Friction & Focus Map: Safety Shower RFQ Workflow

This document uses the Infinity Method grammar to map the Justrite Safety Shower RFQ workflow. Its purpose is to serve as a living artifact for our Design & Build Workshop, enabling us to collaboratively identify and solve for the most critical points of friction.

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The Macro Value Chain: From Inquiry to Award

The Value Pair (North Star)

• What do we do that is genuinely valuable? We deliver expert, de-risked compliance analysis that allows Justrite to confidently and successfully bid on complex, high-value projects.
• Why is that valuable to the people who receive it? For the customer (EPC), it provides deep assurance that a critical safety component will meet their rigorous standards. For Justrite, it unlocks significant revenue and solidifies their market position as a trusted expert, not just a vendor.

Mermaid Diagram

flowchart TD
    subgraph The Macro Value Chain
        direction LR
        Signal["Signal:<br/>EPC RFQ Arrives"] -->
        Micro1["Case File 01:<br/>RFQ Intake & Triage"] -->
        Micro2["Case File 02:<br/>Requirements Analysis"] -->
        Micro3["Case File 03:<br/>Solution Configuration"] -->
        Micro4["Case File 04:<br/>Proposal Handoff"] -->
        Outcome["Outcome:<br/>Contract Awarded"]
    end

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Case File 01: RFQ Intake & Triage

Context: This is about the first hour of contact with a new RFQ. It’s the move from a state of chaos—a massive, undifferentiated zip file—to a state of organized readiness. Getting this right prevents wasted work and builds momentum.

1. Value Pair

• What do we do that is genuinely valuable? We transform a chaotic dump of up to 5,000 pages into a single, coherent picture of the task at hand, establishing clarity and confidence from the very start.
• Why is that valuable to the people who receive it? For the Proposals Engineer, it removes the overwhelming “where do I even start?” feeling and allows him to engage with his expertise immediately, rather than burning energy on administrative Process Friction.

2. Value Chain Placement

Macro Value Chain → Case File 01: RFQ Intake & Triage → Case File 02: Requirements Analysis

3. Function → Task

• Function: Proposals Engineering
• Task: Initial RFQ assessment and document triage.

4. Step Card (Signal → Goal → Steps → Outcome)

• Signal: Email with a zip file from an EPC lands in the inbox.
• Goal: Achieve a confident “go/no-go” decision within the hour, knowing we have all the core documents needed to begin a serious analysis.
• Steps:
  1. Get the documents out of the zip file and into a clean folder.
  2. Hunt through the file list to find the main request (the MR) and the key specs.
  3. Gut-check if this looks like a complete package based on past experience.
  4. Get a feel for the size of the beast—is this a one-day job or a one-week marathon?
  5. Spot any immediate red flags like corrupted files that will stop us cold.
  6. Make the call: are we ready to dive in, or do we need to send up a flare for more info?
• Outcome: A clear decision is made and documented: either we proceed to deep analysis, or a specific, targeted RFI is sent back to the client, preventing a stall later on.

5. Scoping the Work (Possible × Feasible)

• Knowledge Required:
  • Procedural: Knowing the steps to find the primary MR within a large, unstructured zip file.
  • Conceptual: Understanding the difference between a high-priority EPC specification and a non-applicable boilerplate document.
  • Factual: Recognizing the names and typical structure of documents from repeat Engineering, Procurement, and Construction (EPC) clients.
• Cognitive Action (Feasibility):
  • The cognitive work here is primarily Remembering (past project structures), Understanding (document purpose), and Applying (a mental checklist). These are low-to-mid-order actions, making this task a highly feasible and low-risk target for AI augmentation.

