Example agent design
Customer and SKU Profitability Intelligence
ITECS helps manufacturing finance teams see which customers, SKUs, plants, and programs actually create margin after cost movement, service requirements, rebates, chargebacks, freight, and complexity. This is a starting design. An AI Blueprint adapts it to your goals and the systems it will connect to.
Revenue growth can hide unprofitable complexity. Profitability intelligence connects finance, operations, customer terms, production cost, and service burden so leaders can price, negotiate, and simplify with evidence.
- Owner
- Finance and commercial leadership
- Reads
- Sales and margin data · Cost and production data · Freight and service burden · Customer contracts
- Never writes
- Prices or customer terms.
- Typical cost
- AI Blueprint $4,500–$7,500, credited toward the build. Typical first build $35,000–$75,000, or start local at $4,500–$9,500. Agent Operations after launch $2,500/mo for one production agent. See the first engagement
The workflow today
The Largest Customer Is Not Always the Most Profitable Customer
Customer concentration, custom SKUs, rebates, freight, changeovers, chargebacks, and service expectations can make reported revenue look better than realized margin.
A profitability agent should connect cost drivers to customer and SKU economics so finance and commercial leaders know where pricing, contract, or portfolio decisions need attention.
Illustrative margin bridge
From Gross Margin to True Customer Profitability
A margin bridge should show which cost-to-serve items turn a high-volume account into a low-return program.
Executive bridge
Value movement
18.4%
Margin before cost-to-serve and contract leakage
Reported gross margin
-3.1 pts
Lane, expedite, minimum order, and service requirements
Freight and service burden
-2.4 pts
Small runs, custom packaging, and yield loss
Changeover and complexity
+1.6 pts
Contract, surcharge, or portfolio review candidate
Recoverable pricing action
Margin leakage reviewed
$2.7M
Illustrative customer and SKU economics requiring review
What leadership sees
- Connects financial margin to operational cost-to-serve
- Shows customer/SKU combinations that need pricing or portfolio review
- Links PPV, contracts, freight, chargebacks, yield, and changeovers
The workflow
The workflow today and with the agent
Planning comparison only. Scope, timing, and outcomes vary by data, workflow, approvals, adoption, and implementation conditions.
- Margin view
- TodayGross margin by SKU or customer
- With the agentCustomer/SKU margin after cost-to-serve and complexity
- Complexity
- TodayKnown anecdotally
- With the agentQuantified by changeover, yield, service, and freight signals
- Pricing action
- TodayPeriodic commercial review
- With the agentEvidence-backed action candidates
- Governance
- TodaySpreadsheet logic
- With the agentVersioned assumptions and role-restricted views
Outcome hypothesis to validate
The value is margin clarity: better pricing, cleaner customer negotiations, fewer hidden loss programs, and more confidence in growth decisions.
Not a reported client result or guarantee.
With the agent
What Customer & SKU Profitability does
Each capability is designed to produce evidence for the people who already own the manufacturing decision.
Customer and SKU margin model
Build a finance view that connects revenue to the real costs of serving each customer and product.
- Customer, SKU, plant, program, and channel profitability
- Chargebacks, rebates, freight, discounts, and service-level burden
- Margin movement tied to PPV, yield, and labor signals
Complexity cost detection
Expose operational complexity that normal gross-margin reports miss.
- Short runs, custom packaging, changeovers, low-volume SKUs, and rework
- Cost-to-serve signals by customer requirement
- Portfolio simplification candidates for review
Pricing and contract action support
Prepare evidence for pricing, surcharge, pass-through, renewal, or service-level discussions.
- Recoverable margin candidates
- Customer negotiation summaries
- Scenario views for price, volume, and service changes
Scenario
Illustrative profitability scenario
Illustrative planning scenario, not a reported client result. A manufacturer has a large customer program with strong revenue but inconsistent realized margin across plants and SKUs.
- Starting point
- Finance sees gross margin by SKU and customer, but freight, service requirements, changeover burden, chargebacks, and contract terms are not connected in one view.
- Scoped outcome
- ITECS scopes a profitability intelligence layer that shows true margin by customer/SKU/program and creates action-ready pricing and portfolio review candidates.
