# AIS · AI Implementation Specialists > The full text of ais-website, one copy, no navigation. AIS is a fractional AI team for B2B manufacturers and the industrial and wholesale distributors alongside them, across the Rust Belt. Named people and a monthly cost rather than a project vendor or a strategy deck. Pricing is published as ranges. Every system keeps a person in the loop and targets administrative and white-collar work only, never the shop floor. Offices in Dayton, Ohio and Indiana, Pennsylvania. The implementation arm of the 50 Family of Brands. Tagline: Operational AI, Implemented at Scale. Last updated 2026-09-08. Source pages: https://www.aiimplementationspecialists.com/ (home), /how-we-work, /what-we-build, /pricing, /proof. The short index is at /llms.txt. Absolute URLs point at the production host, https://www.aiimplementationspecialists.com. How to cite: "AIS (AI Implementation Specialists), a fractional AI team for Rust Belt manufacturers and distributors, www.aiimplementationspecialists.com". Figures on this page each came off one named deployment and are stated in that client's own terms, not averaged across a portfolio. Where a figure is a projection or a third-party statistic, it says so. --- ## What AIS is AIS is AI Implementation Specialists: the implementation partner for B2B manufacturers and distributors that need AI to work at scale. Not strategy decks. Not vendor lock-in. Systems that run, with a named person still standing beside the client in six months, when the tooling has moved again. The offer is a fractional AI team: named people, a monthly cost, working inside the client's operation. Not a project vendor, and not a strategy deck. Three things AIS does, in order: 1. Identify where AI pays for itself. Find the one constrained workflow with a number behind it. 2. Build the system around the client's people. The 80% Standard: the system does the repetition, the team keeps judgment. 3. Support it as the operation changes. Models get swapped underneath the client. The workflow above them stays. Where AIS sits, described as three layers: - The client's operation: repetitive work, running on people. The way it looks before AIS arrives. - The AIS layer: 80% handled by the system. Judgment, exceptions and approvals stay with the client's team. - The model layer: whatever model is best that quarter, swapped underneath the client. Headline figures on the homepage: 3–7x cash-on-cash return that pilots are priced to; 80% of the work done by the system with a person still approving; 100+ AI systems deployed. ## Who AIS is for One kind of company, stated specifically: B2B manufacturers and the industrial and wholesale distributors alongside them, run by the owner or the C-suite, across the Rust Belt. If that is not the prospect, AIS says so on the call rather than after the roadmap. The definition AIS works to: - Industry: B2B manufacturing, including industrial and B2B wholesale distribution. - Revenue: $10M – $3B. - Geography: the Rust Belt. Ohio, Pennsylvania, Indiana, Kentucky, Michigan, West Virginia and western New York. - Who AIS talks to: owner and C-suite. CEO, President, COO, VP Operations, VP Sales, CFO. A prospect will know it is them from this list rather than from the revenue band: - Not cloud-native, and never claimed to be. - Tribal knowledge living in PDFs, email and spreadsheets. - Fragmented systems that do not talk to each other. - No internal AI owner, and no plan to hire one. Average ROI across deployed systems: 3.2x. ### Scope exclusion Every engagement targets administrative and white-collar work. AIS will use the data a machine produces, but does not go onto the shop floor, and nothing AIS sells touches robotics, machine vision or predictive maintenance. This exclusion appears in every fractional team agreement and on the pricing page. ## The problem AIS is built around: two rational fears Neither fear is about whether AI works. AI moves faster than anyone with an operation to run can track, and the last six-figure integration returned very little. Both of those are reasonable positions. Market context cited on the site: - 60% of AI projects that lack AI-ready data are projected to be abandoned. Gartner projection, not a measured outcome. - 54% of organizations stopped or postponed an AI initiative inside two years. DDN, January 2026. The three fears, named: 1. Obsolescence. The ground moves while you run the company. Models, platforms and prices change faster than a leadership team with a plant to run can follow. That is not a gap in the client's knowledge, it is a full-time job, and it is AIS's. 2. Wasted capital. You have already paid for very little. Six figures to enterprise IT consultants, a multi-month integration, and not much at the end of it. Nobody makes that bet twice, so the first commitment here stays small and the ranges are published. 