system learns that demand fell when supply ran out. The assessment establishes whether your data supports it, and where it does not the useful first step is improving capture.

Yes, we build into your existing inventory, warehouse and purchasing systems rather than replacing them. Purchase orders are generated through the ERP’s supported interfaces so its own approval workflow and controls still apply.

A two to three week assessment covering stock accuracy, demand variability and opportunity sizing runs $10,000 to $18,000, and frequently changes the plan. A replenishment automation build typically runs $45,000 to $95,000 depending on SKU count and integration complexity. Payback is usually measured on working capital released plus stockout reduction.

Service Areas

Inventory Automation across the United States

We deliver inventory automation remotely to clients nationwide, with on-site workshops available in major metros.

Ready to scope your inventory automation project?

Book a free 30-minute consultation. Bring one workflow and leave with a realistic estimate of what it would cost to automate and what it would save.

Free 30-minute consultation
Fixed-scope pilots
U.S.-based team
Custom, not off-the-shelf
SOC 2-aligned practices
ROI tracked in writing
Free 30-minute AI consultation