Machine-like builds and runs custom inventory management agents for distributors, retailers, and product businesses. Agents turn sales history, stock levels, supplier updates, and business rules into demand forecasts, replenishment recommendations, and approved purchasing actions.
Your best seller runs out while the slow movers fill the warehouse. The forecast lives in one file, the latest supplier promise in another inbox, and someone still has to turn both into a purchase order.
Give that mechanical work to an agent built around your operation. Know what needs attention, see the reasoning behind the recommendation, and keep purchasing moving within the limits you set. We build, run, monitor, and maintain the agents. Your team owns the inventory strategy and the decisions that need judgment.
Walk through your current process with Machine-like and identify the first job to hand off. A 14-day free trial begins when the scoped agent goes live.
01 / Direct answer
Agentic inventory management services use AI agents to monitor stock, anticipate demand, and carry out replenishment tasks within business-defined rules. They connect sales, inventory, and supplier data to help decide what to buy, when to order, and where to move stock, then turn those decisions into actions with human approval where required.
The work runs in a loop. Forecasting estimates demand. Planning turns that forecast into quantities, timing, and locations using stock availability, supplier lead times, costs, and your buying rules. Replenishment turns the plan into purchase order drafts, transfers, supplier follow-ups, and updated records. Review highlights what changed and which decisions need a person.
Machine-like’s inventory management agents connect these steps into a recurring workflow. A scoped agent can identify an approaching shortage, draft a replenishment order, request approval, and keep the purchasing record current as the supplier responds.
02 / The cost of the current process
Machine-like’s agents connect demand, inventory, and purchasing work around a shared operating view. The objective is to reduce avoidable shortages, unnecessary purchases, and the manual effort between a planning signal and a completed action.
Total inventory can look healthy while the products that matter are unavailable. Yesterday’s order recommendation can already be wrong after a large customer order, a promotion, or a delayed shipment.
The goal is a planning operation that stays current and gets the next step done.
03 / The work you hand off
Machine-like’s agents coordinate a recurring sequence: refresh data, forecast demand, evaluate inventory, prepare actions, route approvals, and update records. Each workload has defined inputs, outputs, operating rules, and a person responsible for exceptions.
Read the current position. Machine-like agents collect sales, available stock, commitments, open orders, and expected receipts from approved sources.
Find the next constraint. Machine-like agents compare demand and supply over time to identify shortages, excess stock, and unreliable inputs.
Prepare the decision. Machine-like agents calculate a recommended buy, transfer, hold, or follow-up with the source data and assumptions attached.
Get the right approval. Machine-like agents route purchases and exceptions according to agreed vendor, spend, quantity, and policy limits.
Complete the approved work. Machine-like agents create or update records, communicate with suppliers, and track confirmations through configured connections.
Revisit the plan. Machine-like agents refresh the worklist when new information changes the decision.
What lands with your team: a prioritized replenishment list, a forecast you can review, purchase orders ready for approval, and exceptions with a specific next step.
04 / Demand forecasting
Machine-like’s agents prepare demand forecasts at the product, location, and channel levels defined during scoping. Forecasts combine available sales history with approved business context and retain the assumptions behind adjustments.
A recent spike, a recurring seasonal pattern, and a one-time customer order should not all produce the same purchasing response. Agents help separate those signals before the next order is placed.
You get: a refreshed demand outlook with the changes that matter, the reasons behind them, and the items that need a closer look.
For new products or limited history, start with explicit assumptions and comparable products where appropriate. Show the uncertainty instead of disguising a guess as a precise forecast.
05 / Purchasing and replenishment
Machine-like’s agents translate demand into replenishment recommendations using inventory position, expected receipts, lead times, safety stock, and purchasing rules. Recommendations can become supplier-grouped purchase order drafts with contract pricing and approval routing.
Stop treating every line in a reorder report as a new investigation. Give the agent the rules your buyer already applies and let it prepare the work.
You get: order recommendations with quantity, supplier, need-by date, estimated spend, and the calculation behind the request.
06 / Inventory health and allocation
Machine-like’s agents evaluate inventory availability and demand across connected locations and channels. Inventory reviews identify potential transfers, overstock, aging items, and purchasing holds before additional cash is committed.
A warehouse can have excess stock while another location runs short on the same product. Buying more may be the easiest action to take and the wrong one for the business.
You get: transfer candidates, hold-or-reduce recommendations, and excess-stock reviews that make the cash tied up in inventory easier to act on.
07 / Supplier coordination and exceptions
Machine-like’s agents can read supplier confirmations and updates from approved email or document sources. Configured workflows extract changes to quantities, dates, and prices, reconcile purchase order records, and route the affected decisions.
“Shipping next Friday” is a planning event, even when it arrives halfway down an email thread. Agents connect that update to the order, the affected products, and the next action.
You get: current expected receipts and a short queue of supplier issues that actually require your judgment.
08 / Scenario planning and team alignment
Machine-like’s agents prepare scenario comparisons using agreed planning calculations and business constraints. Scenarios show the implications of changes to demand, lead times, order quantities, or stock policies without replacing the approved plan.
