Billing, trials, model tiers, tips, and changelog for the GeonAI workbench. Browse posts on the left; use the sidebar for every article, categories, and tags.
A retail-chain Agent answers store policy, promos, and member questions from one published pack. Expired promos and unread point balances stay unspoken. Price changes, point adjustments, and above-threshold returns need a store manager.
Read full articleSplit private-message leads, floor-plan answers, and showroom booking. Published prices and labels on the floor plan can be quoted. Locking a unit, taking a deposit, or promising appreciation needs a sales owner.
Split enrollment, course Q&A, and academic affairs into three lanes. Published schedules and price lists can auto-answer. Grade changes, certificates, and refunds need a staff owner. Do not invent tuition, pass rates, or job-placement rates.
An internal office Agent is not “hire an HRBP.” Split leave, access, and expense steps into auto, draft-for-approval, and must-have-a-human. Policy Q&A can auto-run; postings and prod access cannot.
After a lead exists, WhatsApp and email must share one follow-up clock and one suppression list. Cold-email blast rules do not paste into chat; first touches to a new number and any price need human confirm.
WeCom and Feishu carry identity; they are not anonymous web widgets. “Will not talk nonsense” means knowledge sliced by identity, versioned talk tracks, and separate 1:1 vs group handoff—not a chattier model.
The 10 selection questions decide whether to proceed. The eight-item brief decides whether the vendor has to guess. Send Owner, boundary tables, sample threads, knowledge, APIs, and pilot gates as attachments—not a prompt.
B2B GEO means writing public pages as extractable, checkable units—not shouting “AI optimized.” Editors can inspect definitions, tables, FAQs, and JSON-LD. No model is guaranteed to name you.
"Digital employee" means handing some job duties to software—not a SKU. Split the work: RPA for stable clicks and APIs, generic assistants for drafts, custom Agents for messy questions plus bounded actions.
Building means staffing a platform engineering team. Buying means purchasing scenario delivery you can accept. Neither is a generic SaaS seat. Decide with headcount, first-scene timeline, and risk—not a “unified Agent platform” first.
Peak season is not a hotter model. At 2×/3× baseline inquiries, keep SLA and wording with routing queues, approved template first replies, and written handoff thresholds—fewer auto-promises beat a temporary auto-quote switch.
Enterprise AI compliance is not “private deploy and done.” Classify data first, then lock audit-log fields and internal access controls—use this checklist in SOWs and security reviews.
Most SMEs should adopt SaaS for acquisition and basic follow-up first, then customize when permissions, integrations, or compliance block you—avoid buying a high-ticket custom Agent before you have flow and data.
Status Agents are read-only: tracking and ERP status codes, KB explanations of those codes. On tool miss or missing IDs, refuse and hand to ops—never invent ETAs or “it should arrive soon.”
A pure translation layer covers languages fast but slips on terms, units, and register. For B2B high-risk replies, prefer localized playbooks plus a glossary; use MT for long-tail drafts with stricter handoff.
Without CRM constraints, trade Agents re-outreach the same buyer and drop hot leads. The Agent drafts and collects slots; the CRM owns uniqueness, stages, owners, and next-action tasks.
Bad quotes usually come from model invention—not weak copy. Structure price, MOQ, lead time, and Incoterms; refuse when missing; humans issue formal quotes.
Multiple entry points are fine—price, MOQ, and terms need one source of truth. Share a knowledge core and adapt copy/fields per channel; do not maintain two fighting price books.
The biggest outreach-Agent risk is not weak marketing copy—it is complaints, spam-folder placement, and domain reputation damage. You need identity disclosure, opt-out paths, caps, suppression, and sampling—not unattended blasts.
In the inquiry path, automate welcome and FAQ; stop for firm price, lead-time promises, and sample-fee confirmation. Use a stage table so “fully automatic” does not mean daily firefighting.
A foreign-trade AI Agent is a role pack for outreach, inquiry follow-up, or multilingual support—each with KB boundaries, tool permissions, and human confirms—not a translating chatbot. It can sit beside SaaS trade tools.
Unowned knowledge after launch often hurts more than picking the wrong model. Pin who edits, who approves, how you release, how you roll back, and how you sample—and keep Prompt/tool changes off the KB hot path.
Custom does not mean private. Classify data, pilot API or private cloud, and move inference on-prem only when residency is written and ops can keep up.
Without a clear owner, knowledge rots, incidents orphan, and nobody signs the go-live gate. Pin a RACI across business, IT, compliance, and vendor before you scale.
ChatGPT gets you a strong model safely; a custom Agent completes a business task under ACL. Fill five layers between “can chat” and “can get work done.”
Inference fees are visible; overruns hide in data cleaning, ACL design, joint integration, and gray-release ops. Line-item them in the SOW before you start.
No-code builders are great for dialogue pilots and light integrations. Custom work wins on ACL depth, write governance, audit, and owned ops—especially for external commitments.
Chasing auto-resolve alone in the first 30 days teaches the system to over-answer and under-handoff. Weight harm metrics first, then scale. See the 12-item POC checklist.
Prompt-only “custom” work fails on stale facts, no actions, and no handoff. A deliverable Agent needs three legs: knowledge, tools, and escalation.
Unclear boundaries create arguments about price changes and lead-time promises. Attach three tables—do, don’t, and human-confirm—to the contract annex; they beat a vague “smart support” line.
Most enterprise knowledge Agents should default to RAG—not fine-tuning first. Use four tests—change rate, citations, ACL, and cost—to choose a path, then apply hybrid patterns, anti-patterns, and a go/no-go table.
