How a 79-Year-Old Manufacturer Deployed AI Across 7 Departments in 6 Months

The starting point

When we first sat down with Orient's director, Rishab Kohli, the picture was familiar. A company with deep domain expertise, decades of operational knowledge, and a growing sense that AI could help — but no clear path from interest to implementation.

Orient isn't a startup. They have seven departments: Marketing & Sales, Design, Supply Chain, Production & Maintenance, Accounts, HR & IT, and Servicing. They run a custom-built ERP. Their product catalogue spans offset printing presses, flexographic presses, inkjet digital presses, folder gluers, and converting machines. The complexity is real.

The question wasn't “can AI help?” — it was “where do we start, and how do we avoid the pilot that goes nowhere?”

Discovery: 49 use cases in 7 departments

We started where we always start: mapping workflows. Not at a strategy level — at the task level. What does someone in procurement actually do when they need to source a component? What does a sales engineer do when a customer asks for a quote on a C-Series digital press? Where do errors happen? Where does work pile up?

Over two weeks, we documented every repeatable workflow across all seven departments. The result was a use-case matrix of 49 distinct opportunities, each scored by impact, feasibility, and dependencies.

Some examples of what we found:

• Sales was spending 3–4 hours per offer document— manually pulling pricing from spreadsheets, formatting specifications, attaching the right terms and conditions (different for domestic vs. international), and assembling an 8-page branded PDF. They were producing dozens of these per month.
• Supply Chain was writing RFQs from scratch every time, despite 80% of the content being templatable. Vendor comparison reports were manual Excel exercises that took a full day.
• Service engineers were troubleshooting from memory, calling senior colleagues, or digging through physical manuals. There was no searchable knowledge base.
• HR was writing job descriptions ad hoc, producing inconsistent postings across recruitment portals. Payroll processing involved manual PF, ESI, and TDS calculations every cycle.
• Production reviews relied on manually assembled presentations that took hours to compile from scattered data sources.

None of these were unsolvable problems. But collectively, they represented hundreds of hours per month of work that could be structured, accelerated, or eliminated entirely.

Architecture: 18 projects, not 7

The instinct in most AI deployments is to organise by department: one AI project for Sales, one for HR, one for Production. We tried this initially and quickly found it didn't work.

The problem is that use cases within the same department often need fundamentally different context. Marketing's “Offer Creation” needs a pricing database and terms and conditions files. Marketing's “SEO Workflow” needs web search access and keyword data. Cramming both into the same project meant bloated context windows, confused instructions, and unreliable output.

So we split the rollout into 18 functional projects grouped by workflow cluster:

• Sales Proposals & Pricing— offer creation, instant price generation, configuration recommendations
• Sales Communications— CRM updates, automated follow-ups, outreach drafting
• Vendor Management & Procurement— vendor discovery, RFQ generation, purchase orders, cost analysis
• Service & Troubleshooting— AI troubleshooting assistant backed by technical manual knowledge base
• Financial Operations— invoice generation, MIS reports, Excel analysis
• Recruitment & Talent— job descriptions, KRA/KPI generation
• Payroll & HR Operations— salary sheet generation with Indian statutory compliance (PF, ESI, TDS)
• ERP Development Assistant— coding assistant for their custom-built ERP system

Each project got its own instructions, its own knowledge files, and its own set of rules. This meant every project could be optimised independently, tested independently, and deployed independently.

The four-tier phased rollout

Not all 49 use cases could ship at once. Some needed nothing more than well-written instructions. Others required integration with Orient's custom ERP. A few depended on external systems that didn't exist yet.

We designed a four-tier rollout:

• Tier 1: Quick Wins (Weeks 1–4)— 14 use cases that needed only project instructions and knowledge files. No integrations, no custom development. Email writing across all departments, instant price calculations, job description generation, Excel analysis, ERP coding assistance.
• Tier 2: Structured Documents (Months 2–3)— 14 use cases requiring document generation capabilities. Offer creation with branded PDFs, BOM generation, RFQ templates, vendor reports, production review presentations, payroll processing.
• Tier 3: ERP Integration (Months 3–6) — 14 use cases that needed a custom connector to Orient's ERP system. Purchase order creation, inventory tracking, invoice generation, sales forecasting, automated reorder alerts.
• Tier 4: Advanced AI (Month 6+)— 7 use cases requiring external system integration. AutoCAD script generation, predictive maintenance from IoT sensors, AI travel desk with booking APIs, image and video generation.

This tiering was critical. It meant Orient could start seeing results in the first month while the more complex integrations were being developed. By the time Tier 3 rolled out, the team had already been using AI daily for three months. Adoption wasn't a problem — it was a habit.