Systems it connects to
What the agent reads
Discovery confirms authoritative systems, data quality, access, and governance before any production workflow is proposed.
- Sales and margin data
- Revenue, price, discounts, rebates, chargebacks, credits, customer, SKU, plant, and program.
- Cost and production data
- Standards, actuals, routings, formulas, changeovers, yield, labor, scrap, and rework.
- Freight and service burden
- Lane cost, expedites, minimum orders, special handling, service levels, and returns.
- Customer contracts
- Price terms, pass-throughs, rebates, service commitments, renewal windows, and penalties.
Workflow
Read-Heavy, Write-Controlled Manufacturing Intelligence
The system connects approved signals, explains risk, prepares recommendations, and routes sensitive actions for human approval.
01
Assemble
Read approved finance, sales, production, freight, quality, and contract data.
02
Allocate
Connect margin to cost-to-serve and complexity drivers.
03
Rank
Identify customer/SKU/program combinations with margin risk or recovery potential.
04
Model
Run pricing, volume, service, surcharge, and portfolio scenarios.
05
Approve
Route commercial actions to finance, sales, and executive owners.
Controls
Read broadly, recommend carefully, keep humans in control
Manufacturing AI becomes trustworthy when it preserves assumptions, source data, approvals, and boundaries.
- Read-heavy
- Reads the systems listed above through access scoped to this workflow.
- Write-controlled
- Writes nothing listed under Never writes in the spec at the top of this page. Anything it drafts waits at the approval points below.
- Approval points
- Recommendations, drafts, and exceptions go to a named reviewer before anything changes.
- Audit trail
- Each run keeps its inputs, sources, output, and the reviewer's decision.
In this design
- The system can recommend pricing and portfolio actions, but it does not change prices or customer terms.
- Sensitive customer profitability views are role-restricted.
- Cost allocation assumptions remain visible and versioned.
- Commercial recommendations preserve source evidence for leadership review.
Security
Security for Manufacturing AI Workflows
Customer profitability is sensitive. ITECS scopes access carefully so margin, pricing, and contract views are only available to approved roles.
- Role-restricted profitability dashboards and agent responses
- No autonomous customer price, rebate, or contract changes
- Versioned allocation assumptions and source references
- Audit history for recommendations and approvals
Deployment fit
Where this design can run
- Local AI Agents
- Runs in one employee's own Claude, Codex, or Copilot workspace, over files that person already has access to. It moves to a hosted agent when it needs scheduling, multi-user access, audit trails, or an SLA.
- Hosted AI Agents
- Runs in a governed Linux environment ITECS operates, on frontier models through enterprise API agreements, connected to sales, cost, and freight data through controlled integrations, with human review and an audit trail.
Typical first engagement
Start with an AI Blueprint
The AI Blueprint sets the actual build rung and its fixed-fee not-to-exceed. The rows below show the typical path and the catalog range for each step.
- FirstAI BlueprintAgent Discovery & Technical Specification. Credited toward the build.$4,500–$7,500
- Typical first buildIntegrated / Line-of-Business Agent$35,000–$75,000
- Or start localLocal Agent SprintOne workflow · 1–2 data sources$4,500–$9,500
- After launchAgent Operations, one production agent$2,500/mo
Prices from the published AI pricing catalog. Every guided or delivered agent build is archived in the ITECS client vault: workspace configuration, skills, prompt libraries, data-source maps, and usage documentation.
Customize this design
Bring your goals and your systems. The AI Blueprint decides the scope and the systems, not this example, and ends with a fixed-fee not-to-exceed for the build.
FAQ
Customer & SKU Profitability FAQ
Is this activity-based costing?
It can support activity-based costing, but the first goal is practical margin visibility by customer, SKU, plant, program, and cost-to-serve driver.
Does it replace finance's margin model?
No. It reconciles to finance's method and makes assumptions more visible, then extends the view with operational and commercial signals.
Can sales use this?
Yes, with role-appropriate access. The system can prepare pricing and renewal evidence for sales while protecting sensitive finance views.
How does this connect to PPV?
PPV explains material cost movement. Profitability intelligence shows where that movement affects customer, SKU, and contract margin.
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