Common GenAI UseCase - magicplatforms/ai-workflows GitHub Wiki
Below is a high-level “Industry-Agnostic GenAI Workflow” that shows the common pattern I see across every solution we just discussed (finance, healthcare, retail, media, manufacturing, professional-services, education, government). The exact systems and regulations vary, but the steps—and the hand-offs—are almost identical.
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Stakeholders identify a pain point (e.g., long loan-memo drafts, clinical note fatigue, product-copy bottlenecks).
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Define success metrics (time saved, accuracy, CSAT, regulatory turnaround).
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Select “human-in-the-loop” checkpoints and escalation rules up-front.
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Inventory domain data (policies, logs, EMRs, CAD files, statutes, catalog SKUs, open-data portals).
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Clean, label, & permission that data; apply privacy rules (HIPAA, PCI, FERPA, CJIS, FedRAMP, etc.).
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Embed or index the corpus in a vector store / search layer for retrieval-augmented generation (RAG).
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Choose base LLM (GPT-4, Claude, Granite, open-source) hosted in the required cloud boundary.
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Fine-tune or prompt-engineer with industry terminology & style guides.
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Wrap the model with guardrails (content filters, citation enforcement, policy checks).
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User prompt (via chat, UI button, API call) is passed to an Orchestrator.
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Orchestrator queries the retrieval layer for relevant context snippets.
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LLM receives
[prompt + retrieved context], produces a draft answer, code, image, or document. -
Optional post-processors add citations, restructure into templates, check for PII leakage.
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Target user (loan officer, doctor, copywriter, engineer, caseworker, teacher) reviews the draft in their primary system (CRM, EHR, PLM, LMS, ERP, Office, etc.).
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They edit / approve / reject; edits flow back as feedback signals for continuous improvement.
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If confidence < threshold or policy triggers, escalate to senior reviewer.
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Embed the assistant where the work already happens (Word add-in, Epic sidebar, QuickBooks chat, HMI tablet, City web portal).
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Use SSO/role-based access so outputs only reach authorized users.
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Log every interaction for audit, billing, and model-drift monitoring.
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Track quality KPIs (latency, accuracy, hallucination rate, user correction rate).
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Periodically re-ingest fresh data (new policies, design versions, regulations).
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Schedule model evaluations & retrains when performance or regulations change.
sequenceDiagram
participant User
participant App/UI
participant Orchestrator
participant Retrieval/Vectors
participant LLM
participant Post-Processor
participant Human-Reviewer
User->>App/UI: Prompt / Action
App/UI->>Orchestrator: API call + metadata
Orchestrator->>Retrieval/Vectors: Search relevant context
Retrieval/Vectors-->>Orchestrator: Context snippets
Orchestrator->>LLM: Prompt ⨁ Context
LLM-->>Post-Processor: Draft output
Post-Processor-->>App/UI: Cleaned, cited draft
App/UI-->>Human-Reviewer: Display for review
Human-Reviewer-->>App/UI: Approve / edit / reject
App/UI-->>Orchestrator: Feedback signals
| Common Need | How It Appears in Every Industry |
|---|---|
| Domain-grounded answers | All systems use RAG or fine-tuning on private corpora (bank policies, medical records, SKU data, CAD files, statutes). |
| Human oversight | Compliance (finance, healthcare, gov) or brand quality (media, retail) demands a human validation gate before publish/send. |
| Security & audit | Data never leaves the approved cloud boundary; every prompt / completion is logged for audit/regulators. |
| Seamless UX | Success hinges on embedding the GenAI step inside the user’s native tool—Word, Epic, QuickBooks, Teams, Siemens HMI—so adoption feels frictionless. |
| Feedback loop | Edits, rejections, and usage stats flow back to the AI team to tune prompts, retrain, and improve accuracy—closing the virtuous cycle. |
Bottom line: Whether a doctor, banker, marketer, or city clerk is using it, a modern B2B GenAI SaaS follows the same spine:
1️⃣ secure data ingest → 2️⃣ retrieval-augmented or fine-tuned LLM generation → 3️⃣ human verification → 4️⃣ continuous monitoring and improvement.