3. No owner. Nobody owns it after the demo. A vendor ships a demo, an internal team inherits it, and the pilot quietly flatlines because implementation was never anyone's actual job. Every AIS engagement has a named owner on the AIS side, not a queue. ## About AIS: the founding team The homepage carries a 3 minute 43 second film, between the hero and the two rational fears, introducing the people who build the systems. It does not autoplay, and it downloads nothing until a visitor presses play. Named on camera: - Will Woods, founding team, head of AI strategy. - Josh Hochstedler, founding team, lead systems architect. This is what "named people, a monthly cost" refers to: every engagement has a named owner on the AIS side rather than a queue. ## The 80% Standard An 80% project is not incomplete, it is deliberately scoped. The system does the repeatable middle. Judgment stays with people. Enough to control, enough to fund, enough to prove. - Clears straight through (handled by the system): classify, extract, validate, draft, route. - Needs judgment (stays with the client's team): approval stays with a person. Exceptions go to a named owner. Nothing runs unattended. ## Integrations 36 platforms supported today, plus any documented API. AIS does not force new software. Platforms shown on the site: OpenAI, Claude, Gemini, Meta, Mistral AI, DeepSeek, Qwen, Cursor, Replit, Windsurf, v0, Claude Code, Bolt, n8n, Zapier, Make, Langflow, HubSpot, Salesforce, Zoho CRM, WordPress, Shopify, WooCommerce, Webflow, GraphQL, LangChain, Webhooks, Google Analytics, Looker, AI Dashboards. --- ## How we work: six phases Page: https://www.aiimplementationspecialists.com/how-we-work A disciplined path from problem to proven system. The same six phases run every engagement, in the same order, and each one ends at a gate the client signs off before the next begins. ### Phase 01 · Discover: find the number worth moving AIS maps the workflow with the people who actually run it, finds the one constrained seat worth attacking first, and costs the current state in hours and dollars. Nothing gets built until both sides agree on the number they are trying to move. - Deliverables: a baseline you can defend in a board meeting; a ranked map of every opportunity found; the one workflow AIS recommends starting with. - Typical length: 2–3 weeks. AIS sits with the client's operators. ### Phase 02 · Design: the approval path, before the code AIS defines the future state in detail: what the system handles, where it hands off, and exactly where a person sits in the loop. The client sees the approval path before a single line of code exists. - Deliverables: solution blueprint and data flow; the human review path, written down; success criteria signed off by both sides. - Typical length: 1–2 weeks. Sign-off gate before build. ### Phase 03 · Build: working versions, not a big reveal AIS develops the agent, connects it to the client's systems, and captures documentation as it goes rather than after. Three to four of those weeks are the build itself, and that holds only when every piece of data AIS asks for arrives on the date it asks for it. Inside a larger organization, expect one to two months instead. - Deliverables: a working system on the client's infrastructure; documentation written during the build; weekly testable versions. - Typical length: 3–4 weeks when the data lands on time. Code ownership: the client's. ### Phase 04 · Test: run it against the baseline AIS runs the system against the baseline with real data and real reviewers. The client's team reviews live outputs, AIS tunes against what they flag, and then both sides make a go or no-go call together. - Deliverables: validation report against the baseline; reviewer feedback folded into the system; an honest go or no-go recommendation. - Typical length: 1–2 weeks. Tested on real data. ### Phase 05 · Deploy: launch is a process, not an email Training, SOPs, monitoring and a named support owner on the AIS side all ship with the system, because adoption is where most AI projects quietly die. - Deliverables: live system with monitoring in place; SOPs and team training delivered; a named owner on the AIS side, not a queue. - Typical length: 1–2 weeks. ### Phase 06 · Optimize: hold the number, then move the next one AIS measures realized ROI against the original baseline, refines what drifted, absorbs model and platform changes, and scopes the next adjacent workflow once this one is holding. - Deliverables: monthly impact report against baseline; tuning as the operation changes; a scoped next step, when the client is ready. - Cadence: ongoing. Impact reporting: monthly. ### How AIS de-risks it The client never buys the build before they have the number. The baseline is agreed before the work starts. If a pilot does not beat it, AIS says so and does not recommend expanding. And when the ecosystem shifts underneath the build, which it will, AIS pivots the strategy rather than writing off the investment. ### Scope notes The phase lengths above hold when every piece of data AIS asks for arrives on the date it asks for it. Inside a larger organization, expect one to two months for the build phase instead of three to four weeks. --- ## What we build: six narrow pilots Page: https://www.aiimplementationspecialists.com/what-we-build Narrow pilots. Measurable lift. Each one is a single constrained seat with a costed baseline, a human approval gate, and a number agreed before the build. The discovery-to-live windows hold when every piece of data AIS asks for arrives on the date it asks for it. ### 1. RFQ to Quote Draft Agent (manufacturing and industrial) RFQs get read, specs extracted, requirements matched to catalog SKUs, and