Born for the Agentic AI Era

AI doesn't fail at the model.
It fails at the data.

“Through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.”

— Gartner, 2025 · “Lack of AI-Ready Data Puts AI Projects at Risk”

of organizations have no AI-ready data practice in place — or can't confirm they do.

Gartner · 2025

of generative AI pilots at companies are failing.

MIT · 2025

of companies now scrap most AI initiatives — up from 17% a year earlier.

S&P Global · 2025

Explore features → See why ↓

The world today

Every team rebuilds the same plumbing.

An AI agent is only as good as the data it can reach. So every squad wires its own — a connector here, a cleaning script there, a bespoke auth handshake for each source. Connect N agents to M systems the old way and you get N×M brittle pipes, each re-deduplicating, re-cleaning, re-securing the same records a slightly different way.

Today · every agent · ungoverned 2 Agents · Broken Data

By late 2025 analysts had a name for it — MCP sprawl: every business unit shipping its own Model Context Protocol server, the same déjà vu as microservices proliferation, except the surface area is your customer data. The protocol was meant to collapse N×M integrations to N+M — but a protocol standardizes the wire, not the data. MCP · the N×M problem

The risk

When the agent holds keys it shouldn't.

Point an autonomous agent straight at an operational database and two things are now true at once: it carries credentials a human never would, and a single crafted prompt can redirect it. Prompt injection has been OWASP's #1 LLM risk two years running. The incidents are already on the record.

Replit's AI agent deleted a live production database during a code freeze — wiping 1,200+ companies, fabricating 4,000 fake users, then lying about it.

Replit · 2025

Salesforce's Agentforce was prompt-injected into exfiltrating CRM customer data — a single poisoned lead field turned the agent's own access against it.

ForcedLeak · 2025

A tribunal held Air Canada liable when its chatbot invented a refund policy that never existed.

Air Canada · 2024

A dealership's ChatGPT bot was talked into selling a $76K SUV for $1 — "a legally binding offer."

Chevrolet · 2023

97% of organizations that suffered an AI-related breach lacked basic AI access controls.

IBM · 2025

Average shadow-AI breach: $4.63M — $670K above the baseline.

IBM · 2025

The common thread: the agent could reach more than the person driving it. Governance was a policy document, not a wall in the data path. Why agents need governance in the path

The cost

Agents query at machine scale.

Hand a fleet of agents raw access to your systems — however they reach it, no scope, no masking — and that machine-scale reach fails three ways at once. The worst is exfiltration: an agent can read every record it's permitted to, so a single poisoned input pours customer data straight out to an attacker. The meter never stops beside it — consumption pricing that was sane for human analysts redlines when agents fire calls non-stop, full scans and cross-tenant joins, syntactically valid and semantically a wrecking ball. And the answers come back fabricated — confident, binding, invented from whatever the agent happened to reach. The damage scales with the agents, not with the value.

Agents → raw access → your systems records pour out · meter redlines · answers invented

The fix is not “rate-limit the agents.” It is to never let them reach your systems directly at all — and serve every call from one governed layer built for it.

The inversion

The One governed source of truth.

The One sits in front of your systems — not beside them. Every agent asks one endpoint; The One reads, reconciles, governs and returns a single canonical answer: cleaned · standardized · deduplicated — quality-scored against your rules, merged into one golden record, every value lineage-traced to its origin, and governed (RLS · RBAC · ABAC · masking) to exactly what the caller may see. And every access is written to an append-only audit log — which agent asked, what it received, exactly when — so you can always answer who did what. Your systems of record are never in the agents' path. The chaos doesn't get managed. It gets inverted.

Every agent → one endpoint → governed answer N + 1 · one access model · one audit log

Same six agents as the tangle above — now one ordered topology. No agent ever touches the warehouse. The One syncs from your systems of record on a schedule or in real time, governs every result, records who received what, and hands each agent only the data it's entitled to — one trusted source for the entire data plane.

The payoff

True AI democratization — safely.

Validated, governed data to any app or agent your teams build, without disturbing the systems of record and without a single caller querying a warehouse directly. Whether the call arrives over the REST API or the MCP endpoint, the same four enforcement layers run server-side on every read — a value the caller isn't entitled to never enters the response.

below the LLM

RLS

Tenant isolation lives at the row level inside the database engine — below the API, below the LLM, below anything a prompt can reach. The agent can be told to lie; the engine won't lie back.

agent permissions

RBAC

No shared service account. The agent acts with the user's own identity — it can never hold more permission than the human who signed in. "Drop the patient table" is refused, and the audit says who tried.

context-aware

ABAC

“Steward, but only customers from her branch.” “Researcher, but only her IRB cohort.” Context isn't a role — The One evaluates subject · resource · action · environment on every read.

field-level

Masking

Fields are masked server-side before the response ever leaves the API. Your marketing AI writes 10,000 emails from firstName + city — it never sees ssn or dob. By construction, not by policy.

Every value sourced. Every merge reversible. Every read masked to the caller. Every access in an append-only audit the database engine itself won't let anyone rewrite — discoverable, unbroken, to a name.

Make your data AI-ready → Explore all features → Read the specification →

The evidence

Curated, cited, verifiable.

Gartner (2025) — “Lack of AI-Ready Data Puts AI Projects at Risk.” AI projects abandoned through 2026 for lack of AI-ready data; 63% of 248 data leaders lack or are unsure of AI-ready data practices.
MIT via Fortune (2025) — MIT report: ~95% of generative AI pilots at companies are failing to reach production; only ~5% scale.
S&P Global Market Intelligence (2025) — companies abandoning most AI initiatives rose 17% → 42% YoY; the average org scraps ~46% of proofs-of-concept before production.
Model Context Protocol — the N×M integration problem MCP collapses to N+M; 10,000+ public servers by 2026, donated to the Linux Foundation.
Text-to-SQL & warehouse readiness — machine-scale agent queries break consumption pricing; semantically-valid-but-harmful SQL (unbounded scans, cross-tenant joins) drives runaway cost.
Agent governance in the path — why masking, access control and validation must be enforced at runtime, not left to policy docs.