6. Friction Map & Stakeholder Lens

• Operator (The Proposals Engineer): The “Where Do I Even Start?” Problem (Cognitive Load). “It can sometimes be overwhelming knowing where to start… we can receive anywhere from 40 to 110 files… and these files can contain anywhere from 2,000 to 5,000 pages.” This is exhausting, low-value Repetitive Administrative Work that burns an expert’s focus before the real analysis even begins.
• Orchestrator (The Project Orchestrator): The “Missing Piece” Stall (Delay). The entire project timeline is at risk from day one. A missing document discovered after triage has started can cause a multi-day stall, jeopardizing responsiveness and creating scheduling chaos.
• Executive (The Executive Sponsor): The Expert Bottleneck (Knowledge Gap). “If a large RFQ was allocated to an associate with less experience, they probably wouldn’t know where to start.” The process is unscalable and creates a single point of failure, putting a hard cap on the number of complex, high-value bids the team can handle.

7. SEA Intent (Primary)

• Streamline: We are choosing to make the work easier. The primary pain here isn’t the quality of the final triage—an expert gets it right—but the sheer, soul-crushing manual effort it takes to get there. We want to eliminate the low-value Repetitive Administrative Work.

8. TOTE Loops (for each step)

• TOTE for Step 1: Download & Extract
  • Test 1: Is there a new, unhandled RFQ zip file in the intake channel?
  • Operate: An automation watches the folder, grabs the new zip, and extracts it into a clean, consistently named project folder.
  • Test 2: Does the project folder exist and contain files?
  • Exit: The files are now in a known location, ready for processing.
• TOTE for Step 2: Locate Primary Documents
  • Test 1: Has the document set been scanned to identify the core “anchor” documents?
  • Operate: AI reads all file names and first pages, looks for keywords like “Material Requisition,” and presents a “Top 3 Most Likely Primary Docs” list.
  • Test 2: The engineer glances at the list and confirms the MR with a single click.
  • Exit: The primary document is now flagged, providing context for all other files.
  • Test (generic — variants/power/evidence): During initial scans, detect variant‑available phrasing, power terms (voltage/phase/frequency), and references to certificates/declarations/registers; flag items for focused checks downstream.
• TOTE for Step 3: Completeness Check
  • Test 1: Is the package unverified against what we expect from this client?
  • Operate: AI compares the file list against a simple template for that EPC and flags what’s missing.
  • Test 2: The engineer sees a clear “Completeness: PASS” or “Completeness: FAIL - Missing Paint Spec.”
  • Exit: The package is marked “Verified Complete” or a draft RFI is automatically created.
• TOTE for Step 4: Complexity Assessment
  • Test 1: Is the project’s true scale just a gut feeling?
  • Operate: AI generates a one-page “RFQ at a Glance” manifest: total page count, file count, and a list of which documents reference others.
  • Test 2: The engineer reviews the one-pager to get an instant, data-driven sense of the project’s size.
  • Exit: The project is tagged with a complexity score (Small, Medium, Large).
• TOTE for Step 5: Identify Blocker Issues
  • Test 1: Are we assuming all files are usable?
  • Operate: AI attempts to open and parse every file, creating a “File Integrity Report” that flags any corrupted or unreadable files.
  • Test 2: The engineer sees a clean “Integrity: PASS” or a list of specific problem files.
  • Exit: The set is confirmed as “All Files Readable,” or the problem files are added to the draft RFI.
• TOTE for Step 6: Decide to Proceed
  • Test 1: Is the “go/no-go” decision still an implicit, un-logged action?
  • Operate: A single dashboard presents the results of all previous TOTE loops to the engineer with two clear buttons: “Proceed to Analysis” or “Review RFI.”
  • Test 2: The engineer makes the explicit choice.
  • Exit: The project status is officially updated, triggering the next Case File or the RFI approval process.

9. Light Automation / AI Assist (Lovable Interventions)

• To eliminate the painful Repetitive Administrative Work of… manually creating a mental map of 100+ files, the AI can… generate a one-page “RFQ at a Glance” manifest. This frees the operator to focus on the high-value “judgment work” of… instantly assessing the project’s true scope and strategic importance.
• To eliminate the painful Repetitive Administrative Work of… re-reading emails to check if everything arrived, the AI can… run an automated completeness check against a simple template. This frees the operator to focus on the high-value “judgment work” of… deciding how to engage the client about any missing information.