a quote draft prepared for engineering review. - Average turnaround: 5 days to 6 hours. - 3x more quotes out. - Discovery to live: 8 weeks. ### 2. Invoice Processing Agent (finance, accounts payable) Invoices arrive by email, portal and PDF. The system extracts, matches and validates; only true exceptions reach a human. Nothing gets paid on its own. - 85% less processing time. - 3% of invoices reach a human. - Discovery to live: 6 weeks. ### 3. Ticket Classification and Routing (customer operations) Every ticket classified, prioritized, routed, and handed over with a drafted first reply attached. Agents keep control. - 60% faster first response. - 4 channels unified. - Discovery to live: 4 weeks. ### 4. Call Prep and Follow-up Agent (sales) A brief before the call. The follow-up email and CRM update drafted the moment it ends. Reps review and send. - 2.4x more follow-through. - 9 minutes saved per call. - Discovery to live: 3 weeks. ### 5. Onboarding Orchestrator (client operations) Contract signed: accounts provisioned, boards populated, kickoff brief drafted, owners assigned. - 48 hours from signed to kickoff-ready. - 11 handoffs removed. - Discovery to live: 5 weeks. ### 6. Answer-Engine Visibility Agent (marketing) Tracks how the client is cited in ChatGPT, Gemini and Perplexity, and drafts the rewrites, FAQ and schema that close the gaps. - 4.1x more AI citations. - 90 days to first movement. - Discovery to live: 3 weeks. ### How one seat runs Work arrives → the system classifies, extracts, drafts → a person approves → the system routes, logs and updates the baseline. ### Eight ways AIS puts AI to work Internal automations that make the client's team faster, or client-facing systems under the client's own brand. 1. AI Automations. Remove the repetitive manual work sitting across sales, operations, marketing and reporting. These pay for themselves fastest, because the baseline is easy to measure. Examples: proposal generators, meeting to CRM, automated reporting. 2. Agentic AI Workflows. Multi-step workflows where several agents collaborate across the client's systems, each with a bounded job and an explicit handoff. A person signs off at the end. Examples: client onboarding, search audits, campaign setup. 3. Custom AI Agents. Agents built for one named role rather than one generic assistant for everyone. Trained on the client's material and documented well enough to hand off tomorrow. Examples: lead qualification, scope generators, RFP analysis. 4. AI App Development. When the workflow needs an interface, AIS builds it. Internal tools the team lives in, or products shipped under the client's own brand, on the client's cloud with the client's data. Examples: support chat, knowledge assistants, client portals. 5. Research and Knowledge. Retrieval and intelligence layered over the documents and data the client already owns. Most companies have their largest untapped asset here. Examples: RAG on the client's corpus, knowledge bases, competitive intel. 6. Client-Facing AI. AI the client's customers interact with directly, across web, WhatsApp, Instagram and Messenger. Guardrails, escalation paths and tone are designed before launch. Examples: 24/7 support bots, qualification, voice agents. 7. Intelligence and Visibility. Two problems, one system: getting found inside AI answers, and turning analytics into decisions instead of dashboards nobody opens. Examples: LLM visibility, GEO and AI SEO, predictive analytics. 8. Content and Publishing. From idea to published, at volume, with a human approving the work before anything goes out. Trained on the client's voice, past work and standards. Examples: blog generation, repurposing, translation to 80+ languages. ### Assumed, not confirmed Every discovery-to-live window on this page assumes each piece of data AIS asks for arrives on the date it asks for it, and every number came off one real deployment rather than an average. A prospect's own numbers get costed on their own baseline before anything is built. --- ## Pricing: four rungs, ranges published Page: https://www.aiimplementationspecialists.com/pricing Published so a prospect can rule AIS in or out before spending thirty minutes on a call. Every engagement sits on one of these four rungs. Bespoke totals inside a range stay private until scope is agreed. ### Rung 01 · Scoping · Roadmap · $2,500 Paid before the roadmap call is booked. High level by design. It tells AIS where to start, and it is built into every pilot rather than priced twice. A ranked, costed shortlist of what is worth attacking, not a maturity score. Best when leadership knows AI should be doing something but nobody can agree on what comes first. You get: where to start, ranked and costed. ### Rung 02 · Build · Pilot project · $25,000 – $75,000 Per project, roughly 90 days. The foot in the door. One person, one seat, one major function, proven quickly. Training, SOPs and monitoring ship with it, and if it does not beat the baseline agreed on, AIS says so. Best when you want the number proven on one real seat before anything expands. You get: live system, docs, trained team. ### Rung 03 · Care · Operate · $2,000 – $5,000 per month Attached to a pilot. Uptime, monitoring and a monthly report on what was built. Care and hosting for the system the client