The pre-flight check for agentic AI

Stop bad AI projects before
agents recommend them.

The scoring tool your Claude agent calls before it recommends an AI deployment. It checks the business case, operating-model readiness, change enablement and governance exposure, then returns Accelerate, Fix, or Stop with a modelled EUR value.

npx aibvf-mcpRead the protocol
DeterministicOpenOn-premMIT
AccelerateFixStop
What it does

Four pillars, one deterministic call

Every initiative is scored 0–100 on four pillars, honest self-assessment. Rules are deterministic, no network, no dependencies.

01
Strategic Alignment
How clearly this moves a board-level KPI.
02
Financial Return
Strength of the modelled return.
03
Change Enablement
Sponsor in place, owner named, change budget funded.
04
Governance Risk
Regulatory and reputational exposure. Higher is worse.
GR ≥ 70 or FR ≤ 20 returns Stop. All four pillars at or above 60 with GR ≤ 40 returns Accelerate. Everything else is Fix, with a specific gap list.
The deterministic rule set
MCP Server · 12 tools

Callable from any agent

score_initiative
Four-pillar score → Accelerate / Fix / Stop with EUR value range, decision confidence, applied modules, reasoning.
score_portfolio
Scores every initiative in one call. Returns the board-level shape: verdict counts, aggregate EUR, mean confidence.
assemble_portfolio
Builds the portfolio document from loose inputs: aliases resolved, ids generated, missing pillars estimated with provenance, validated before return.
recommend_improvements
For Stop or Fix, the specific pillar raises that would flip the call toward Accelerate.
calculate_pace_layer_drag
Annual Organisational Drag Cost in EUR from AI-tier vs operating-model misalignment.
validate_portfolio
Validates a portfolio JSON document against the BVF v1.0 schema.
get_benchmark
Published benchmark rates for a business function and industry.
list_taxonomy
Valid values for industries, functions, AI tiers, readiness levels.
diagnose_process
Advisor Brain: diagnoses one process from observed signals → verdict + net EUR saving.
sequence_portfolio
Three-wave rollout plan with named gates: Stops first, quick wins second, complex work third, with change capacity enforced per function.
map_to_taxonomy
Everyday business language mapped onto the canonical enums, so callers never guess.
infer_readiness
Measures organisational readiness from process signals (hand-offs, rework, wait time, automation, cycle time) instead of accepting self-report. When the measured answer is lower than the claimed one, the gap is itself a change-readiness finding.
CI/CD pre-flight gate · aibvf-check

SonarQube for AI

Wire the gate into CI. Initiatives that can't survive a board review fail the build, before the budget is committed.

ci · pre-flight gate
$ aibvf-check ./portfolio.bvf.json
Quality gate
min-verdict=Fix · max-governance-risk=60
FAIL
✓CX copilot rolloutAccelerate72
✓HR onboarding assistantAccelerate66
✓Claims triage automationFix54
✕Autonomous procurement agentStop38
✕Contact-centre voice botStop44
What you don't gate, you don't govern.exit 1 · 2 initiatives blocked
30-second install

Run it directly

+claude.ai · Settings · Connectors · Add custom connector · https://mcp.aibvf.com/api/mcp
$npx -y aibvf-mcp
$npm install -g aibvf-mcp
{ "mcpServers": { "aibvf": { "command": "aibvf-mcp" } } }

No install needed on claude.ai (web and mobile) via the hosted connector, or register the npm package with Claude Desktop, Claude Code, or any MCP client. Deterministic · open · MIT.