Decision support that recommends, not guesses
Our decision-support agents gather the evidence, weigh the options against your criteria, and hand your team a reasoned recommendation — with sources, confidence, and a human still holding the final call.
- Evidence gathered, not invented
- Options scored against your criteria
- Calibrated confidence on every call
- Human approves the final decision
An analyst that does the legwork and shows its work
Decision support is the agent pattern for high-stakes judgment calls you don't want a machine making alone.
Most teams don't want an AI to fire a customer, approve a loan, or pick a vendor on its own. But they do want the hours of gathering, cross-referencing, and option-weighing that precede those calls to disappear. That gap is exactly what a decision-support agent fills.
Give it the question and the criteria. It pulls the relevant evidence from your systems and documents, scores each option against your weighted factors, surfaces the trade-offs, and writes up a recommendation a human can read in two minutes and defend in a review. The decision stays with your team — the legwork doesn't.
Every recommendation, fully assembled
A decision-support agent doesn't return an opinion. It returns a defensible case.
Grounded evidence
Pulls facts from your data, documents, and APIs — every claim cited back to its source so nothing is invented.
Scored options
Weighs each option against your criteria, shows the math, and names the alternatives it ruled out and why.
Calibrated confidence
Reports how sure it is, not just what it thinks. Low-confidence or contradictory cases escalate instead of bluffing.
A clear approval step
The agent recommends; a named person approves, edits, or overrides. The human stays in the loop by design.
Full decision trail
Criteria, weights, evidence, and the final call are logged end to end — auditable months later, not a black box.
Wired to your systems
Reads from your CRM, data warehouse, and policy docs; posts the recommendation where the decision actually happens.
From question to approved decision
A predictable loop you can supervise, audit, and trust.
Frame
The agent reads the question, your decision criteria, and any policy constraints that bound the answer.
Gather
It retrieves evidence across your systems and documents, citing each source it pulls into the case.
Weigh
It scores the options against weighted criteria, flags trade-offs, and assigns a confidence level.
Recommend
It writes up the call with reasoning and routes it to the right human to approve, edit, or override.
The agent reasons. Your team decides.
Decision support is deliberately not autonomous. The agent's job ends at the recommendation; a person makes the call. That boundary is what makes it safe to point at consequential decisions — underwriting, escalations, spend approvals — without handing over the keys.
You set who approves what, and where confidence thresholds force a second look. High-confidence, low-stakes recommendations can move fast; anything ambiguous or expensive lands in front of the right human with the full evidence already laid out.
- Named approvers per decision type
- Confidence thresholds trigger review
- One-click override, with reasons captured
A dashboard vs. a decision-support agent
One shows you numbers. The other hands you a defensible answer.
| A BI dashboard | A decision-support agent | |
|---|---|---|
| Output | Charts and metrics | A reasoned recommendation |
| Legwork | You gather and interpret | Agent gathers, cites, and weighs |
| Options | You compare manually | Scored against your criteria |
| Confidence | Implicit, in your head | Calibrated and stated |
| Final call | You — after hours of work | You — in two minutes |
Frequently asked questions
How is decision support different from a fully autonomous agent?
A decision-support agent stops at the recommendation. It assembles the evidence, scores the options, and explains its reasoning — then a person decides and acts. Autonomous agents take the action themselves; here the human keeps the final call.
Will it just make up a justification to sound confident?
No. Every recommendation is grounded in retrieved evidence with citations, and the agent reports a calibrated confidence score. When the data is thin or contradictory, it says so and routes to a human instead of guessing.
Can we see why it recommended one option over another?
Yes. Each recommendation ships with the criteria, the weights, the evidence behind each factor, and the options it rejected. The full decision trail is logged so you can audit any call months later.
What kinds of decisions does this work well for?
Recurring, evidence-heavy judgment calls — credit and underwriting reviews, vendor selection, triage and prioritization, pricing exceptions, churn-risk interventions. Anywhere a smart analyst weighs inputs against criteria and recommends a course of action.
Stop drowning in the prep work before a decision.
Bring one recurring judgment call. We'll show you the agent that assembles the case and leaves the verdict to you.