10. Retro (Workshop Questions)

• What’s the one thing on the “RFQ at a Glance” manifest that would make you feel the most “in control”?
• If this system gave you back 50 minutes from every complex RFQ triage, what is the first thing you would do with that time?
• What would it take for you to love this initial step, instead of just enduring it?

11. The Compounding Advantage

Fixing this first step isn’t just about saving an hour. It’s about establishing a pattern of clarity and control from the very beginning of every project. This single change transforms the start of the process from a source of anxiety into a source of momentum, creating a repeatable, scalable foundation that makes every subsequent step faster, easier, and more reliable.

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Case File 02: Requirements Analysis

Context: The triage is complete. We have a verified, organized set of documents. This next stage is the “marathon”—the deep, meticulous work of extracting every single technical and commercial requirement to create the foundational source of truth for the entire proposal.

1. Value Pair

• What do we do that is genuinely valuable? We meticulously deconstruct thousands of pages of dense, technical language into a single, structured, and verifiable list of every client requirement.
• Why is that valuable to the people who receive it? For the Proposals Engineer, this creates an unambiguous map of the challenge, replacing the chaos of raw documents with an orderly checklist. For Justrite, this is the most critical de-risking activity in the entire process; a single error here can lead to significant financial loss, reputational damage, and project failure.

2. Value Chain Placement

Macro Value Chain → Case File 01: RFQ Intake & Triage → Case File 02: Requirements Analysis → Case File 03: Solution Configuration

3. Function → Task

• Function: Proposals Engineering
• Task: Detailed requirements extraction and compliance mapping.

4. Step Card (Signal → Goal → Steps → Outcome)

• Signal: The “go/no-go” decision from the Triage stage is “Go.” A validated set of documents is ready for analysis.
• Goal: To produce a 100% complete and accurate list of all technical and commercial requirements, with each item traced back to its source document and page number, before proceeding to solution configuration.
• Steps:
  1. Open the primary specification document and a blank spreadsheet (the future compliance matrix).
  2. Read through the document, line by line, paragraph by paragraph.
  3. Identify a sentence or clause containing a specific requirement (e.g., “all piping shall be 316L stainless steel”).
  4. Manually transcribe the requirement into a row in the spreadsheet.
  5. Note the source document, clause number, and page number in adjacent columns.
  6. Repeat this process hundreds of times across all relevant specification documents.
  7. Implicitly use expert judgment to mentally flag which requirements are high-risk or unusual.
• Outcome: A comprehensive, internally created spreadsheet that serves as the single source of truth for all client requirements for the remainder of the project.

5. Scoping the Work (Possible × Feasible)

• Knowledge Required:
  • Factual: Knowing the definitions of specific standards (Atex, NACE), material grades, and technical terms.
  • Conceptual: Understanding the difference between a hard requirement (“shall”), a guideline (“should”), and descriptive text.
  • Procedural: Knowing how to structure and populate the compliance matrix spreadsheet.
• Cognitive Action (Feasibility):
  • The core human task is Applying a set of rules (find requirement → copy → cite) repeatedly. However, the real cognitive work is in Analyzing dense text to locate the requirements and Evaluating their potential impact. The “Applying” part is low-order and highly feasible for AI to augment, freeing the human for the higher-order “Analyzing” and “Evaluating.”

6. Friction Map & Stakeholder Lens

• Operator (The Proposals Engineer): The “Marathon of Manual Labor” (Undesirable Work). “Manually reviewing this is a time-consuming exercise.” This is the “8+ hour bottleneck.” It’s tedious, error-prone, and a profound misuse of a senior expert’s time and judgment.
• Orchestrator (The Project Orchestrator): The “Unpredictable Black Hole” (Delay). This stage has the highest variability in duration. A seemingly simple RFQ can suddenly consume an entire week, making project planning and resource allocation incredibly difficult.
• Executive (The Executive Sponsor): The “Buried Time Bomb” (Rework & Risk). “We missed a document deep, deep within this trail… one particular spec… which kind of like five documents deep.” This is where costly mistakes are born. A single missed requirement here is a direct hit to profitability and client trust post-award.

