Every system of record assumes its data is true. MGE tests whether the evidence agrees.
MGE (Metrics Governance Engine) runs every result through an 8-stage certification pipeline, the Trust Loop, scores its evidence across 11 Trust Gates and seals what it certifies in an append-only KPI Vault. No AI or machine learning touches certification: the same evidence, rules, versions and configuration always produce the same certified result. When the evidence falls short, MGE refuses to certify.
Seven systems. Seven versions of the truth.
POS, payroll, inventory, delivery aggregators, payment processors, supplier systems and accounting platforms each validate their own data. None of them independently verifies the others before their numbers become the official record. Errors that slip through don't get caught downstream. They get amplified.
The Trust Loop: eight stages, no AI anywhere.
Every certification runs the same eight stages. Run the same evidence through the same rules, versions and configuration and you get the same certified result and the same SHA-256 fingerprint; FohBoh's own quality checks certify every test twice and require the hashes to match.
Evidence is checked and mapped to canonical records.
Every rule, formula and scoring version is pinned for the run.
Governed metrics are computed, money in whole cents.
Sources are reconciled against each other: sales to deposits, statements to agreements, invoices to receiving.
The module's executable rules run; findings are proven or held.
The Trust Score is calculated across 11 Trust Gates.
The outcome is set: CERTIFIED 85–100, VERIFIED 70–84, REVIEWED 60–69, FAILED 0–59. Missing essentials force REVIEWED; they never deduct.
The result, narrative and evidence are hashed with SHA-256, signed and stored write-once, so any later change is detectable.
Thirteen technical elements make up the core MGE stack. Each application, such as Sentry's card-fee, delivery-fee and supplier-invoice modules, adds its own governed rule packs on top.
What the seal does, and doesn't do. The SHA-256 seal lets anyone verify that a certified record hasn't changed since it was sealed. It doesn't make the underlying data accurate. The evidence tests in stages 4 through 7 are what support the result, and when they fail, MGE holds or refuses certification instead of sealing a guess.
Confidence in the process, not approval of the result.
The Trust Score measures how completely the evidence supports a result, across all 11 Trust Gates. It doesn't grade whether the number is good, and it isn't a probability that the number is correct. Missing essentials hard-gate the outcome to REVIEWED, whatever the score.
Food cost at 45%. Trust it.
Inventory complete, every invoice accounted for, formula verified across all 11 Trust Gates: CERTIFIED. The evidence fully supports the 45%, so fix the business problem, not data ghosts.
Food cost at 28%. Don't.
Inventory five days stale, labor hours missing. The good-looking number rests on incomplete evidence, so it scores REVIEWED and MGE™ flags it before anyone acts on it.
Five structural advantages. All compound with use.
Deterministic core, KPI Registry and Vault, semantic model, cryptographic evidence chain and embedded layer. A concept drawing of how the parts fit together.
One engine. Five ways to put certified data to work.
Every path runs the same Trust Loop and returns the same certified result. What changes is where MGE sits in your stack and who consumes its output.
License the layer your platform is missing.
Your customers never leave your product; they just start trusting it. Systems of record, POS, BI and AI platforms call MGE through a versioned API and get back sealed, verifiable certified records and Trust Scores.
Headless: no UI, no user-facing footprint. Certification runs at your ingestion edge.
Certification ships as a capability of your platform, under your brand. FohBoh stays behind the curtain.
Embeds via API ahead of your write path. No re-platforming, no data migration, no workflow changes.
A license fee plus a per-certification fee. Partners onboard through a sandbox first. You sell certified data as your differentiator. Request pricing.
Put certified data to work, your way.
License MGE by API, use it as the trust layer for your AI agents, or run it inside our applications. Request a technical walkthrough.