A promotion could lift sales. A supplier could slip. A lower unit price could require a much larger order. Before you choose, see what the change means for availability, inventory, and cash commitments.
You get: an explanation of what changes under each scenario and a recommendation ready for discussion. Your team chooses which assumptions become the next operating plan.
09 / Materials and production planning
Machine-like’s agents can extend into material requirements and production planning when bills of material, supply data, and operating constraints are available. The scope connects finished-goods demand to component needs, purchasing dates, and production dependencies.
A finished-goods forecast is only useful if the materials and production slots can support it. For businesses that manufacture or use contract manufacturers, the planning job goes deeper than the next finished-product purchase.
You get: material shortage lists and production or purchasing recommendations connected to the products customers are waiting for.
Start with the decision that creates the most recurring work. Detailed scheduling and complex network optimization are scoped separately when the operation requires them.
10 / Planning analysis and reporting
Machine-like’s agents prepare inventory analysis and recurring reports from approved business data. Answers include source references, reporting periods, calculations, and material assumptions so planners can review the evidence.
Useful questions should not require an afternoon of exports and spreadsheet joins.
Agents prepare the stock review, supplier exception summary, forecast-versus-actual report, and planning brief on an agreed schedule. The output lands in the tools your team uses, with the priority decisions called out.
You get: less report assembly and a clearer starting point for the next business decision.
11 / Your rules. Our operating responsibility.
Machine-like configures agents around customer-approved access, policies, and approval gates. Machine-like operates and maintains the agents; customer teams retain business decisions and control over authorized actions.
Your inventory budget should not depend on an agent improvising. Define what can proceed, what needs review, and what must stop.
Begin with recommendations and drafts. Expand execution only after the workflow meets the agreed standard and you authorize the next level of autonomy.
Machine-like owns the build, monitoring, maintenance, and fixes. Your team owns supplier strategy, service targets, budgets, and the exceptions that require a commercial decision.
12 / Compare the operating model
Machine-like delivers custom agents as a managed service. Spreadsheet-led planning and planning software can support inventory decisions, but responsibility for configuration, daily operation, and maintenance differs by approach.
The comparison below focuses on ownership of the work. Planning products vary, and many already offer forecasting, automation, and approval controls.
| Buying consideration | Spreadsheet-led planning | Planning software | Machine-like managed agents |
|---|---|---|---|
| Getting started | Internal team builds and maintains the workbook. | Internal team or implementation partner configures the product. | Machine-like scopes, builds, and tests a defined workload. |
| Planning logic | Workbook formulas and planner knowledge. | Vendor models, configuration, and available extensions. | Agreed rules plus forecasting methods or existing planning outputs. |
| Routine work | Internal team refreshes data and carries out actions. | Automation varies by product and implementation. | Machine-like agents complete the connected, authorized workflow. |
| Human decisions | Planner reviews and executes decisions. | Planner approvals depend on configured product controls. | Customer sets approval gates; agents route exceptions with context. |
| When connections fail | Internal team investigates and repairs. | Responsibility depends on vendor contract and internal ownership. | Machine-like monitors agent operation and handles fixes. |
| Changing the process | Internal team changes formulas and procedures. | Configuration, services, or development may be required. | Same-job, same-tool changes are included; new workloads are quoted. |
| Best reason to choose | A manageable process with available internal capacity. | A team seeking dedicated planning software and operational ownership. | A team seeking recurring work delivered and maintained as a service. |
Choose Machine-like when the bottleneck is the recurring work around the plan and your team needs someone accountable for running it. An existing ERP or planning system can remain part of the workflow.
13 / Customer proof
Agents handle governed analytics preparation and support automation across your existing systems.
Machine-like built an agentic decision-support layer for a performance marketing analytics team. Agents handled recurring preparation, data QA, monitoring, and reporting while human analysts retained interpretation and final recommendations.
“Our talent is too valuable to spend on manual, repeating tasks.”
Machine-like’s fitDEGREE deployment automated 82% of Tier 1 support tickets. Agents retrieved context, followed approved procedures, updated records, and escalated cases requiring a person.
“The agents worked across our systems and kept me in the loop when something needed my attention.”
Bring one recurring inventory task. Machine-like will help define the inputs, output, and approvals for an initial scope.
14 / Fit assessment
Our agents are a fit for inventory operations with recurring decisions, accessible source data, and rules that can be defined. The starting scope should be narrow enough to test and valuable enough to remove a real operational burden.
Machine-like is a strong fit for teams spending significant time preparing and executing repeatable inventory decisions. The highest-value starting points have clear inputs, an identifiable owner, and a useful output.
Machine-like’s inventory management agents need dependable source access and an accountable business owner. Automation cannot substitute for missing inventory records or undefined purchasing authority.
A useful first workload: review a defined group of products, produce a replenishment recommendation, and prepare the approved order. Expand when the first job earns its place.
