Oversight you can stand behind.
Attribution your board respects.
FlowTabulator translates human oversight into precise ESG attribution intelligence across a Zero Trust platform secured for absolute data sovereignty.
Oversight you can stand behind
The ESG Attribution Engine and Shadow Metrics Hub turn what each module tracks, analyzes, and maps into actionable results — keeping a human in the loop.
How Do You Govern a Black Box Without Trusting Another One?
FlowTabulator is an AI Governance platform that does not use AI or LLMs. Every figure is derived from inputs you control: Deterministic, Configurable, Ephemeral Processing.
Figures you can defend
Deterministic, fully reproducible calculations. The same inputs always yield the same , traceable end to end with zero black box inference.
Artifacts that earn trust
The and the turn raw AI usage into clear, traceable artifacts your board and reviewers can stand behind.
Model your reality
Select and configure your own data center factors and tokenizer factor tables. Model your reality, not a vendor's assumption.
Sovereignty you can prove
Download your data as JSON, run a multi signature org data wipe, run an individual data wipe with no effect on your org, and sync your vault across devices. Sovereignty you can prove.
See your Shadow AI risk in seconds
Set your organization profile and active controls, then get your annual cost exposure and monthly incident probability instantly. No sign up, no email. Your inputs never leave your browser.
Shadow AI Risk Estimator
Projected cost of a realized event based on risk exposure from unauthorized AI use — Enterprise Risk Exposure Model.
Organization size
Shadow AI surcharge
Active security controls· 0 of 4 active
Monthly telemetry · dynamic probability (3 Vs)
1. Variety — new AI tool domains accessed (CASB/DNS)
2. Volume — DLP paste/upload block rate MoM (Endpoint DLP)
3. Velocity — % AI traffic from personal/non-corporate accounts (SWG/Firewall)
Annual breach exposure
$—
Monthly risk probability
—
One platform. Human oversight. ESG alignment.
Human in the Loop Advocacy
ESG Attribution Tracking
Shadow Metrics Hub
The same Monday. Two outcomes.
Jane Doe is an AI Governance Lead at a financial services firm of roughly 3,000 employees. Follow her through five oversight moments in one day. On the left, what happens without FlowTabulator; on the right, the record each moment becomes with it. The risk isn't a single bad prompt. It's that the gap between what AI produces and what a human actually oversees grows wider every week, and the cost of closing it is absorbed silently into the organization. Don't wait for a regulator or board to demand facts. Have them ready.
Without FlowTabulator
With FlowTabulator
The workflow rework nobody logged
The lending decisions workflow produces inconsistent outputs on 40 edge cases. Two analysts spend the morning redoing them. Jane asks what prompts drove the inconsistency, what the reruns cost in tokens and cloud. The AI tool bills by seat, the middleware logs only success/fail, and the analysts' hours live in a time-tracking system that doesn't tie back to the workflow.
The workflow rework nobody logged
Jane pulls the prompts behind the affected runs into the Bulk Analyzer for the token, cloud, and energy footprint of the failure itself, then runs the human rework calculator against the two analysts' loaded labor rate and time. The corrected workflow is re-run and logged.
Rework cost in dollars + labor hours + Wh + gCO2e, attributed to the Data team, tagged 'edge case review'. Forecast assumed zero rework; actual now shows the true cost of the gap.
Bulk Analyzer + Human rework calculator · Data team · Labor Intervention $845
The verification that ate a morning
The agentic triage assistant misroutes 200 support tickets to the wrong queue. A QA lead hand audits all 200 to be safe. Jane asks what the intervention cost in labor and what prompts and steps caused the misroute. The agent platform shows the run 'completed'; the prompt and step trace are opaque. The QA lead's three hours aren't attributed to the agent, the model, or the team that owns it.
The verification that ate a morning
Jane runs the agent's steps through Agentic Diagnosis for the prompts and steps that caused the misroute and their token cost, then runs the verification bottleneck calculator against the QA lead's loaded labor rate and the three hours.
Verification cost in dollars + labor hours, attributed to the Support team; the agent step trace that caused it, captured at prompt granularity.
Agentic Diagnosis + Verification bottleneck calculator · Support team · Labor Intervention $425
The tuning update that silently raised the bill
The data team pushes a tuning update to the document summarization model to fix hallucinations. It works — but the new model uses more tokens per call, which means more cloud spend, more electricity, more carbon, every day going forward. Nobody measured the before and after delta. Finance will see a higher cloud line item next month with no explanation.
The tuning update that silently raised the bill
Jane runs prompts that have been reported as not producing the outcomes as they were previously (due to model drift) through the Bulk Analyzer for the before footprint; after the team optimizes the prompts, she runs the optimized version through the attribution engine for the after.
The before and after delta — tokens per call, cloud cost, electricity, carbon — per call and annualized, attributed to the Data team. Finance now knows why next month's cloud line item moves before it moves.
Bulk Analyzer · Data team · $0.05/day
The agentic mishap you can't trace
A follow up booking agent double books a regional team's calendar for an entire week. An ops coordinator burns the afternoon undoing it by hand. The agent's logs say 'executed successfully' 14 times. There's no token accounting, no step-level cost, no record of what the human had to override.
The agentic mishap you can't trace
Jane runs the agent's executed steps through Agentic Diagnosis and finds the loop: 14 'executed successfully' calls, each with a step level token and cloud cost, and the exact step where the booking logic branched wrong. She runs the human rework calculator against the coordinator's loaded labor rate.
Mishap cost in dollars + labor hours + cloud, attributed to the Automation team; the specific step and prompt that caused it, captured.
Agentic Diagnosis + Human rework calculator · Automation team · Labor Intervention $220
The ask you can't answer
The CFO walks over: the board wants the company's AI workload Scope 3 contribution, by category. Jane has a spreadsheet of SaaS license counts and a cloud invoice. Nothing attributable to a team, nothing granular to a prompt or a workflow, nothing that separates forecast from actual or labor from compute. The oversight gap is now a gap between the company and a regulator.
The ask you can't answer
Jane opens the Shadow Metrics Hub rollup: AI TCO, the labor cost of every oversight intervention, the carbon and electricity attributable to each team's AI workload, and the forecast vs. actual deltas — in the units the disclosure wants.
The company's AI workload Scope 3 contribution by category, granular to team and workflow, with forecast vs. actual already separated and labor cleanly distinguished from compute. A record, not a spreadsheet.
Moments rolled up
6:00 PM · The tally
14
Analyst hours
$1,490
Intervention cost
342,000
Tokens
1,200
Wh
22
gCO2e
Five oversight moments. Five human interventions — now five attributed records. Everything attributed to a team, everything forecast vs. actual, and the cost of closing the gap visible, defensible, and ready. The regulator who shows up finds facts, not a pile of task tickets and communication threads.
Token cost
$0.86
TOKEN COST
9:10 + 10:45 + 2:00, derived from token counts
$0.05
Tuning delta (12:20)
Before vs after token cost increase
+$16.43
Annualized delta
12:20 tuning delta, annualized
$0.91
Total token cost
342,000 tokens across all five moments
The deliverables Jane handed over
The 4:30 PM board ask, answered. These are the exact sample reports the scenario produces — built from the five moments above, attributed to the teams that own them.
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