SQB™
Scoped logic. Predictable AI. Trusted interaction.
SQB™ is a deterministic framework for deploying AI-assisted and non-AI digital systems with enforceable structure, scoped content boundaries, and consistent behavioral logic. It structures QClarity, DropBots, and other digital systems where uncontrolled AI is not acceptable.
What Makes SQB Different?
- Structured interaction trees
- Tiered AI control (from FAQ-only to constrained generation)
- Optional fallback logic (e.g. Tiered response system when FAQs fail)
- Transparent and inspectable layers, no mystery prompts
Built For:
- Client-facing workflows in legal, medical, and financial fields
- AI compliance environments where source control and audit trails are mandatory
- Embedded enterprise AI-assisted and non-AI digital systems tied to internal knowledge systems
- Multi-language deployments with structured language-layering
Commercial Use
SQB™ is a protected system specification. Please contact SQB™ to license the framework, integrate it into your stack, or commercialize an SQB-powered product:
Contact SQB™
info@sqbdev.com
SQB™ Use Case Examples
SQB™ governs how AI-assisted and non-AI digital systems behave, what sources they use, and how far they’re allowed to go. Below are real-world contrasts that show how SQB-controlled systems differ from open-ended AI bots in sensitive applications.
Legal Intake: SQB vs Non-SQB
| Category | SQB Legal Intake | Non-SQB Legal Chatbot |
|---|---|---|
| Purpose | Structured pre-consultation for a law firm | Answers random legal questions using AI |
| Response Logic | Only asks law firm–approved questions | May give advice it’s not authorized to provide |
| Source of Truth | Law firm documents, templates, and flows | Trained on public or internet data |
| AI Usage | Optional, scoped fallback only | Always active with no hard boundaries |
| Audit Trail | Every step is logged and verifiable | No way to prove where an answer came from |
| Compliance Ready | Aligned with attorney marketing rules | May violate UPL or advertising laws |
| User Trust | Clear disclaimers and consistent flow | Can be mistaken for legal advice |
Example SQB Use:
“Please select the state you’re in.”
“Have you already received a demand letter?”
“Would you like to speak with someone from our team?”
AI assist (if enabled) pulls only from uploaded firm guidance.
Example Non-SQB Use:
User: “Do I have a case if my landlord changed the locks?”
Bot: “Yes, that may be considered an illegal eviction under local laws.”
No structure, disclaimers, or safeguards.
Medical Screening: SQB vs Non-SQB
| Category | SQB Medical Intake | Non-SQB Medical Chatbot |
|---|---|---|
| Purpose | Pre-screening and triage intake only | Answers symptom questions with AI guesses |
| Response Logic | Follows uploaded protocol | Responds freely based on model training |
| Source of Truth | Clinical triage or intake workflows | Unverified or public web sources |
| AI Usage | Only used to clarify or rephrase questions | Primary response engine |
| Audit Trail | Every prompt and branch is logged | No explanation of AI output |
| Compliance Ready | HIPAA-safe triage compliance | Potential medical risk or misguidance |
| User Trust | Stops at red flags and recommends escalation | Feels like diagnosis; user may act on it |
Example SQB Use:
“Are you experiencing chest pain?”
“How long has it lasted?”
“This may be an emergency. Please seek care now.”
No AI reasoning or diagnosis attempted.
Example Non-SQB Use:
User: “I've had chest pain since this morning.”
Bot: “That could be stress or indigestion. Try resting and see if it improves.”
Makes a health assumption without authority.
Lending: SQB vs Non-SQB
| Category | SQB Lending Intake | Non-SQB Lending Chatbot |
|---|---|---|
| Purpose | Collects borrower data for review | Tries to answer loan qualification questions directly |
| Response Logic | Structured intake: credit, income, DTI, etc. | Responds based on guesswork |
| Source of Truth | Lender-approved eligibility logic | Trained general finance knowledge |
| AI Usage | Used only for definition/help text | Makes qualification predictions |
| Audit Trail | Fully logged intake session | No record of assumptions made |
| Compliance Ready | Aligned with lending compliance (TILA/UDAAP) | Risk of misleading or deceptive response |
| User Trust | No promises, clear intake path and disclaimers | Implied approvals or misleading confidence |
Example SQB Use:
“What is your estimated credit score?”
“What’s your monthly income?”
“Thanks. We’ll evaluate this and follow up via secure review.”
Optional human follow-up, no promises made.
Example Non-SQB Use:
User: “Can I get a $500K loan with 720 credit?”
Bot: “Yes, you should qualify just fine with that credit score.”
Unverified assumption, no disclosures.