AI Enablement for Product Manufacturers

Your Domain Knowledge Is Your Greatest Asset. We Make It Work.

Cassel Industrial AI captures decades of product expertise from your unstructured data, builds RAG-powered AI systems, and redesigns workflows to transform how your engineers, service techs, sales teams, and marketing actually operate.

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80%
of organizational knowledge
is tacit — undocumented
$660K–$2.5M
annual cost of inaction for
a typical product manufacturer
80–95%
of AI projects fail — 70%
from people & process issues
Our Founder

Founded by a technology builder who spent 30 years designing, manufacturing, and selling industrial equipment — and now applies AI to the domain he knows best.

CC
Cord Cassel Founder, Cassel Industrial AI
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The Problem
Six Forces Demanding Transformation
Each force is independently significant. Together, they create a strategic environment where inaction carries higher risk than action.
Force 01
Product Knowledge Drain
80% of organizational knowledge is tacit. When your best engineers, service techs, and application experts leave, decades of design rationale, configuration knowledge, and field experience leave permanently.
$75K–$250K per experienced person lost
Force 02
Field Service & Support Burden
Your best service techs carry decades of diagnostic pattern recognition. As the installed base grows, support knowledge expands faster than any team can maintain. First-call resolution depends on reaching the one person who's seen a particular failure.
$100K–$400K/year in service inefficiency
Force 03
R&D Inefficiency
Engineering teams repeat past mistakes because failure data, test results, and design rationale are scattered across file shares, email threads, and people's memories. No systematic access to institutional history.
$100K–$500K/year in redundant effort
Force 04
Sales Engineering Bottleneck
Only 2–3 people can reliably handle complex application questions. New sales engineers take 12–24 months to become effective. Deals are lost when the right person is unavailable.
$150K–$500K/year in lost deals & delays
Force 05
Quality & Warranty Gaps
Years of quality data, warranty claims, and field returns treated as isolated events. Systemic patterns remain invisible until they compound into major warranty events.
$75K–$300K/year in warranty costs
Force 06
Untapped Marketing Evidence
Hundreds of real proof points trapped in ERP systems and engineering files. Marketing says "we serve oil & gas" when data could prove "347 pumps into high-temp processing with 99.2% uptime." Buyers trust evidence.
$100K–$400K/year in lost conversions
Annual Cost of Inaction
$660K–$2.47M
Research-validated for 30–150 employee product manufacturers
VS
Year 1 Investment Starting At
$84K
Complete three-phase engagement
Sources: McKinsey, BCG, Deloitte, Gallup, MIT
Our Approach
Assessment First, Then a Three-Phase Partnership
We don't start building until we know you're set up to succeed. Every engagement begins with a structured readiness assessment.
Pre-Engagement
Readiness Assessment
1–2 weeks · $3K–$5K (credited against Phase 1)
  • Six-area diagnostic across product knowledge, service, R&D, sales, quality, marketing
  • Executive sponsorship evaluation
  • Data maturity audit
  • Change readiness analysis
  • Client-specific cost-of-inaction
  • Go/no-go recommendation
Phase 1
Intelligence Layer
6 weeks
  • Company & product context capture
  • Terminology and vocabulary encoding
  • Unstructured data ingestion via RAG
  • 3–8 AI capabilities (tier-dependent)
  • Baseline metrics & workflow mapping
  • AI that responds like a 20-year veteran
Phase 2
Workflow Redesign & Automation
4–6 weeks
  • Co-design workshops with affected teams
  • 2–6 workflow automations (tier-dependent)
  • Role impact analysis & retraining
  • 30-day transition support per workflow
  • Automated actions, alerts, routing
Phase 3
Ongoing Partnership
Monthly retainer · 12-month minimum
  • J-curve management (first 90 days)
  • System monitoring & optimization
  • Knowledge base curation
  • Quarterly business reviews
  • Expansion roadmap planning
Phase 1
Eight AI Capabilities Built on Your Data
Every capability is powered by your product knowledge, your application history, and your domain expertise — not generic models.
01
Company & Product Context
Business identity, product lines, model numbering, application parameters, customer segments, and terminology baked into every AI response.
02
Product & Design Knowledge
Decades of engineering expertise preserved and searchable — design rationale, test data, engineering change history, application notes.
03
Sales & Application Intelligence
Answer application questions, recommend configurations, surface relevant past projects from your full history.
04
Field Service & Diagnostics
Mine service records, warranty claims, and repair histories to power AI-assisted diagnosis. Match symptoms to known issues instantly.
05
Quality & Reliability Mining
Cluster warranty claims, field returns, and quality events across products, components, suppliers, and environments to reveal systemic patterns.
06
Customer & Order Intelligence
Extract actionable changes from customer communications, engineering notices, and order modifications. Prevent missed changes.
07
AI-Powered Technical Support
Engineers, techs, and sales reps get instant answers from product manuals, service histories, and your company's own expertise.
08
Marketing & Evidence Intelligence
Mine order history, project records, and application data to generate niche capability sheets, data sheets, and success story drafts.
Phase 2
How We Change Operations
McKinsey found workflow redesign is the single strongest predictor of AI financial impact. Here's what that looks like for product manufacturers.
Sales & Application Engineering
Before
Customer asks about a product for an unusual application. Sales rep emails the senior application engineer. Waits hours to days. Only 2–3 people can answer reliably.
After
Sales rep queries the product knowledge system. AI retrieves relevant past applications, configuration recommendations, and known limitations. Senior engineer handles only edge cases.