Instruction engineering: the offer generator

The offer generator became our flagship deployment — and the best example of what instruction engineering actually looks like in practice.

Before AI, creating a customer offer for a digital press took 3–4 hours. A sales engineer would pull pricing from a master spreadsheet (five sheets covering C-Series 600/1200 and L&P 600/1200 configurations plus extra colour options), manually calculate head counts based on print width and colour configuration, apply the 20% gross margin, format the specification, select the right terms and conditions (domestic vs. international), and assemble everything into a branded 8-page document.

After deployment, the same process takes 30 minutes.

The system works in two steps. First, the sales engineer enters the machine specification into a Claude project configured with Orient's pricing logic, product knowledge base, and full terms and conditions. Claude calculates the correct pricing — including head count formulas, add-on components (unwind systems, IR drying, coating units, RIP software, sheeters), and installation costs — and outputs structured data across five sections: cover data, machine specification, equipment pricing, T&C reference, and delivery terms.

Second, that structured output feeds into a dashboard tool that generates a branded 8-page DOCX with Orient's boilerplate pages (company overview, product introduction, client logos, press configuration diagrams) and the calculated pricing pages.

The instructions include safety rules: never reveal internal costs or partner margins to the customer. Review gates require confirmation before finalising pricing on non-standard configurations. The output format is locked to Orient's brand standards.

This is what we mean by instruction engineering. It's not a prompt. It's a production system.

What shipped in the first engagement

Eleven projects went live in the first phase:

• Offer Generator— 85% reduction in document creation time. Previously 3–4 hours, now 30 minutes. Dozens of offers generated per month.
• Instant Price Calculator— real-time pricing from natural language input. Sales engineers get accurate quotes in seconds instead of manually navigating pricing spreadsheets.
• Configuration Suggestor— customers describe their printing requirements, the system recommends the optimal machine configuration. Reduced back-and-forth between sales and engineering.
• Email Writer (all departments)— context-aware email drafting tuned to Orient's tone and terminology. Deployed across Marketing, Supply Chain, Production, Accounts, HR, and Servicing.
• RFQ Generator— templated request-for-quote documents generated from component specifications. Cut procurement preparation time by 60%.
• Vendor Analysis Reports— automated vendor comparison reports from uploaded cost data. What used to take a full day now takes under an hour.
• Service Troubleshooting Assistant— AI-powered diagnostics backed by Orient's technical manuals. Engineers describe symptoms, get ranked root causes and diagnostic steps. Reduced average troubleshooting time and dependence on senior staff for routine issues.
• BOM Generator— structured bills of materials from order specifications. Automated what was previously a manual, error-prone process.
• Job Description Generator— manufacturing-context job descriptions with consistent formatting across all recruitment portals.
• Excel AI Assistant— natural language analysis of financial and operational spreadsheets. Accounts team uses it daily for data analysis without writing formulas.
• ERP Coding Assistant— development support for Orient's custom ERP system. The IT team loaded the ERP schema into the project's knowledge base, giving Claude full context on their codebase.

The numbers

After 90 days of production use:

• Document generation time: 85% reduction — offers, RFQs, BOMs, reports, and presentations that previously took hours are now produced in minutes.
• Estimated $200,000+ in annual labour savings — calculated across all deployed use cases based on hours saved per task multiplied by frequency and fully-loaded employee cost.
• 400+ hours saved per monthacross all departments — from eliminated manual document assembly, reduced troubleshooting time, automated procurement prep, and streamlined communications.
• Task-level time reduction: 4 hours → 30 minutes on the highest-impact use case (offer generation), with similar ratios across RFQ creation, vendor analysis, and production reporting.
• Error reduction in pricing— instruction-enforced calculation logic eliminated the manual errors that previously occurred when sales engineers navigated complex pricing spreadsheets by hand.
• 11 custom skills built, including a pricing calculator, configuration suggestor, BOM generator, Indian payroll processor with statutory compliance, and a troubleshooting assistant.

What's next

Orient is now in Tier 3 — building a custom connector to their ERP system. This will unlock the remaining 14 use cases that require live data: automated purchase orders, inventory tracking, invoice generation, sales forecasting, and reorder alerts.

Tier 4 is on the horizon: AutoCAD script generation for the Design team, predictive maintenance from machine sensor data, and an AI-powered travel desk for the Service team's field visits.

The longer-term vision is productisation. Orient plans to take the use cases that delivered the strongest ROI internally and rebuild them using the Claude API and Agent SDK — offering them as AI-powered tools to other printing and packaging companies worldwide.

From a 79-year-old manufacturer that had never used AI to a company deploying it across every department, with a roadmap to productise it for their industry. That's what structured deployment looks like.