7. SEA Intent (Primary)

• Enrich: While this intervention will also Streamline effort and Accelerate the timeline, the primary intent is to make the outcome better. The goal is to create a more accurate, complete, and auditable requirements list than is humanly possible under time pressure, thereby eliminating the risk of a “buried time bomb.”

8. TOTE Loops (for each step)

• TOTE for Steps 1-6: Requirements Extraction
  • Test 1: Is the set of client specification documents un-analyzed?
  • Operate: An AI agent reads all specification documents, identifies every sentence containing requirement language (e.g., “shall,” “must,” “will be,” “minimum of”), and extracts three pieces of data for each: 1) The requirement text itself, 2) The source document name, 3) The page and clause number.
  • Test 2: The Proposals Engineer reviews the extracted requirements in a clean, table-based interface (not the raw PDFs). They can approve, edit, or delete each AI-suggested requirement with a single click.
  • Exit: A fully populated and human-validated draft compliance matrix exists as a structured data object.
• TOTE for Step 7: Risk Flagging
  • Test 1: Is the validated list of requirements un-assessed for risk?
  • Operate: The AI agent compares each validated requirement against a knowledge base of “known critical keywords” (e.g., “Atex,” “NACE,” “PMI,” “paint,” “bolting,” “Traffolyte,” “LDs” - derived from the Engineering Lead’s and the Proposals Engineer’s feedback).
  • Test 2: The engineer reviews the compliance matrix, where all requirements containing these keywords are now automatically flagged as “High-Risk - Expert Review Required.”
  • Exit: A prioritized compliance matrix exists, allowing the engineer to focus their attention on the 20% of requirements that carry 80% of the project risk.

9. Light Automation / AI Assist (Lovable Interventions)

• To eliminate the painful Repetitive Administrative Work of… manually reading 5,000 pages and copy-pasting hundreds of lines of text, the AI can… perform the initial, comprehensive extraction sweep. This frees the operator to focus on the high-value “judgment work” of… validating the extracted list and investigating the nuances of the most critical requirements.
• To eliminate the fear of… the “devil in the detail” and missing one critical line in a sea of documents, the AI can… act as a tireless, vigilant assistant that automatically flags known high-risk terms. This frees the operator to focus on the high-value “judgment work” of… applying their deep expertise to solve these known challenges, rather than wasting energy just trying to find them.

10. Retro (Workshop Questions)

• What would you need to see in the AI’s extraction interface to trust its output? (e.g., seeing the extracted text highlighted in the original PDF side-by-side?)
• If the AI could reliably flag 90% of the known high-risk requirements for you, how would that change your approach to the review?
• What is one “hidden gem” requirement you’ve found in the past that we should add to the AI’s “known critical keywords” list?

11. The Compounding Advantage

By transforming requirements analysis from a manual, artisanal craft into a standardized, AI-assisted, and human-validated process, we create a verifiable audit trail for de-risking every project. This becomes a teachable, scalable capability. A junior engineer, augmented by this system, can perform a review with the thoroughness of a senior expert. This breaks the “expert bottleneck” and allows Justrite to confidently pursue more complex projects in parallel, directly enabling business growth.

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Case File 03: Solution Configuration & Deviation Analysis

Context: The requirements are now known, structured, and prioritized. This stage is where the core engineering judgment happens: translating the client’s needs into a concrete Justrite solution. It’s a process of matching, problem-solving, and, most critically, identifying where the proposed solution deviates from the request.

1. Value Pair

• What do we do that is genuinely valuable? We apply deep, experience-based product and engineering knowledge to configure the optimal compliant solution, while transparently and defensibly documenting any necessary deviations.
• Why is that valuable to the people who receive it? For the customer (EPC), it provides a clear, honest, and technically sound proposal that they can trust, even when it’s not 100% compliant. For Justrite, it establishes credibility, avoids future commercial penalties for unstated non-compliance, and turns the act of deviation from a weakness into a demonstration of expertise.