Field Service & Diagnostics
Before
Customer calls with a field problem. Tech diagnoses from memory, or calls the one senior tech who's seen this before. Repeat visits from incomplete first diagnosis.
After
Service tech describes symptoms. AI matches against full service history — same product, application, symptom pattern. Returns likely diagnosis, repair procedure, and parts needed.
R&D & Product Development
Before
Engineer starts a new design. Misses relevant test data, design rationale, and failure reports scattered across file shares, email, and colleagues' memories.
After
Engineer describes the design challenge. AI surfaces relevant design history: why previous designs made specific choices, what test data showed, what field failures occurred.
Marketing & Customer Evidence
Before
Marketing asks engineering for case study input — gets ignored for months. Capability statements list broad industry categories. Sales says "we've done this" but can't prove it.
After
AI mines order history to auto-generate niche capability sheets with real numbers. Case study drafts assembled from completed project data — without engineer interviews.
Why Cassel
Built for Product Manufacturers. Built to Last.
Human Expertise Is the System
Our founding principle — AI only works reliably when human expertise is systematically embedded into it. Your AI agents actively seek out the right expert when they hit the limits of their confidence. Your people teach the AI, not babysit it.
Exclusively SMB Product Manufacturers
We understand product knowledge walking out the door, service calls piling up, engineering history nobody can find, application expertise locked in a few heads, and marketing that can't prove what you've done.
We Learn Your Products First
Generic AI gives generic answers. We capture your product lines, model numbering, application parameters, design terminology, order history, and service patterns before writing a line of code.
Workflow Change, Not Just Technology
We redesign how your team works — co-designing new processes with the engineers, techs, sales people, and marketing teams who do the job. That's the part that makes AI deliver. It's the part most vendors skip.
We Don't Disappear After Deployment
Phase 3 partnership means your AI systems are monitored, tuned, and expanded. We manage the J-curve transition — the well-documented productivity dip before acceleration — and plan every next step with you.
Right-Sized Investment
Starting at $84K for Year 1 — not $3M enterprise engagements. Purpose-built for $5M–$50M product manufacturers that Big 4 consulting can't serve profitably.
The Reality
80–95% of AI Projects Fail
We built our entire methodology around preventing the failure modes that sink most engagements.
How Most Vendors Fail
Sell technology looking for a problem. Pursue 6+ use cases simultaneously.
How Cassel Addresses It
Six-area diagnostic identifies the 2–3 highest-impact areas. Scope limited by evidence, not ambition.
How Most Vendors Fail
Assume clean data exists. No assessment of data maturity before committing.
How Cassel Addresses It
Readiness assessment evaluates data maturity before quoting. Red = no-go, regardless of deal size.
How Most Vendors Fail
Deploy generic AI that knows nothing about your products, customers, or applications.
How Cassel Addresses It
Phase 1 captures product knowledge, application expertise, and terminology. Domain context in every response.
How Most Vendors Fail
Bolt AI onto existing processes unchanged. No workflow redesign.
How Cassel Addresses It
Phase 2 redesigns workflows around AI outputs. Co-design workshops with your teams. Role impact analysis.
How Most Vendors Fail
Sell to department managers. No executive sponsorship requirement.
How Cassel Addresses It
Named executive sponsor required. Quarterly business reviews. 3x correlation with success outcomes.
How Most Vendors Fail
Deploy and disappear after go-live. No ongoing support or optimization.
How Cassel Addresses It
Phase 3 monthly partnership with J-curve management, monitoring, curation, and expansion planning.
Investment
Right-Sized for Product Manufacturers
Purpose-built for $5M–$50M manufacturers — not $3M–$10M enterprise consulting engagements that Big 4 firms sell to Fortune 500 companies.
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It Starts With a Diagnostic
Every engagement begins with a paid readiness assessment. We diagnose your highest-impact transformation areas, evaluate data maturity, and confirm executive sponsorship — before either of us commits to a full engagement. The fee is credited against Phase 1 if you proceed.
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Fixed-Price Implementation
Phases 1 and 2 are fixed-price — no open-ended hourly billing, no scope creep anxiety. You know exactly what you're investing before we start. Workflow redesign, change management, and training are standard inclusions, not surprise add-ons.
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Ongoing Partnership, Not a One-Time Project
Phase 3 is a monthly retainer that keeps your AI systems monitored, tuned, and expanding. Includes J-curve management, quarterly business reviews with your executive sponsor, and continuous knowledge base curation as your business evolves.
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Scaled to Your Complexity
Investment scales with company size, data volume, and the number of transformation areas in scope. Whether you're a 25-person OEM or a 250-person industrial equipment maker, the engagement is sized to your operation — not a one-size-fits-all package.
Every proposal includes a client-specific cost-of-inaction analysis built from your actual operational data — so you're comparing the investment against what it's already costing you not to act.
Currently accepting a limited number of engagements
The Technology Is Ready. The Question Is Whether Leadership Has the Conviction to Change.

The cost of inaction is $660K–$2.47M per year. The question isn't whether to transform — it's whether to start now or wait while the gap compounds. Let's have a conversation about your operation.

Schedule a Conversation → Learn the Process
Sources: McKinsey · BCG · RAND · MIT · Deloitte · Gartner · Stanford HAI