now runs, so it does not quietly drift as the operation changes around it. Best when one workflow is live and holding, and you want it kept honest rather than expanded yet. You get: uptime, monitoring, monthly report. ### Rung 04 · Flagship · Fractional AI team · $15,000 – $50,000 per month Bought alone, rolling term. The enterprise offering. Standing capacity, workflows in flight, and the monthly value report. Named people and a monthly cost, not a project vendor and not a strategy deck. Best when you would rather have someone competent watching the landscape on your behalf than buy another project. You get: named people, standing capacity. Three months of the fractional team at $15,000 is $45,000, roughly what a pilot costs. That is deliberate: a client who does not need the proof first can start at the top without repricing the relationship later. ### How a pilot gets priced: not off a rate card Two things set the number: how severe the pain is, and what the fix returns. AIS sizes the saving with the people who do the work, then prices so the client's cash-on-cash return lands between three and seven times. What makes something a pilot rather than a larger build is how hard it is for AIS to deliver, not what AIS could charge for it. That is also why totals are not on the page: the range is honest, a single published number would not be. The arithmetic, worked (one example, not a formula): - The workflow: quote drafting. - Saved per salesperson: 2 hours a week. - Salespeople on it: 10. - Recovered a week: 20 hours. - Priced to return: 3–7x cash-on-cash. AIS runs the same arithmetic on the prospect's own numbers during the call, before anyone quotes anything. ### Every fractional team agreement names four things 1. A unit of value. One measure that matters to the client, in their own terms, tracked every month. 2. A capacity ceiling. How many workflows can be in flight, and how many build hours a month. Written down before signing. 3. An exclusion block. What is out of scope, stated in the agreement and published on the pricing page. 4. A value report. What moved, measured against the baseline, plus one standing next recommendation. Two of the four rungs are monthly on purpose. An offer that ends at delivery has to be resold every quarter, and it leaves the client at the exact moment the tooling shifts. ### Not included at any rung Anything on the shop floor. Every engagement targets administrative and white-collar work, and nothing AIS sells touches robotics, machine vision or predictive maintenance. --- ## Proof: real builds, real numbers Page: https://www.aiimplementationspecialists.com/proof Proven inside the 50 Family of Brands first. Every engagement below is measured against a baseline set before a line of code, including the one that did not work. Headline figures: - 144 hours a week given back to one team by an agentic Client Hub. - 83,000 record database rebuilt into an industry moat. - Proposal effort from 20 hours to about 2, with 10% human input and 80% agentic. - $12K – $18K a week in senior capacity recovered. ### Case 01 · An 83,000 record database, turned into an industry moat A scrapped pilot left a large proprietary dataset unused. AIS rebuilt it as a bounded agentic intelligence layer with explicit guardrails and token limits. 83,000 records. Bounded guardrails. Token limits by design. ### Case 02 · 144 hours a week, given back to the team An agentic Client Hub records calls, processes transcripts, populates project management and drafts creative briefs. People review every output. 144 hours a week returned. Human review on every output. ### Case 03 · From 20-hour proposals to 10% input, 80% agentic A human-in-the-loop generator trained on the firm's SOPs, past proposals and case studies. Roughly $12K – $18K a week in recovered senior capacity. Effort split 20 hours to about 2. Trained on the firm's own work. ### Case 04 · The build that failed, and why AIS publishes it AIS's first second brain pushed portfolio data straight into LLMs with no retrieval architecture. Results were inconsistent, so AIS scrapped it. That lesson now shapes every build shipped, and AIS paid for it itself. Published failure. Data without architecture fails. ### In-house first: the 50 Family of Brands AIS is the implementation arm of the 50 Family of Brands. Nothing reaches a client until it has survived the family's own operations, on its own money. 1. AIS. Builds and runs every system in the family first. 2. 50 Marketing. Agency operations, proving ground for the Client Hub. https://www.50marketing.com/ 3. 50 Capital Growth. Capital and growth advisory. 4. 50 Motion. Video and production, a human approving every cut. 5. Own the Answer. The lab behind the AEO audits and citation systems. 