Generic chatbots fail because industries do not share the same workflows, systems, or risk. GreeLogix designs production AI around how transportation, hospitality, SaaS, healthcare, real estate, and ecommerce actually run — then says no when automation is the wrong lever.
150+ products shipped · 75+ AI systems · Engineer-led · QA sign-off
30 minutes · Senior engineer · No commitment
The same delivery pattern for every industry cluster — add a config, not a one-off page.
We inventory the operational pain — missed handoffs, after-hours demand, manual data entry — before choosing a model.
Each use case is scored for ROI, integration risk, and whether software or staffing is a better answer than an LLM.
Chat, voice, document extraction, or background automation — whichever matches how the work actually moves.
CRM, PMS, TMS, EHR, Shopify, or custom APIs. Write-back and audit trails are in scope, not a phase two surprise.
Undefined processes, one-off creative work, or missing data access get a no-go — and a recommendation for what to do instead.
PII boundaries, least-privilege keys, and human fallback for sensitive topics are designed in, not bolted on at launch.
Typical opportunity classes. Dedicated industry pages will expand each cluster without duplicating this template.
After-hours support, booking changes, order status, and FAQ deflection that still escalates with full context.
Lead qualification, quoting assists, and follow-up that write into the CRM instead of dying in a chat transcript.
Document intake, scheduling, status sync, and exception routing between the tools the industry already runs.
IVR and after-hours call handling for verticals where the phone is still the primary channel.
Teams evaluating industry ai solutions usually hit these walls before a production system exists.
Off-the-shelf chat widgets ignore how the industry actually routes work, exceptions, and compliance.
The model answers in a sandbox but never updates the CRM, PMS, EHR, or ops tool of record.
Prompts, APIs, and policies drift. Nobody is on the hook when the automation quietly degrades.
Some workflows need better software, QA, or staffing — not an LLM bolted onto a broken process.
150+ products shipped · Engineer-led delivery · QA sign-off on production AI.
We map the operating process before choosing a model, vendor, or chatbot skin.
CRM, helpdesk, telephony, and ops write-back — not a widget that dumps transcripts into email.
We will say when AI automation does not make sense and recommend software, QA, or process work instead.
Laravel, React, n8n, and LLM systems from one accountable squad — including post-launch tuning.
Typical industry ai solutions path from discovery to a measured production slice.
Stack is chosen for the vertical's systems of record. We do not force a single chatbot template onto every industry.
We scope what is indexed, what can be logged, and which fields never enter a prompt — aligned to your contracts.
Least-privilege API keys, environment vaults, and role-aware bot behavior for staff vs customers.
Low-confidence and sensitive topics escalate with transcript context instead of guessing.
“What a 70-engineer team could not deliver, a small senior team at GreeLogix shipped. The app went from stuck to live across mobile, web, and backend.”
Structured answers for search engines and AI assistants — definition, fit, cost, timeline, and comparisons.
Structured answers for founders, CTOs, and procurement — written for clarity in search and AI assistants.
Industry AI solutions from GreeLogix are production automations, chatbots, voice agents, and integrations designed around how a specific vertical actually operates — not a generic widget dropped onto every homepage. We map workflows, systems of record, and compliance boundaries first, then ship a measured pilot with QA sign-off.
Typical timeline: Narrow chatbot or workflow: 2–4 weeks. Multi-system industry slice: 4–8 weeks. Managed tuning: monthly retainer.
GreeLogix pricing tiers: Discovery & Pilot: $5,500 – $14,000 — Vertical discovery, prompt design, and a narrow production pilot. Production Build: $14,000 – $45,000 — Full voice, chatbot, or automation rollout with guardrails and monitoring. Managed AI Ops: $4,500 – $12,000/mo — Ongoing tuning, model updates, and incident response for live AI systems.
Narrow chatbot or workflow: 2–4 weeks. Multi-system industry slice: 4–8 weeks. Managed tuning: monthly retainer. Phases: Discovery (Week 1); Pilot architecture (Weeks 2–3); Production build (Weeks 3–8 (2–4 weeks for a narrow bot; 4–8 weeks for a product slice)); Launch & optimize (Days 30–90).
