AgentPing for legal and professional services

Stand behind
every AI answer.

Research, contract review and drafting agents now do real fee-earning work, where a confident wrong answer carries real liability. AgentPing scores what your AI produces, keeps an evidence trail of how it performed, and tracks cost per matter, so the firm trusts the tool on something it can measure.

Track your first AI workflow
summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

What professional firms cannot leave to trust.

Accuracy is everything, but reliability and cost per matter matter too. One run record per piece of work carries all three.

Output accuracy

Score every run for real citations, on-scope answers and grounded analysis, so a wrong answer is caught before it reaches a client.

summariser · judge score↓ 4.2 to 3.8
  • cites a source pass
  • answers the question pass
  • stays on policy fail

Explore Verify →

Reliability you can rely on

Know when a research or drafting workflow stalls or fails, so a deadline does not slip on a silent error.

support-triage · schedulelive
support-triage missed its 14:00 run paged on-call · last ok 13:00 · 247 runs clean before

Explore Pulse →

Cost per matter and client

Attribute AI spend to the matter, client and practice area, so it is clear what assistance costs to deliver.

spend · this month↑ on budget
$5,382
spent
$0.094
cost / successful run
content-writer$2,189
research-agent$1,474
support-triage$685
email-classifier$262

Explore Spend →

How do we catch a hallucinated citation or wrong analysis?
AgentPing scores production output against checks and a plain-English rubric: cites a real source, answers the question asked, stays within scope. In professional work a confident wrong answer is the whole risk, and a run that returns success tells you nothing about whether the analysis holds. Quality scoring is what watches the substance.
Can we keep a record of how the AI performed?
Yes. Every run records its output, the score it received and the reasoning behind it, trended over time. That gives the firm an evidence trail of how an AI tool performed on real work, rather than a vendor claim.
Can we track cost per matter or client?
Tag runs by matter, client or practice area and AgentPing attributes spend that way, priced server-side. You see what AI assistance costs to deliver a piece of work, which matters when it is billed back or folded into a fixed fee.
Does any of our document content have to leave our systems?
Only when you choose. You can run on metadata and deterministic checks alone, with no content sent to a model. Evaluations that read content are an explicit per-team switch, off by default for teams you create later, so you control exactly what is shared.

Catch a wrong answer before it reaches a client.

Score one research or drafting workflow, write your standard as a rubric, and keep a record of how the AI performed, within minutes.

Track your first AI workflow

For customer support → For product teams → All features →