Master that pattern once, and you can tailor it to virtually any vertical by swapping in the right domain data, regulatory controls, and UX veneer.
sequenceDiagram
%% === 1 – Define ICP & Build Lists ============================
participant Client
participant Agency
participant DataTool as Data Tools
participant Platform as Engagement Platform
participant Prospect
participant CRM as CRM / Reporting
Client ->> Agency : Share ICP answers & targeting criteria
Agency ->> DataTool : Pull & enrich contacts
DataTool -->> Agency : Hyper-targeted prospect list
Agency ->> Platform : Upload list + segment tags
%% === 2 – Craft Personalized Sequences ======================
Agency ->> Platform : Create multi-touch sequence
Platform->> Prospect : Touch #1 (email / LinkedIn / call)
loop Until reply or sequence ends
Prospect --x Platform : No response
Platform ->> Prospect : Next scheduled touch
end
Prospect ->> Platform : Positive reply
Platform ->> CRM : Log response & meeting booked
%% === 3 – Optimize & Scale ==================================
CRM ->> Agency : Performance data (opens, replies, meetings)
Agency ->> Agency : Analyse best-converting segments
Agency ->> Platform : Refine targeting & messaging
Platform ->> Prospect : Scaled outreach to high-fit segments
%% === 4 – Reporting & Handoff ===============================
Agency ->> Client : Weekly report (results & next steps)
sequenceDiagram
participant User
participant App/UI
participant Orchestrator
participant Retrieval/Vectors
participant LLM
participant Post-Processor
participant Human-Reviewer
User->>App/UI: Prompt / Action
App/UI->>Orchestrator: API call + metadata
Orchestrator->>Retrieval/Vectors: Search relevant context
Retrieval/Vectors-->>Orchestrator: Context snippets
Orchestrator->>LLM: Prompt ⨁ Context
LLM-->>Post-Processor: Draft output
Post-Processor-->>App/UI: Cleaned, cited draft
App/UI-->>Human-Reviewer: Display for review
Human-Reviewer-->>App/UI: Approve / edit / reject
App/UI-->>Orchestrator: Feedback signals
sequenceDiagram
participant Team
participant Platform
participant Channel
participant Prospect
Team ->> Platform: Load leads & set cadence
loop Automated Outreach
Platform ->> Prospect: Touch via Channel
alt Prospect replies
Prospect ->> Platform: Response
Platform ->> Team: Alert & stop automation
else No reply
Platform ->> Platform: Wait until next step
end
end
Team ->> Prospect: 1-to-1 follow-up
Platform ->> Team: Analytics & optimisation
sequenceDiagram
participant Manager
participant HeyReach
participant LinkedIn
participant Lead
Manager ->> HeyReach: Import profile URLs
Manager ->> HeyReach: Start multi-account campaign
HeyReach ->> LinkedIn: Sender A – Connect invite
LinkedIn -->> HeyReach: Invite accepted
HeyReach ->> LinkedIn: Sender A – Welcome message
Lead ->> HeyReach: Reply
HeyReach ->> Manager: Unified Inbox notification
Manager ->> Lead: Respond as Sender A
Note over HeyReach: Parallel sends via Sender B…N
sequenceDiagram
participant SDR
participant Outreach
participant CRM
participant AE
participant Prospect
SDR ->> Outreach: Enroll lead in sequence
Outreach ->> Prospect: Email #1
Outreach ->> SDR: Task – Call
SDR ->> Outreach: Complete call
Outreach ->> Prospect: Email #2 (AI-optimised)
Prospect ->> Outreach: Reply
Outreach ->> CRM: Create opportunity
Outreach ->> AE: Assign follow-up
AE ->> Prospect: Continue engagement
Outreach ->> AE: Deal AI insights
sequenceDiagram
participant SDR
participant Apollo
participant Prospect
SDR ->> Apollo: Search DB & add leads
Apollo ->> Prospect: Email #1
Apollo ->> SDR: Task – Call tomorrow
SDR ->> Apollo: Log call (no answer)
Apollo ->> Prospect: Email #2
Prospect ->> Apollo: Positive reply
Apollo ->> SDR: Notify & create CRM task
sequenceDiagram
participant Rep as Sales Rep
participant Reply as Reply.io
participant Prospect
Rep ->> Reply: Import prospects
Rep ->> Reply: Launch multichannel sequence
Reply ->> Prospect: Email #1
Prospect -->> Reply: (no reply)
Reply ->> Prospect: LinkedIn connect
Prospect -->> Reply: Accepts
Reply ->> Prospect: Email #2
Prospect ->> Reply: Positive reply
Reply ->> Rep: Alert & stop sequence
Rep ->> Prospect: Personal follow-up