Business Observability for your Organization

Where is every claim, loan, or order right now?

Your IT team watches every server, every API, every network link. The end-to-end processes your customers actually experience, spanning many systems and many handoffs, go dark between them. Outcome Owl makes them visible: every instance, where it stands, which ones are stuck, and what is at risk.

See Clearly. Act Decisively.

See how it works → Executive Briefing Book

Order 44210 · three steps, two systems

  1. Order Receivedorder system
  2. Order Pickedwarehouse
  3. Order Shippedwarehouse

Flagged: shipped 3 days 2 hours after the order was received. Your rule allows 2 days.

illustrative sample data one row per step

How it works

One row per step. One timeline per order. A flag when a step runs late.

  1. Name the process and its steps. In the administration screens, with the rules each should meet, such as "an order ships within two days of being received." No code and no data model to build.
  2. Send what your systems already record. One row per step: a CSV file exported from the systems you already use is enough to start, and event streams or an API work too.
  3. Outcome Owl links the rows and checks your rules. One timeline per order, even when rows arrive late or out of order; your rules are checked four times a day.
  4. Your team sees what needs attention. Flagged items wait in the web app, and your AI assistant can ask about them.
Diagram. Your systems, such as an ERP, a warehouse system, forms and spreadsheets, send rows to Outcome Owl as CSV files, event streams or through the API. Outcome Owl runs in your own dedicated environment, links each step into its process, checks your rules four times a day and never changes anything in your systems. Your team uses the web app; as options, your AI assistant and your BI tools read a read-only copy.
idnameparentidendcomments
ERP-44210Order ReceivedERP-442102026-09-14 09:00:00[Customer Type:Wholesale]
WMS-44210-PICKOrder PickedERP-442102026-09-14 15:30:00[Warehouse:Reno]
WMS-44210-SHIPOrder ShippedWMS-44210-PICK2026-09-17 11:00:00[Carrier:Ground]

Illustrative sample data: the three rows behind the order above, from two systems, the enterprise resource planning (ERP) system and the warehouse management system (WMS). Each row's parentid names the step before it (the first row names itself).

The full walk-through, and what Outcome Owl does not do →

What it is

A business process observability platform, not a dashboard.

Outcome Owl captures every meaningful event in a process (an order placed, a claim submitted, a loan advancing) as an Observation, links them into end-to-end chains across systems, and detects when something takes too long, goes missing, or breaches a threshold. Unlike process-mining tools, it needs no event log or data model built first. It takes one row per step from any system that can send one, and keeps a row that is wrong visible, with its reason, until it is corrected.

Any source
CSV, Kafka, REST API, or OpenTelemetry, with no agents to install.
Imperfect data
Late, out-of-order and incomplete rows are staged and shown with their reason until fixed; a file that cannot be read is set aside, never silently lost.
Near-real-time
Instances and stalls within minutes of arrival; rules checked four times a day; value-at-risk totals as of the daily refresh.
Single-tenant
Each customer gets its own database, storage and compute; no data layer is shared with another customer.

See it answer a real question

Four lines of business. One capability.

A plain-language question and its follow-ups, answered through a short run of read-only calls, with every figure cited and its limits disclosed. Each briefing was assembled from the Outcome Owl tools in July 2026, against a synthetic demo estate.

Workers' Compensation · open exposure
$871.6M
open reserve · 9,098 active claims

“What is my open exposure right now, and what should I worry about?” answered with a cited figure, a composition read and a drill into the single largest claim. It volunteers the coverage floor, names its method, and reports facts, not judgments.

Read the briefing →
composition + risk read largest-claim drill cited · bounded

The largest reserve was progressing cleanly. Exposure is not the same as risk. The briefing shows both, and never conflates them.

Loan Origination · open pipeline
$3.43B
5,792 applications · 27% overdue · 67% jumbo

The exposure-is-risk counterpoint: a $2.0M mortgage stalled 261 days at Conditional Approval, surfaced from the top of the pipeline.

Read the briefing →
Order Management · open quoted value
$384.5M
current · $410.9M high-water peak

Reporting the current book, not the high-water peak, spares a $26.5M overstatement, while still surfacing the tail risk: a delivered, invoiced order unpaid 220 days.

Read the briefing →
SaaS Retention · ARR at risk
$784.8M
16% past save window · 1,238 rule violations

The proactive angle: a $2.0M-ARR account still inside its save window, with 33 days left.

Read the briefing →
Your line of business

The same capability runs on your own processes and lines of business, once your systems send one row per step.

Bring us one process →
illustrative sample data · synthetic multi-vertical demo estate, not a customer's book · a static snapshot from July 2026

Built for your AI, too

27 typed, read-only tools your agents can trust.

Ask your AI assistant a question in plain language: it asks Outcome Owl for the facts and writes your answer. And every AI agent you deploy (to adjudicate, route, price or escalate) creates another end-to-end process nobody is watching; send its steps in like any other system's, and Outcome Owl watches that process too.

As an option, your live operations are exposed as 27 typed tools over the Model Context Protocol (MCP), reading a read-only copy of your data: the same tools an AI assistant, or your own autonomous agents, call to explore your data in plain language. Two record kinds: processes (a claim, a loan, an order, each with an SLA and a conclusion) and audit trails (an asset, a contract, an employee record, each a timeline with no deadline).

cited

Every figure traces to a call

No number appears without the tool, filter, and population that produced it.

bounded

Cost-governed at scale

An over-broad query is refused, naming the filters it has not set, never a partial total dressed as complete.

clocks

Which clock, which freshness

Every data response carries its as-of, its basis, and a confidence rung.

facts

Facts, never judgments

It reports state and behavior; it never calls a finding “concerning” or tells anyone what to do.