6. Expo Woods. Manufacturing and eCommerce. Real RFQs, real inventory. ### The Missing Half Podcast AI, in plain English, from the people building it. Episode 88: what it actually takes to make AI useful inside a business. Small pilots, the 80% solution with a human in the loop, one seat first, and what a CEO should do first. - EP 87 · A GTM Framework for Portfolio Companies · Ed Marsh · AI as capability, not cost cutting. https://www.50marketing.com/podcast/private-equity-organic-growth-sales-gtm-ed-marsh/ - EP 85 · How Smart Manufacturers Are Winning with CX and AI · Brian Morrell · simple tools, AI-driven buying. https://www.50marketing.com/podcast/b2b-manufacturing-marketing-direct-sales-strategy-brian-morrell/ - EP 83 · AI in Manufacturing and PE · Sebastian Chedal · tribal knowledge, past proof of concept. https://www.50marketing.com/podcast/ai-manufacturing-tribal-knowledge-sebastian-chedal/ - All AIS episodes: https://www.youtube.com/playlist?list=PLU95maVLjZATFyuaXQpDr8YSHAz4AOvwk - Channel: https://www.youtube.com/@themissinghalfpodcast ### What these numbers are Each figure came off one named deployment and is stated in that client's own terms, not averaged across a portfolio. AIS publishes the build that failed for the same reason: a number you cannot check is worth nothing. --- ## Frequently asked questions The questions every operator asks AIS first. Answers verbatim from the homepage. **What if the pilot does not beat the baseline?** We tell you, in writing, and we do not recommend expanding. The baseline is agreed before we build precisely so neither side can move the goalposts afterwards. A no-go recommendation is a normal outcome of the Test phase. **Are we locked into you afterwards?** No. You own the code and the prompts, everything runs on your cloud, and every build ships with documentation written during the work rather than after it. Your team or another vendor can take it over tomorrow. **Do we have to replace our current software?** No. We integrate into the stack you already run across 36 supported platforms, and we build against any documented API. We do not sell you new software and we are not tied to a single model vendor. **Will this replace our people?** The 80% Standard exists because it will not. The system handles the repeatable middle of the work and routes judgment, exceptions and approvals to a person. Nothing runs unattended. **How fast do we see a number?** The discovery call gives you a rough baseline in thirty minutes, free. The $2,500 roadmap tells us where to start. A pilot runs roughly ninety days, and individual builds inside it land in three to four weeks, though that only holds when every piece of data we ask for arrives on the date we ask for it. In a larger organization, expect one to two months instead. **What happens when the AI landscape shifts again?** It will, probably more than once this year. When the ecosystem moves we pivot the strategy rather than write off the investment. We absorb the model and platform changes on our side and tell you only what actually matters to the workflows you are running. That is what the retainer is for, and it is why two of the four rungs are monthly rather than one-off. **Why are your prices on the website?** Because the ranges are honest and rate-card theater is not. All four rungs are published so you can rule us in or out before you spend thirty minutes on a call. Bespoke totals stay private, because they depend on how severe your pain is and what the fix returns, and no single published number would be true for everyone. **How do we know this works before we hire you?** Every system we sell has been run on our own operations inside the 50 Family of Brands first, on our own money. We publish the build that failed, too. --- ## Next step Thirty minutes, free. AIS pinpoints one constrained workflow where AI can pay for itself, runs the arithmetic on the prospect's own numbers, and says plainly if it does not think there is anything there. 1. You describe the bottleneck. The workflow that eats the most hours, or the one that keeps breaking. 2. AIS sizes the opportunity live. Baseline hours, cost, and the realistic 80% split, on the call rather than in a follow-up deck. 3. You get a scoped next step. The $2,500 roadmap, a pilot range, or an honest no. Nothing is paid before this point. Book the discovery call: https://calendly.com/aiimplementationspecialists/30min Not ready to talk? The AI Readiness Audit is a free, low-risk diagnostic that surfaces where the ROI actually is before any commitment. What it finds feeds into the roadmap. It is reached from the site's calls to action and is deliberately not indexed. ## Locations and identity - Dayton, Ohio and Indiana, Pennsylvania. Serving the Rust Belt. - Legal and brand name: AIS · AI Implementation Specialists. Short form: AIS. - Part of: the 50 Family of Brands, alongside 50 Marketing. - Tagline: Operational AI, Implemented at Scale. ## Glossary, in AIS's own terms - The 80% Standard: the system handles the repeatable 80% of a workflow; a named person keeps the judgment, exceptions and approvals. Nothing runs unattended. - Seat: one person's constrained workflow, the unit a pilot is scoped around. One person, one seat, one major function. - Rung: one of the four priced offerings. Roadmap, Pilot project, Operate, Fractional AI team. - Discovery to live: the window quoted for each pilot on What we build, conditional on every requested piece of data arriving on the date asked. Each figure came off one real deployment rather than an average. - Fractional AI team: standing capacity with named people on a monthly cost, bought alone on a rolling term. The flagship offering. - Baseline: the costed current state, in hours and dollars, agreed before anything is built, and the number every result is measured against.