2. Value Chain Placement

Macro Value Chain → Case File 02: Requirements Analysis → Case File 03: Solution Configuration → Case File 04: Proposal Handoff

3. Function → Task

• Function: Proposals Engineering
• Task: Product selection, technical configuration, and deviation identification.

4. Step Card (Signal → Goal → Steps → Outcome)

• Signal: A human-validated, risk-flagged compliance matrix is ready for analysis.
• Goal: To produce a fully configured technical solution and a corresponding, clearly justified list of all deviations from the client’s specification.
• Steps:
  1. Review the compliance matrix, focusing first on the high-risk, flagged requirements.
  2. For each requirement, mentally query internal product knowledge to determine if a standard Justrite product/component complies.
  3. If a direct match isn’t obvious, consult internal datasheets or engineering colleagues to find the best-fit solution.
  4. If a requirement cannot be met, or can be met with an equivalent-but-different component, formulate a “deviation.”
  5. Manually enter the selected product codes into the Salesforce CPQ system, which has no intuitive guidance.
  6. Simultaneously, build a separate list of all identified deviations and the technical rationale for each.
• Outcome: A configured Bill of Materials in CPQ and a separate, unstructured list of technical deviations ready to be incorporated into the final proposal.

5. Scoping the Work (Possible × Feasible)

• Knowledge Required:
  • Metacognitive: The expert’s ability to know which requirements to worry about most and how to approach solving them. This is the highest form of knowledge in the process.
  • Conceptual: Understanding the fine line between “compliant,” “compliant with equivalent,” and “non-compliant.”
  • Factual: Deep knowledge of the entire Justrite product catalog, including thousands of individual components and their specifications.
• Cognitive Action (Feasibility):
  • The core human task is Evaluating (judging compliance) and Creating (formulating a solution and justifying deviations). These are high-order cognitive actions. The opportunity for AI is not to replace this judgment, but to augment it by handling the lower-order tasks of Remembering (product specs) and Applying (comparison logic), which are highly feasible for AI.

6. Friction Map & Stakeholder Lens

• Operator (The Proposals Engineer): The “Tacit Knowledge” Bottleneck. “It all tends to be reliant on the individual’s product knowledge… a person in theory could potentially select the wrong option.” The entire process lives in the expert’s head. This creates immense pressure, is prone to error, and makes the knowledge impossible to transfer or scale.
• Orchestrator (The Project Orchestrator): The “Reinvention” Cycle (Rework). Without a systematic way to capture past deviation decisions, the team is “sleepwalking into the same issues.” Each new engineer must re-learn the same hard lessons, leading to inconsistent proposals and repeated internal consultations.
• Executive (The Executive Sponsor): The “Uncaptured IP” Risk (Knowledge Gap). The most valuable intellectual property—the expert logic of how to solve a client’s problem with Justrite products—is undocumented. If the expert leaves, that IP walks out the door, representing a significant, unmitigated business risk.

7. SEA Intent (Primary)

• Enrich: The primary goal is to make the configuration and deviation process better. We aim to codify expert logic into a repeatable, auditable system that produces higher-quality, more consistent technical solutions and justifications, effectively creating a “digital expert” that assists the human operator.

8. TOTE Loops (for each step)

• TOTE for Steps 1-4: Compliance & Deviation Analysis
  • Test 1: Is the validated requirements list un-compared against Justrite’s product capabilities?
  • Operate: For each requirement, an AI agent queries a knowledge base of Justrite’s technical datasheets and past project decisions. It proposes a status: “Fully Compliant,” “Compliant with Equivalent,” or “Deviation Required,” and provides the specific internal product spec or past rationale that supports its conclusion.
  • Test 2: The engineer reviews the AI’s suggestions in a three-column interface: [Client Requirement] → [AI Suggestion & Rationale] → [Engineer’s Final Decision]. The engineer’s role is to validate, override, or refine the AI’s work.
  • Exit: A fully annotated compliance matrix exists, with every line item having a human-validated status and a data-driven rationale.
  • Test (generic — gates): Before exit, verify Variant_Decision (if variants detected), Power_Decision (spec vs offer), Environmental_Minima evidence, and Regulatory_Declarations (attached or RFI) are satisfied for all High‑Risk rows.
• TOTE for Steps 5-6: Configuration & Deviation Logging
  • Test 1: Is the final, validated compliance matrix ready to be actioned?
  • Operate: The AI agent performs two actions simultaneously: 1) It generates a pre-formatted list of all required product codes for easy entry into the CPQ system. 2) It generates a formal, structured “Deviation Report” that lists each deviation and the human-validated justification.
  • Test 2: The engineer confirms that the product list matches the solution and that the deviation report is clear, complete, and ready for the customer.
  • Exit: A structured, error-free list of products is ready for quoting, and a formal, client-ready deviation report is generated, eliminating the need for manual transcription.

