Get in touch →hello@pivotaltech.techPrecision and Trust, Engineered.
The stakes

Almost everyone is
getting AI wrong.

The industry is shipping AI fast and shipping it correctly almost never. The result is a graveyard of stalled pilots, insecure code in production, and — increasingly — real harm to real people. This is the gap Pivotal exists to close.

95%
of enterprise generative-AI pilots deliver no measurable return — despite $30–40B invested.
MIT NANDA · 2025
45%
of AI-generated code ships with exploitable OWASP vulnerabilities — 2.74× the human rate.
Veracode · 2025
65%
of "vibe-coded" production apps had security flaws; hundreds leaked secrets and personal data.
Escape.tech · 2025
90%
error rate alleged for a major insurer's AI that cut off elderly patients' care — yet only 0.2% appealed.
Reported class-action litigation
How we correct it

Every failure above is a discipline we already engineer.

The failure
Pilots stall in a "learning gap" — tools that dazzle in a demo but never integrate with real workflows or data.
Pivotal
We build integrated platforms on a unified context and real data pipelines — production systems, not demos. Internal builds fail roughly three times as often as disciplined ones; we build the disciplined way.
The failure
Vibe-coded software reaches production carrying vulnerabilities, exposed secrets, and unprotected personal data.
Pivotal
DevSecOps, policy-as-code, and automated security testing gate every release. Encryption, least-privilege access, and immutable audit logging are defaults — security is a wall, not a warning.
The failure
Black-box models make consequential decisions with no audit trail, no explanation, and no accountability.
Pivotal
What we build is provable, auditable, explainable, and accountable by construction — consent-gated, logged end to end, with human judgment kept in the loop where it matters most.
The failure
Governance and compliance are bolted on at the end, if at all — so the system can't survive real-world scrutiny.
Pivotal
Governance, security, and compliance are architectural primitives from the first commit — the same rigor a HIPAA-grade health platform demands, applied to everything we build.
Why the stakes are different now

Cyber risk is corporate. AI risk is personal.

A breach is measured in dollars, downtime, and reputation — painful, but largely institutional and insurable. AI done wrong lands somewhere else entirely: on a single human life.

Cybersecurity done wrong

A corporate blast radius

Stolen records, regulatory fines, operational downtime, brand damage. Serious and costly — but the harm is absorbed by an institution and spread across insurance, legal, and time. Companies recover.

AI done wrong

A personal blast radius

An algorithm discharges a grandmother from the care she needs. A model denies a mortgage or a job to someone who qualified. A hallucinated dose reaches a patient. The damage doesn't land on a balance sheet — it lands on a person, in their own life, and often can't be undone.

This is why we build the only way we do. When AI touches health, money, or a person's future, "move fast and break things" breaks people. Precision and trust aren't features — they're the entire point.

Already shipped something?

Our Assure vector exists for exactly this. We find what's broken, quantify the risk, and remediate it — before your users or a regulator do it for you.