RapidCanvas

Proof, not promises.

Every solution has to clear a bar before it ships, and keep clearing it afterwards. Acceptance criteria are set per task type, answers are traced back to the column they came from, and the rate at which people accept the output is tracked over the life of the deployment.

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A scorecard with acceptance criteria set per task type above it. Groundedness and extraction accuracy clear their thresholds and pass. Forecast error falls short and is marked held rather than shipped. Beside it, an answer traced from a mapped entity to a governed dataset to the column it was summed from, and below, a strip tracking how often people accept the output unchanged.

What you get

Every solution ships with a scorecard.

Criteria are agreed per task type before the work starts, then scored against held-out examples from your own data. Nothing ships until its row clears, which sometimes means a row does not.

Accounts payable automation / Acceptance criteria / Pre-release. Illustrative figures shown to explain the format. Published accuracy work is reported as ranges across deployments, never as a single number from a single account.
Task typeMetricThresholdResultStatus
Document extractionField-level accuracy≥ 95%97.2%Cleared
Line-item classificationMacro F1≥ 0.900.94Cleared
Demand forecastMAPE≤ 12%14.1%Held. Held. Re-scoped to weekly buckets, re-scored at 9.8%, shipped two weeks later.
Generated summaryGroundedness100% traced100%Cleared
The loop

Evaluation does not end at go-live.

A solution clears a bar before it ships, then keeps being measured against that same bar. The criteria are written once and used twice.

Illustrative figures, shown to explain the format. Published accuracy is reported as ranges across deployments, never as a single figure from a single account.

What makes it different

Two differences, and neither is a better score.

Every vendor will show you a groundedness number, and almost all of them mean the same thing by it: did the answer match the text a retriever handed over. What differs here is what that number resolves to, and who is left running it.

What an answer resolves to

Most evaluation: A chunk of text a retriever judged relevant

Here: A column in your warehouse

What that proves

Most evaluation: The model was consistent with what it read

Here: The figure carries the field it was drawn from, so it can be followed home

Who runs the scoring

Most evaluation: An AI engineering team you hire, on a tool you license

Here: The team delivering the solution, against criteria agreed before the work starts

What you are left holding

Most evaluation: A license, and evaluations somebody has to keep current

Here: The solution and the evidence it works, with the method reused by the next one

Evaluation platforms do their job well. The question is who operates them, and what happens to the evidence when nobody does.

What we publish.

Method and ranges rather than single figures or named accounts. Published this way, the work ships on a cadence instead of waiting on a disclosure decision each time.

How we evaluate before go-live

The harness itself: acceptance criteria per task type, the regression suite, and what good enough to ship actually means.

Model selection benchmarks

How leading models perform on real workload types, extraction, classification, forecasting and code generation, refreshed each quarter.

Accuracy by task type

Published ranges and the method behind them, across deployments rather than from any single account.

Groundedness measurement

How we test that an answer is supported by its source, what it is scored against, and what happens when it is not.

Human acceptance rates

How often output is accepted unchanged versus corrected, and how that curve moves over a deployment's life.

Got questions? We're here to answer them for you

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That an answer resolves to the column it was drawn from, not to a passage a retriever returned. Most groundedness scoring asks whether the answer matched the text it was given, which tells you the model was consistent with what it read but not whether what it read was the right data. Because the Context Engine holds your mapped schema, a figure in an answer carries the field it came from.
Continuously, not once. Every output is evaluated against the criteria the solution shipped against, and an answer that cannot be traced back to a source is treated as a failure rather than a low-confidence pass. That distinction matters: a system that degrades gracefully into plausible text is the failure mode this is built to prevent.
No, and that is the point. Evaluation platforms are instruments, and someone has to wire the application, decide what to score, write the evaluations and keep them current as the solution changes. Here the criteria are agreed before the work starts and the scoring is run by the team delivering it, so what you receive is the solution and the evidence it works.
Built in. Access is centrally managed with role-based permissions applied consistently across environments rather than configured per deployment, and the compliance posture travels with it. RapidCanvas runs in your own VPC, a managed environment or SaaS, and you can move between them without rebuilding.
The permissions of the account it runs under. Access is service-account based and scoped per environment rather than one credential across all of them, with key-pair authentication supported and IAM scoping on the cloud that provides it. Nothing asks for a standing human credential, and every action is logged, so what an agent did and what it was entitled to do are both recoverable.
Model choice is configuration-driven: Azure OpenAI on Azure, Bedrock on AWS, Vertex on Google Cloud. Swapping one for another does not mean rewriting your agents, because the platform layer wraps every call with evaluation and lineage. It does mean the solution clears the regression suite again before the change reaches anyone.
Continuously. The criteria are written once and used twice: a solution clears them before it ships, then keeps being measured against the same bar. Drift is watched against the original acceptance criteria rather than a moving baseline, so the comparison is always to what the solution promised on the day it shipped.
It goes back through the same gates. The examples used to qualify the solution become the suite it is re-run against, so a model swap, a prompt edit or a schema change has to clear the same bar again before it reaches anyone.
Yes. Usage, cost and performance are tracked by solution, and resources can be adjusted to manage spend and efficiency. Cost sits alongside the evaluation rather than in a separate tool, because the trade between a more expensive model and a better acceptance rate is one decision, not two.
The trail. User activity, system events and operational changes are recorded and accessible, and every output can be traced back to the data and the logic that produced it. A decision can be reconstructed during a review rather than reassembled from memory. RapidCanvas holds SOC 2 Type II, HIPAA, GDPR and ISO 42001, and those controls apply consistently across every deployment environment.
Your data moves, and a solution that was accurate in March can quietly stop being accurate by September. Accuracy by task type, groundedness and human acceptance are measured live against the original criteria, and telemetry is read for the likely cause rather than the symptom.
Ranges and method, quarterly, rather than single figures or named accounts. That covers the harness itself, model selection benchmarks on real workload types, accuracy by task type across deployments, how groundedness is measured, and how often output is accepted unchanged versus corrected.
RapidCanvas
G2 Winter 2026 awards: Momentum Leader, High Performer, Best Support, Users Most Likely To Recommend, Users Love Us
G24.9 out of 5
Gartner Peer Insights5.0 out of 5
Certifications: AICPA SOC, ISO 27001, ISO 45001, GDPR, HIPAA

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