15 / Getting started
Machine-like scopes, builds, and tests a custom agent before the 14-day live-work trial begins. The initial workload defines connected systems, business rules, deliverables, approval requirements, and acceptance criteria.
Have a recent stock report, a sample purchase order, and your purchasing rules ready for the conversation. Integration depth and data quality determine the launch plan; the trial clock starts when the agent is live.
16 / What success looks like
Assess agent performance against your documented baseline.
The first 14 days can establish whether the agent completes useful work correctly. Inventory turns, seasonal forecast quality, and working-capital effects require observation over the relevant replenishment and demand cycles.
Success means the workflow earns its place in your operation, with evidence beyond a polished forecast chart.
17 / Pricing
Hand off mechanical tasks to custom AI agents that we build, run, and maintain.
See your agent complete real work free for 14 days before you decide.
| Plan detail | Operator | Growth | Command |
|---|---|---|---|
| Active workloads | 1 to 2 | 3 to 5 | 6 or more |
| Monthly rate | $249 per workload | $199 per workload | Custom quote |
| Monthly example | One workload: 1 x $249 | Three workloads: 3 x $199 | Quoted against scope |
| Support | Email and Slack | Email, Slack, and video | Dedicated success manager |
Scope starts with a defined inventory job. The full capability list is not a single workload. All workloads are scoped with you.
Included: unlimited seats and connectors, custom skills and APIs, approval gates, audit trails, testing before go-live, hosting, monitoring, and fixes.
$0 setup fees. $0 integration fees. No credit card for the trial. The 14-day trial begins when the agent goes live. Billing is monthly and you can pause or cancel anytime.
See current Machine-like pricing and terms → Start free trial →
18 / Questions before you hand off the work
Your systems, recurring inventory work, and approval rules define what the agent takes on. Here’s how the service fits your operation, from existing forecasts to purchasing approvals and ongoing maintenance.
Machine-like’s inventory management agents work with your existing systems. Your ERP, warehouse system, or planning tool can remain the system of record. We confirm access, data mapping, and any actions that update your systems during scoping.
Yes. Inventory management agents can use a forecast you already trust. They handle the work that follows: checking stock, preparing replenishment recommendations, drafting purchase orders, and tracking supplier responses.
When forecasting is part of the workload, inventory management agents use source data and agreed analytical methods to prepare demand estimates. We select the model, forecast horizon, refresh schedule, and validation approach for your operation. Recommendations include the underlying data, calculations, and assumptions.
Machine-like scopes the required data around the first inventory job. Typical inputs include item and supplier records, sales history, usable stock, commitments, open orders, expected receipts, lead times, and buying rules. Materials planning also requires bills of material and relevant production constraints.
Machine-like identifies data limitations during scoping and testing. Conflicting quantities, missing units, and stale receipts must be resolved or handled through defined exceptions. Limited-history forecasts use explicit assumptions and closer review instead of unsupported precision.
Inventory management agents execute purchasing actions only within the access and approval rules agreed for the workload. A deployment can begin with drafts requiring approval, then add authorized routine execution with vendor, spend, and quantity limits. Exceptions go to the person you designate.
Machine-like can scope inventory reviews and allocation recommendations across connected locations and channels. The work requires reliable location-level quantities, transfer lead times, commitments, and agreed allocation priorities.
Inventory management agents can account for material requirements, shelf life, and seasonal demand when the necessary data and business rules are available. Detailed production scheduling and complex optimization are scoped separately.
Inventory management agents can detect supplier date changes, refresh expected receipts, and identify affected orders. They then prepare options for approval, such as following up with the supplier, transferring stock, finding another source, or requesting faster delivery.
Machine-like tests agent workflows before launch and investigates incorrect outputs as part of the managed service. Approval gates and exception handling are set during scoping. Material decisions can remain under human review while the workflow is evaluated and improved.
Machine-like determines the launch window during scoping. Source access, data quality, integration depth, and the actions required drive the schedule. The free 14-day trial begins after the agent goes live, so building and testing do not consume the trial.
Machine-like prices defined recurring jobs, not the entire capability list as a single package. Related steps can belong to one workload; distinct jobs can require separate workloads. The agreed scope establishes the outputs and monthly price before the build.
Machine-like evaluates inventory management performance against the workload’s agreed baseline and targets. No forecast removes uncertainty, and inventory outcomes also depend on demand, supplier reliability, data quality, and business decisions. The trial tests useful work and output quality; longer-term inventory effects need observation across replenishment and demand cycles.
Machine-like builds, monitors, maintains, and fixes deployed agents as part of the service. Connections use access controlled by the customer, runs are logged, and Machine-like’s published policy states that customer data is not used to train models. Specific access and data-handling requirements are reviewed during scoping.
19 / YOUR NEXT PLANNING CYCLE
Show us how you decide what to buy today.
We’ll map the work your agent can take on, from the first data pull to the approved action, and define what a successful trial should deliver.
You keep the supplier relationships, commercial judgment, and decisions that move the business forward. We take care of the recurring, mechanical work behind them.