Reduce unsupported claims
Find where generated language needs source evidence before it reaches customers, reports, or sales material.
AI workflow boundary review
Find where AI needs a human stop.
unFragged reviews one AI workflow and shows where evidence, approval, or controls are needed before generated output affects customers, records, tools, or business decisions.
First step: send a short workflow summary. I reply with fit, scope, and what is safe to share next before any deeper material is reviewed.
Research, technical-review, or partnership inquiry: send the exact workflow or proof surface you want reviewed and the decision you need to make from it.
Business value
The practical result is a clearer stop/go boundary for AI-assisted work.
A review helps a team see where AI output can become risky business state and what needs to happen before that output is trusted.
Limit unauthorized actions
Identify where tool calls, record updates, or workflow actions need authority and human approval.
Clarify ownership
Show who should approve, revise, block, or hold AI output before it moves downstream.
Who this is for
Teams adopting AI before every approval, evidence, and tool-action boundary is obvious.
Best fit: founders, product owners, AI engineers, operations leads, and governance-minded teams using LLM, RAG, or agentic workflows.
The review is useful when AI output can influence a customer-facing claim, internal approval, report, CRM update, file action, ticket action, or other downstream decision.
The goal is practical: map the risky handoffs, identify missing evidence or authority checks, and show where human approval or stronger controls should sit.
Concrete example
A support AI drafts an answer and can update a customer record.
A Boundary Review asks where the generated answer came from, which source supports each claim, whether the CRM update is authorized, what a human must approve, and what receipt proves the action was allowed or stopped.
Find the handoff
Where generated output leaves the model and enters a trusted workflow.
Check support
What evidence, authority, policy, or review is required before it moves.
Record the stop/go rule
What gets allowed, revised, blocked, or held for human approval.
Sample output
A review packet turns a vague AI risk into concrete stop/go questions.
This is a synthetic example, not customer data. A real packet is scoped to one workflow and keeps sensitive material out of the first conversation.
Synthetic workflow
Support AI drafts a customer answer and can update a CRM field
Handoff
Review question
Likely boundary
AI drafts customer-facing language
Which source supports the answer?
Hold until cited source is attached
AI proposes a CRM update
Who has authority to change the record?
Require human approval before write access
AI labels an issue resolved
What proves the customer problem is closed?
Block if evidence is missing or stale
What you receive
A scoped review packet for one AI workflow.
The review is designed to be useful before a full governance program exists. It gives the team a concrete map of what should be allowed, revised, blocked, or reviewed by a human.
Workflow boundary map
Where generated output leaves the model and enters a customer-facing, operational, or system-of-record path.
Evidence and authority gaps
Which claims, decisions, or actions need better source support, authorization, policy, or reviewer ownership.
Human-review points
Where a person should approve, revise, block, or hold output before the workflow proceeds.
Control recommendations
Practical next controls to consider, such as stricter inputs, tool-call limits, receipts, replay checks, or claim boundaries.
Engagement basics
Start narrow: one workflow, one review packet, one next decision.
The first conversation is a fit check. Scope, timeline, and pricing are confirmed before any deeper review begins.
Typical first scope
One AI workflow, one downstream path, and the claims, records, tools, or decisions it can affect.
What you send first
A short workflow summary: what the AI produces, who uses it, and what systems or records it can touch.
What you receive
A written boundary-review packet with risky handoffs, evidence gaps, human-review points, and control recommendations.
What happens next
I confirm fit and define the narrow review path before sensitive data, deeper materials, or paid work are discussed.
How it works
AI can propose. The boundary decides what needs evidence, approval, or a stop.
DVS is the underlying deterministic verification substrate. A Boundary Review applies that thinking to one scoped workflow and maps the points where generated content, outside input, tool calls, authority checks, replay checks, and review evidence should be separated or stopped.
The output is a practical review packet: risky handoffs, evidence gaps, unsupported claims, recommended human-review points, and next engineering controls to consider.
It is not a legal opinion, compliance certification, penetration test, or deployment approval.
Founder-led review
Built by the founder of DVS and kept inside a strict claim boundary.
unFragged is led by Luis Alberto Montoya, the founder building DVS as a patent-pending deterministic verification substrate for AI workflow boundaries.
Before building DVS, he spent over a decade in service, logistics, and dispatch operations across 2013-2016 and 2017-2023, where unsupported records, assumed authority, and bad handoffs could turn into downstream operational reality. That operating exposure is paired with an AS in Warehousing and Logistics and an AS in Business Administration from Barstow Community College.
The current posture is intentionally bounded: local-alpha / prepilot, private technical review only, and no claim of production readiness, external validation, customer validation, or certification.
That boundary is part of the service philosophy: a verification company should not ask buyers to trust claims that outrun the evidence.
What is reviewable now
The current evidence is local-alpha / prepilot and bounded.
The proof stack supports private technical review of deterministic boundary behavior. It does not claim production readiness, external validation, certification, customer validation, durable production ledger status, or full MCP wrapping.
Private technical review of current proof materials is available on request. The public site does not publish internal mechanics, customer data, or a claim that an unrestricted public proof repository is ready for review.
Boundary gate model
Checks separate source, authority, policy, review, replay, and receipt evidence before downstream movement.
Local refusal behavior
Unsupported local tool paths are treated as unsupported instead of being silently accepted as safe behavior.
Strict local inputs
Malformed envelopes, unsafe scalar values, and unsupported request shapes are kept outside trusted state.
Clean-clone gate
Fresh-checkout reproducibility work keeps review evidence tied to exact GitHub-visible bytes.
Claim boundary discipline
Public copy is checked against the claim ceiling before publication or review decisions.
Review receipts
Review packets, hashes, and receipts preserve what was checked and what was not authorized.
Current limits
Honest boundary: useful for review, not a production certification.
- Local-alpha / prepilot only.
- No claim of production readiness, external validation, or customer validation.
- No claim of security, compliance, or legal certification.
- No claim of durable production ledger status, full MCP wrapping, or public-internet connector coverage.
- No claim that a public service is live, available, deployed, or collecting intake.
- No guarantee language about AI safety, hallucination prevention, or failure prevention.