The agentic
data trust layer
for finance.
A signed layer of provenance, lineage, and source reliability — the data trust layer on which autonomous agents operate with full traceability.
Built for finance teams who need every claim grounded, every action audit-defensible, and a compliance officer who can sign off on the answer.
One operator terminal,
every signal traceable to its source.
The morning inbox shows what reconciled cleanly, what didn't, which sources are degrading, and which agent proposals are waiting on you — all linked to signed lineage rows.
Three layers of trust,
every claim grounded in the one below.
An LLM acting on unverified data is a regulatory liability. We stack autonomous agents on top of a signed trust layer — every answer points to evidence a compliance officer can sign off on.
Agentic copilots
Autonomous, operator-guarded. Every action grounded in a signed lineage chain.
Knowledge & reasoning
Per-column profiles, semantic index, lineage trace API, per-org credentials, MCP tools.
Trust layer
Lineage + golden values, hash chain, source reliability, deterministic DQ engines.
LLMs propose. Deterministic engines enforce. Every proposal lands as a reviewable artifact — never a direct mutation.
Agents that propose,
operators who decide.
Four copilots, each thin layered over an existing surface. None of them bypass the operator.
Anomaly explainer
Reads the field's last N observations, the column's profile, and similar incidents. Drafts a 2–3 sentence operator-language explanation with a lineage citation on every numeric claim.
Yahoo reported qty=12,500 for AAPL — 8.3× the column's typical value. Alpaca and IBKR both reported 1,500. Likely a Yahoo decimal-place error.
Break investigator
Walks the lineage chain backward. Cross-references the source-reliability tier and recent uptime history. Outputs a signed evidence packet auditors can replay.
Alpaca file arrived 2h late on 2026-05-20; the recon ran against stale cached bytes. Packet signed with the chain hash.
Corp actions copilot
Ingests CA notifications. Drafts adjustments per account. Surfaces conflicts (divergent record dates, missing positions, tax-lot differences) as blocking approvals.
AAPL 4-for-1 split queued: IBKR 1,200 → 4,800; Alpaca 800 → 3,200. Conflict: divergent record dates between sources.
Northpoint Q
Natural-language question bar over catalog + lineage + incidents + findings + runbook history. Every numeric answer cites signed provenance.
Q: Which datasets had the most finding-volume this week? A: prices.alpaca (38), prices.yahoo (31), recon.daily (24).
The pieces that catch bad data
before it reaches a decision.
Cross-source reconciliation, source-reliability tiers, the operator inbox, the signed audit trail — the load-bearing primitives the AI roadmap sits on top of.
One trust layer for the whole workflow.
Broker vs internal, market vs mark, expected vs arrived — reconciled across every source, with an audit trail that proves what fired, who saw it, and how it was resolved.
An inbox that only fires on state change.
Mismatches, freshness breaches, and source-health regressions roll up into one feed. Notified once when something starts failing and once when it recovers — not on every tick.
Every source, scored for reliability.
Brokers, market data, on-chain inputs, spreadsheets, internal files — connected once and tracked for time-weighted uptime, so you know which feeds you can trust before you act.
Every event, traceable.
Reconciliation runs, incident acks, resolutions, ack-by-whom and ack-when, root cause, downstream notifications — logged, timestamped, and exportable for review.
Achieve operational excellence.
The day after handoff. And the year after.
Reconciled inputs, accountable controls, and checks that run on schedule — the way data trust compounds.
Reconciled inputs
Brokers, market feeds, spreadsheets, webhook streams, internal files — cross-checked against each other so what reaches your desk has already been agreed by every source that touches it.
Controls where they matter
Role-based access, audit trail, ack/resolve state, root-cause notes — the controls that turn alerts into accountable incidents.
Built to compound
Saved checks run on schedule, runbooks codify the response, source-health scores trend over time — so trust gets built, not re-litigated every morning.
From scattered inputs to one trusted source of truth.
Review, ship, hand off, support.
Workflow review, Northpoint deployed against your stack, full documentation, and continued support — so the trust layer outlasts the engagement.
A trusted operating terminal that shows what reconciled, what didn’t, where the source-health risk is building, and what’s still waiting on you.
01
Workflow review
Map current tools, manual checks, alert paths, and reporting requirements.
02
Build and deploy
Connect the data, ship the operating surface, configure alerts, and document the system.
Common questions.
What is Northpoint?
The agentic data trust layer for finance: a signed layer of provenance, lineage, and source reliability — with autonomous copilots that operate on top with full traceability.
Who is this for?
Finance teams whose work is regulated, audited, or compliance-reviewed: trading desks, asset managers, RIAs, family offices, ops desks at funds.
How does Northpoint use AI?
LLMs propose. Deterministic engines enforce. Every AI proposal lands as a reviewable artifact — never a direct mutation.
Is my data sent to an LLM?
Only if you choose. PII is redacted before any LLM send. BYO key is the default. On-device embedding option for high-security customers.
Ready to ship?
A short workflow review, then the shortest path to the trust layer plus your first copilot.