Custom Agent failures often come from vague deliverables—not weak models. Use this artifact checklist from brief to acceptance and paste it into the SOW.
Changing orders or checking stock is not a longer prompt—it is controlled tool calling. This guide separates Function Call from MCP, lists hard rules for ERP/CRM access, compares two implementation paths, and gives a pilot checklist plus anti-patterns.
More Agents is not more intelligence. Use four hard signals—ACL, write grade, cost/latency, and compliance voice—to decide when a single assistant is enough, when to add a router + specialists, and which topologies and anti-patterns to avoid.
A pretty POC is not production-ready. This checklist covers 12 pass/fail metrics—scope freeze, citations, wrong promises, handoff, latency, degrade, ACL, audit, KB sync, and rollback—each with a pass line and evidence so “feels fine” never ships full traffic.
ROI is not a “30% efficiency” slogan. This guide gives one formula, a cost checklist, and fill-in templates with worked examples for support deflection, sales follow-up, and internal knowledge—so funding debates use numbers, not stories.
A finance Agent is not a friendlier chatbot—it encodes speakable boundaries, prospectus versions, and a human-review state machine. This guide gives say/don’t-say lists, mandatory review triggers, audit fields, and pilot metrics compliance and business can sign together.
Plants juggle equipment manuals, QC SOPs, and after-sales tickets. This guide splits three manufacturing Agent scenarios—governed KB, read-only MES/CMMS hooks, and mandatory human handoff—so one chatbot does not “run the factory.”
Coze shines at low-code bots and light workflows; custom enterprise Agents focus on ACL, integrations, audit, and ops. Compare boundaries, upgrade signals, and a migration path that keeps what already works.
Built-in Douyin enterprise smart customer service fits standard FAQs. When you need order reads, multi-channel voice, or governed handoff, evaluate a custom Agent—with a clear boundary map and pilot checklist.
Private deploy is not “download weights = compliance.” Compare API vs private cloud vs on-prem on residency, GPU and ops cost, when it pays off, and hybrid patterns that actually ship.
Use DeepSeek as the governed generation layer and RAG for parsing, retrieval, and access control. A practical build guide covering ingest, hybrid search, ACL, citations, and a 6–10 week pilot.
Website and IM presales must catch after-hours inquiries without hard selling. This playbook covers intent tiers, lead capture cadence, human handoff, and KPIs for a 24/7 consultation Agent.
Which sales-funnel steps deserve an Agent? Map lead scoring, follow-up drafts, product/quote copy, and CRM write-back boundaries—with pilot scope, red lines, and auditable KPIs.
Align scenario, data, integrations, human boundaries, compliance, and ROI before you buy. Avoid “great demo, unusable in production”—with a one-pager for GeonAI discovery.
A smooth vendor demo is not a production Agent. Spot five red flags—fake data, no integrations, no human handoff, unowned KB, demo-only metrics—and the questions to ask on the spot.
Carts stall after questions, add-to-cart without checkout, or clumsy payment nudges. Design multi-turn flows for product Q&A, shipping-insurance/returns copy, and governed checkout reminders—with OMS/catalog boundaries.
Cross-border stores juggle languages, carriers, and country-specific return rules. This guide covers custom Agent channels, governed KB, OMS/logistics APIs, handoff—and boundaries vs marketplace bots and TradeAICP.
Enterprise Agents rarely rely on a single LLM. This guide routes DeepSeek, GPT, Claude, and Qwen across support, RAG, coding, analytics, and high-compliance flows—with cost, fallback, and GeonAI gateway patterns.
DeepSeek attracts teams on cost, Chinese-language performance, and deployment options. This guide covers coding copilots, RAG knowledge bases, multilingual support, and analytics Agents—plus how GeonAI places models behind guardrails.
KB Agents need more than PDF upload—RAG pipelines, permission isolation, citations, and governance. Architecture modules, acceptance checklist, and GeonAI delivery notes.
Production support Agents need governed knowledge, ticket/CRM tools, tiered handoff, and ops—not FAQ bots alone. Architecture modules, acceptance metrics, and GeonAI delivery path.
There is no single list price for bespoke Agents. Six factors—scenario complexity, integrations, deployment, compliance, and ops—plus how GeonAI quotes after email assessment, without fake low prices.
Bought ChatGPT, Copilot, or chatbot SaaS—but teams still copy-paste manually? Five root causes when tools exist but workflows do not, and when to move to bespoke Agent delivery.
Successful Agent programs close the loop across discovery, design, integration, and operations—not just buying seats. Deliverables, metrics, and pitfalls for support, knowledge, and internal assistants.
A custom Agent is a deliverable AI capability tied to your workflows—not a generic chat window. Compare ChatGPT Enterprise, legacy chatbots, and GeonAI bespoke delivery.
Enter pricing from the landing “TOKEN marketplace”; pair it with recharge to understand balance and settlement.
Long-form left column and nav on the right suit deep reading; keep it open beside the workbench.
Sessions and messages are persisted for review; never paste secrets or personal data into chat.
The backend may try several models in order; the UI shows the tier that ultimately succeeded.
Save format notes under My employees; we attach them server-side to the system prompt—same logic as chat center.
It starts after the first successful AI employee chat request—not at signup.
Subscriptions unlock product features and tiers; Tokens reflect real inference cost, usually from input and output usage.
Basic focuses on the free model pool and onboarding; Recommended and Optimal target subscribers and bill by Token.
Filter by role and task type; add high-frequency scenarios first so your list stays manageable.
First visit? This post explains how skills, digital employees, models, and Tokens fit together.