What changes
A clearly scoped improvement
A risk map, acceptance suite, routed cost boundary, failure replays and documented release conditions.
SaaS & AI Products
Shipping AI is easy. Trusting it in production is harder.
Make releases safer, protect customer data and keep AI costs visible. Test failures before customers find them, with a named person responsible when the system needs help.
Common examples include AI-built SaaS, internal AI tools, agent-enabled applications and fast-shipped software products. Fit depends on the workflow; this is not a claim of expertise in every listed profession.
BUILDVERIFYRELEASE
What happens outside the demo path?
Denied accessIdentity + record ownership
Retried eventSignature + deduplication
Provider failureTimeout + bounded recovery
Broken releaseDetection + rollback
A scoped audit and regression record; no blanket security certification.
SaaS founders / Product teams / AI engineering
The engagement
What changes. What you receive. How we check it.
What to measure
Compare before and after
Track accepted results, blocked failures, review load and routed cost per accepted task.
How we start
One owner, one workflow
Bring a representative example. Together we confirm scope, access, acceptance criteria and pricing before implementation.
ExploreCurrent vs controlled / Reference workflow
The same work. A clearer route through it.
StageCurrent / frictionControlled / accountable
01User
CurrentAn ambiguous request enters the app
ControlledValidate identity and request scope
02Product
CurrentA demo path is treated as complete
ControlledDefine accepted and failed states
03AI
CurrentFluent output is trusted
ControlledRequire task evidence and uncertainty
04Tools
CurrentBroad actions are exposed
ControlledEnforce allowed tool actions
05Data
CurrentSource and tenant boundaries blur
ControlledPreserve tenant scope and provenance
06Evaluation
CurrentOnly passing examples are shown
ControlledReplay representative and adversarial failures
07Cost
CurrentRetries multiply unnoticed
ControlledBound routed calls and record spend
08Human escalation
CurrentFailures arrive without an owner
ControlledEscalate with context and a named owner
People approve verifier changes, risk acceptance and release. A passing check is bounded evidence.
Interactive reference / No external action
Test the boundary before trusting the action.
Reference verdict
HUMAN REVIEW
The checks pass in this fictional example. The consequential action still needs approval.
Automation / AI / Your team
Give each kind of work the right owner.
Automation
Moves and checks
Enforce routed limits, validate artifacts and replay deterministic checks.
AI assistance
Interprets and prepares
Perform the bounded task and explain results with traceable evidence.
Human accountability
Approves and decides
People approve verifier changes, risk acceptance and release. A passing check is bounded evidence.
Modelled impact / Calculator
What does the retry loop add to the bill?
Illustrative assumptions / editable
Rates are your own assumptions. A gateway limit covers only calls routed through it. Quality and human review require separate acceptance checks.
Use this scenario in my assessment ↗Formula and sensitivity
Base monthly cost = daily runs × calls per run × cost per call × operating days. Total = base × (1 + extra retry calls / 100). This is a linear scenario, not a token or provider billing simulator.
Practical starting points
Start small enough to verify.
Bounded workflow
Routed cost boundary
Inspect user: An ambiguous request enters the app. Agree the owner and acceptance evidence before changing the live process.
ExploreBounded workflow
Release evidence
Inspect product: A demo path is treated as complete. Agree the owner and acceptance evidence before changing the live process.
ExploreBounded workflow
Permission and failure review
Inspect ai: Fluent output is trusted. Agree the owner and acceptance evidence before changing the live process.
ExploreExternal guidance / Separate from our work
Use published risk guidance as a reference.
EXTERNAL INDUSTRY EVIDENCE
NIST AI Risk Management Framework
A voluntary framework for incorporating trustworthiness into AI design, development, use and evaluation. This is external guidance, not certification of a LaunchSoloAI build. Reviewed 2026-09-12.
ExploreEXTERNAL INDUSTRY EVIDENCE
OWASP: Excessive Agency
OWASP describes risk from excessive functionality, permissions and autonomy. The reference informs boundary design; it does not prove any implementation is secure. Reviewed 2026-09-12.
ExploreLaunchSoloAI engineering evidence
Inspect the artifact and its boundary.
PUBLIC PRODUCT
Runcap
Public source and replayable Proof Gate examples demonstrate bounded controls. Routed spend coverage excludes bypassed calls.
Open the evidence record
Implementation / Evidence before expansion
A bounded engagement, with a decision at each stage.
- 01
Map
Confirm the workflow, owner and baseline.
- 02
Bound
Agree data, permissions, scope and unacceptable failures.
- 03
Build
Implement the smallest useful intervention.
- 04
Verify
Track accepted results, blocked failures, review load and routed cost per accepted task.
- 05
Hand over
Train the owner, document recovery and decide whether to expand.
What you receive
A risk map, acceptance suite, routed cost boundary, failure replays and documented release conditions.
A safer next release
Start with the failure your customers cannot afford.
Bring one release risk, unreliable AI action or recurring incident. We will identify a testable next step, with clear human ownership.
Review my product workflow