9. Light Automation / AI Assist (Lovable Interventions)

• To eliminate the painful Repetitive Administrative Work of… manually searching through thousands of product codes in a non-intuitive CPQ system, the AI can… act as a “configuration caddy” that suggests the right product based on the requirement and provides the exact code to enter. This frees the operator to focus on the high-value “judgment work” of… confirming the overall technical solution is sound and elegant.
• To eliminate the painful Repetitive Administrative Work of… writing deviation justifications from scratch every time, the AI can… surface justifications from similar past projects (“We’ve seen this before on a Representative Past Project and here’s how we handled it”). This frees the operator to focus on the high-value “judgment work” of… tailoring that proven rationale to the current client’s specific context.

10. Retro (Workshop Questions)

• What is the one deviation you have to write over and over again? What if you never had to write it from scratch again?
• If the AI could act as a perfect “memory” of every decision made on past projects, which part of this stage would that help the most?
• How much faster could a junior engineer be onboarded if they had this “digital expert” guiding their configuration choices?

11. The Compounding Advantage

By codifying expert configuration and deviation logic, we transform the company’s most valuable, intangible asset—its problem-solving expertise—into a durable, scalable, and teachable system. This breaks the single-expert bottleneck, de-risks the business, and creates a platform for true organizational learning. Each new project makes the system smarter, creating a compounding advantage that competitors relying on individual heroics cannot match.

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Case File 04: Proposal Assembly & Handoff

Context: The deep technical work is done. The solution is configured, and the deviations are understood. Now, this complex technical reality must be translated into a clear, coherent, and compelling set of documents for both internal (Sales) and external (Client) audiences. This is a critical communication and translation step.

1. Value Pair

• What do we do that is genuinely valuable? We synthesize hundreds of discrete technical decisions into a clear, professional, and persuasive proposal package that communicates both competence and compliance.
• Why is that valuable to the people who receive it? For the Sales team, it provides a trustworthy technical foundation upon which to build a commercial offer. For the customer (EPC), it presents a “very, very professional response” that preempts questions and demonstrates a deep understanding of their needs, setting Justrite apart from the competition.

2. Value Chain Placement

Macro Value Chain → Case File 03: Solution Configuration → Case File 04: Proposal Assembly & Handoff → Outcome: Contract Awarded

3. Function → Task

• Function: Proposals Engineering & Sales Collaboration
• Task: Assembling the technical proposal and handing it off for commercial pricing.

4. Step Card (Signal → Goal → Steps → Outcome)

• Signal: A human-validated compliance matrix, configured product list, and deviation report are complete.
• Goal: To generate a complete, client-ready technical proposal and a clear internal handoff summary for the Sales team within one business day of completing the technical analysis.
• Steps:
  1. Manually create a summary document for the Sales team, explaining the proposed solution.
  2. Carefully detail the technical rationale for each deviation to ensure Sales understands its commercial implications.
  3. Assemble the various output documents (the quote from CPQ, the technical datasheets, the deviation report, the compliance matrix) into a single package.
  4. Write a cover letter or executive summary for the client.
  5. Send the internal summary to the Sales team for pricing.
  6. Wait for Sales to complete the commercial offer before the final package can be sent to the client.
• Outcome: A complete technical package is delivered to the Sales team, and a parallel, client-ready package is prepared, awaiting only the final commercial details.