Compared to alternatives — Off-the-shelf chatbot SaaS: choose when Generic FAQs with no CRM write-back or industry workflow; In-house ML team: choose when You already have production ML engineers and want to own model training; DIY Zapier / Make: choose when Simple two-step automations with no custom logic, RAG, or compliance needs; Industry software vendor: choose when A vertical SaaS already covers the workflow and you only need configuration. Choose GreeLogix when you need production reliability, fixed milestones, and engineer-led delivery with QA sign-off.
Talk to a senior engineer — scope, timeline, and cost range in one 30-minute call. No sales script.
Most AI vendor pages pretend every business is a SaaS support queue. Transportation dispatch, hotel ops, clinic intake, property inquiries, and ecommerce exceptions do not share the same objects, SLAs, or compliance surface. This hub is the parent for GreeLogix industry clusters under /ai-solutions/{industry}/ — each cluster is a structured config, not a forked landing page.
Existing GreeLogix work already covers adjacent paths: software delivery under /industries/*, outcome pages under /solutions/*, and capability pages such as /ai-automation and /ai-consulting. Industry AI solution pages sit between those layers — vertical enough to rank and sell, specific enough to say when automation is the wrong buy.
AI automation is a fit when a workflow is high-volume, repeatable, and already lives in systems we can read or write. You should be able to name a baseline metric — handle time, after-hours missed calls, lead response, or manual hours — and accept a human fallback for exceptions.
We will not recommend an LLM because a competitor bought a chatbot. If the operating process is undefined, the data is inaccessible, or the task is a one-off judgment call, we recommend software, QA, or operations design instead — and we will say so on a discovery call.
Cost is driven by integration depth, whether the system must write back, retrieval over private content, voice/telephony, and how much monitoring you want after go-live. Narrow pilots land in the low thousands; multi-system industry slices and retainers scale from there. We quote fixed milestones after discovery — not a mystery hourly burn.
Industry AI almost always touches customer or operational data. We scope what is indexed, what can be logged, which fields never enter a prompt, and how staff vs customer roles behave. Secrets stay in environment vaults. Sensitive topics escalate instead of guessing.
Answers to the buyer questions we hear most before a project starts.
Map workflows, systems, compliance constraints, and the metric that would prove AI is working.
Design the smallest production slice — integrations, guardrails, and a staging pilot on real records.
Ship with escalation paths, monitoring, and regression on the workflows that matter.
Production deploy, team training, and a 30-day optimization window against the baseline KPI.
Tell us the vertical, the workflow that hurts, and the systems involved. We will say whether AI is the right lever.
From chatbots and voice agents to workflow automation — pick the capability that matches your roadmap.
Use our free calculators and planners — then book a strategy call when you are ready for a senior engineer review.
Case studies, guides, and free tools from the same engineering team.
Vertical workflows first — then models, integrations, and QA. Not a generic chatbot dropped on a homepage.
Interview operators, inventory systems of record, and define data boundaries for the vertical.
Score opportunities by ROI, integration risk, and whether AI is actually the right lever.
Staging build with PII filters, human fallback, and realistic records — not demo copy.
Deploy with logging, cost caps, and a KPI dashboard the buyer can defend internally.
Retainer for prompt/integration drift, or runbooks so your team can own the system.
Tangible artifacts your engineering and product teams can act on — not vague pass/fail notes.
Transparent ranges based on app complexity, platform count, and engagement depth. Final quotes follow a scoping call.
$5,500 – $14,000
Vertical discovery, prompt design, and a narrow production pilot.
$14,000 – $45,000
Full voice, chatbot, or automation rollout with guardrails and monitoring.
$4,500 – $12,000/mo
Ongoing tuning, model updates, and incident response for live AI systems.
Prices in USD. Retainers and multi-platform engagements quoted after scope review. QA as a Service available for ongoing coverage.
Capability pages, outcome pages, and published industry clusters — same commercial architecture.
Scope, timeline, and cost range — no sales deck. Or start with the free readiness quiz if you are still evaluating your stack.
You'll talk to Abuzer — the senior engineer who'll scope your build
Founder & Senior Engineer · Response within 4 business hours
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