The MCP surface is built and in use today. Every briefing above was produced with it.

Connect your AI to Outcome Owl: the step-by-step guide →

Diagram. 1: you ask your AI assistant a question. 2: the assistant asks Outcome Owl for the facts. 3: Outcome Owl answers with cited facts, read-only and signed in as you. 4: the assistant gives you the answer.
Outcome Owl gives the facts; you and your AI supply the judgment.

How the exposure answer was assembled: the tool trace, abridged

A read-only AI connection and plain-language questions: a few steps to connect, and only scoped, cited answers reach your assistant.

How your data gets in

If a system can export a row per step, or send one, Outcome Owl can watch it.

No event log or data model to build first: one row per step, naming the step before it. No proprietary agents. No vendor-specific protocol.

CSV Upload
Bulk loads, historical backfills, and scheduled extracts, via the UI or cloud storage.
Kafka Streaming
Near-real-time ingestion for organizations with event-driven architectures.
REST API
Application-to-application integration: submit and correct observations as they happen; each is processed within minutes.
OpenTelemetry
Existing OTel traces become business observations. Spans map to Observations.

Graceful by design. Late, out-of-order or incomplete rows are accepted into a visible staging layer and surfaced with a specific explanation; a file that cannot be read at all is set aside with the reason. A record with errors is rechecked automatically for a configurable window after each change (72 hours by default), and a corrected record from the upstream system flows through whenever it arrives; everything downstream then reprocesses. No one has to re-queue a corrected record by hand.

An answer you can act on

In a category full of confident wrong answers, Outcome Owl states its work.

Every figure the AI connection returns names its method, shows its coverage, and carries an as-of clock and a confidence level. It reports what is happening (counts, timing, state) and leaves the judgment, and the action, to you or to your agents. Because an agent may act on the answer, correctness is a processing-integrity commitment, not a nicety. That is what Act Decisively requires: an answer you can trust enough to act on.

Is our data safe? Your data lives in its own database, storage and compute, shared with no other customer, and is encrypted in transit and at rest. Your team signs in with a password or your company's single sign-on, and administrative actions are recorded in an audit trail the application never deletes. Your AI assistant reads a read-only copy, and only the answers it asks for leave. Outcome Owl is engineered against a SOC 2 Type 2 readiness program, but no SOC 2 report has been issued yet.

Never changes your systems
Outcome Owl surfaces insight and never acts on your behalf; its AI connection is read-only.
Single-tenant isolation
Each customer gets its own database, storage and compute, shared with no other customer.
SOC 2 readiness
Engineered against a SOC 2 Type 2 readiness program; no SOC 2 report has been issued yet.

Common questions

Answers, in brief.

What is Outcome Owl?

A business process observability platform. It captures events across every system a process touches (an order placed, a claim submitted, a loan advancing), links them into end-to-end chains, and reports where each stands, which are stalled, and what value is at risk.

How is it different from process mining or a BI dashboard?

Process-mining tools need a clean, structured event log and a data-modeling project before they show anything; a dashboard shows metrics you already defined. Outcome Owl takes one row per step from any system that can send one, keeps imperfect rows visible until they are fixed, derives the end-to-end chain for you, and shows individual live instances, not just aggregate charts.

Couldn't we build this ourselves with AI?

You could build the dashboard; AI coding tools make a first version fast. The work is underneath it, for as long as you use it: steps that arrive out of order, corrected records sent again, rows with errors that must be rechecked rather than dropped, an audit trail, and the day its builder moves on. Outcome Owl runs and secures your dedicated deployment for you. If a tracker for one system is all you need, building it may be the better choice.

How does my data get in?

Start with a CSV file: one row for each step that happened, exported from the systems you already use and uploaded in the browser or dropped into cloud storage. Kafka streaming, a REST API and OpenTelemetry traces work too, with no agents to install. Late, out-of-order or incomplete rows are staged and shown with their reason until they are fixed.

What doesn't Outcome Owl do?

It does not plan: it does not work out what to buy or build from a bill of materials and a schedule. It does not change anything in your systems. It does not send email or chat alerts; flagged items wait in the web app, where your AI assistant can check them too. And it reads one row for each step that happened, not report layouts.

Can it tell my team what to do next?

No. Outcome Owl reports facts: which orders, claims or cases are late, stuck or breaking a rule, and what changed. Deciding what to do next, such as what to buy or build, stays with your people, your planning system, or an AI agent you run.

Does it send alerts?

Not by email or chat. Outcome Owl checks your rules four times a day and flags what breaks them in the web app, where your team sees them. Your AI assistant can check the same flags, and an agent you run can turn them into emails or tickets.

Can my AI assistant or autonomous agents use it?

Yes, as an option: your operations are exposed as typed, read-only tools over the Model Context Protocol (MCP), reading a read-only copy of your data. Every figure is cited to the query that produced it, and the surface reports facts, never judgments.

Where does my data live, and is it read-only?

Each customer gets its own database, storage and compute in AWS, shared with no other customer. Outcome Owl never changes anything in your systems: it surfaces insight, never acts on your behalf, and its AI connection is read-only. It is engineered against a SOC 2 Type 2 readiness program but holds no SOC 2 report today.

Outcome Owl — See Clearly. Act Decisively.

Bring us one process.

You do not need to capture your entire organization. Bring one process and one data file; we aim to have it running in days or weeks, not months. Plan on four to eight hours of an operations lead's time and four to sixteen hours of an analyst's, spread over the first two weeks.

Start a conversation →