5. Scoping the Work (Possible × Feasible)

• Knowledge Required:
  • Conceptual: Understanding the different information needs of a Salesperson versus a client’s Engineering lead.
  • Procedural: Knowing the correct format and sequence for assembling the final proposal package.
• Cognitive Action (Feasibility):
  • The primary cognitive action is Creating a set of new documents (summaries, cover letters) by synthesizing existing, structured information. This is a mid-to-high-order task that is highly feasible for modern AI, which excels at summarizing and reformatting existing, validated data into new forms.

6. Friction Map & Stakeholder Lens

• Operator (The Proposals Engineer): The “Translator” Burden (Undesirable Work). After completing the intense technical work, the engineer must switch contexts entirely and become a technical writer, manually creating summaries and re-packaging information. This is often seen as administrative Repetitive Administrative Work.
• Orchestrator (The Project Orchestrator): The “Cross-Functional Handoff” (Delay). This is a classic cross-functional handoff. Any lack of clarity in the engineer’s summary leads to a cycle of questions, meetings, and delays with the Sales team, stalling the entire quoting process.
• Executive (The Executive Sponsor): The “Inconsistent Messaging” Risk (Misalignment). If the technical rationale for a deviation is not communicated perfectly to Sales, they may misrepresent it to the client, creating commercial risk and undermining the expert image Justrite wants to project.

7. SEA Intent (Primary)

• Accelerate: While this will also Streamline the engineer’s work, the primary intent is to make the overall process faster. The biggest opportunity for delay in this stage is the back-and-forth between Engineering and Sales. By creating a perfect, unambiguous handoff object, we can dramatically shorten the time-to-quote.

8. TOTE Loops (for each step)

• TOTE for Steps 1-2: Internal Handoff Summary
  • Test 1: Is the validated solution ready for Sales review but no internal summary exists?
  • Operate: An AI agent ingests the final compliance matrix and deviation report and generates a standardized “Technical Handoff Summary” containing a high-level overview, a clear list of all deviations with their approved justifications, and a link to the full technical data package.
  • Test 2: The engineer reviews the one-page summary to confirm it accurately represents the technical solution.
  • Exit: A perfect, standardized “handoff object” is created, ready to be sent to Sales.
• TOTE for Steps 3-4: Client Proposal Assembly
  • Test 1: Are all the component parts of the proposal complete but not yet assembled into a single, professional package?
  • Operate: The AI agent takes the various documents (CPQ quote, datasheets, deviation report, compliance matrix) and assembles them into a single, professionally formatted PDF with a standardized cover page, table of contents, and a draft executive summary.
  • Test 2: The engineer reviews the assembled package to ensure all documents are present and correctly formatted.
  • Exit: A client-ready technical proposal is generated, awaiting only the final commercial offer from Sales.

9. Light Automation / AI Assist (Lovable Interventions)

• To eliminate the painful Repetitive Administrative Work of… manually writing a summary of your own work after you’ve just finished doing it, the AI can… generate the standardized “Technical Handoff Summary” instantly. This frees the operator to focus on the high-value “judgment work” of… having a quick, high-level conversation with Sales to confirm alignment, rather than wasting time writing emails.
• To eliminate the painful Repetitive Administrative Work of… manually assembling disparate documents, the AI can… act as an automated document assembler. This frees the operator to focus on the high-value “judgment work” of… performing a final, holistic review of the story the proposal tells, rather than fighting with PDF software.

10. Retro (Workshop Questions)

• What is the one piece of information that always gets lost or misunderstood in the handoff between Engineering and Sales?
• If you could have a “perfect handoff” every single time, how many hours would that save per week across the team?
• What does a “world-class” proposal document from a supplier look like to you? What elements does it contain?

11. The Compounding Advantage

By standardizing the internal handoff and external proposal assembly, we create a pattern for clear, consistent, and rapid communication. This reduces cross-functional friction, accelerates the sales cycle, and ensures that the deep expertise developed in the analysis phase is presented to the customer with maximum professionalism and impact. This repeatable communication pattern enhances the Justrite brand with every proposal sent.

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