Make evaluation part of how GenAI gets built, tested, released, and improved—not a late-stage checkbox. Catch regressions earlier, prove improvements with evidence, and ship changes with more confidence.
Evaluation as a service fails under delivery pressure when release discipline isn’t built for non-deterministic systems. Manual checks, fragmented scorecards, and inconsistent standards let regressions slip through and make release decisions harder to defend. That’s when leaders find themselves asking questions like:
Are we...
…using release gates built for non-deterministic systems—instead of pretending a pass/fail check is enough to protect production?
…running evaluations automatically when meaningful changes happen?
…working from one enterprise scorecard for quality, safety, and task success?
…able to prove privacy-safe, representative evaluation end to end—instead of testing on data that wouldn’t stand up to scrutiny?
…detecting quality and safety drift in production fast enough to intervene?
Our Solution - Build the release discipline GenAI scale demands
Built to make GenAI release decisions more measurable, defensible, and repeatable, our Enterprise GenAI Evaluation as a Service Playbook helps you embed evaluation into design, testing, CI/CD, release gates, and production monitoring—so teams can detect regressions early, enforce standards consistently, and ship with far more confidence.
Your Evaluation-Driven Delivery Playbook @ a Glance
- Structured 1:1 discovery sessions to surface release, evaluation, and governance priorities
- A targeted readiness scan to isolate the highest-impact testing, gating, and monitoring gaps
- An executive brief covering enterprise evaluation-driven delivery best practices, scaling requirements, and business implications
- Introducing scalable methods to embed evaluation-driven delivery across the GenAI lifecycle
- Exploring applied Use Cases, adoption best practices, and key “Watch Outs”
- Aligning on an actionable scaling plan
- Identifying and prioritizing the testing, release-gating, and monitoring gaps creating the most delivery risk
- Exploring our 23 Enterprise GenAI Evaluation Acceleration Guides
- Leveraging a GenAI Strategist-led planning session to define your action plan
- Defining Your Evaluation-Driven Delivery Strategy & Governance Framework
- Pre-Production Evaluation Best Practices
- CI/CD Evaluation Integration Best Practices
- Production Guardrails, Monitoring, & Drift Response
- Continuous Improvement & Knowledge Sharing Best Practices
- Co-deliver quick wins to “make it stick” and accelerate your target-state delivery goals
- Configuring and customizing your GenAI Evaluation as a Service scaling playbook
- Defining the decision rights, release gates, and operating cadence required to govern GenAI changes at scale
- Optimizing and evolving your TOM as release cadence, models, risk thresholds, and use cases change
- Configuring and customizing your GenAI Evaluation as a Service metrics and insights plan
- Defining the scorecards, alerting, and review rhythms needed to surface regressions, drift, and release risk early
- Optimizing and evolving your insights so risk signals get clearer as delivery scales
- < 30 Days Wins: Lightly configurable resources and solutions
- 30 – 60 Day Wins: Lightly customizable Quick Wins
- 60 – 90 Day Wins: Increasingly high value Quick Win deliverables
- Baseline your release evaluation discipline, scorecard gaps, and supporting resources
- Tailor the plan to the release gates, scorecard priorities, and evaluation gaps most likely to create regression risk
- Deliver Quick Wins, build capability, and scale priority solutions through one integrated plan
- Identify your priority stakeholders, communication needs, and evaluation service readiness gaps
- Configure and deliver a tailored GenAI Evaluation as a Service communications plan, custom Comms Hub, and role-specific enablement assets
- Build and sustain momentum with explainers, demos, videos, and proof points.
- Define your quarterly GenAI Evaluation as a Service review, optimization, and adaptation process
- Enable quarterly strategy and scaling plan updates, with rapid response to major market, innovation, service, and competitor shifts
- Keep your evaluation service approach evergreen by continuously tightening release standards, updating scorecards, and adapting how evaluation is embedded as delivery evolves
- Identify where your teams need targeted coaching to overcome evaluation, gating, monitoring, or execution gaps
- Deliver tailored expert support, working sessions, and practical guidance where release confidence is weak or delivery teams are stuck
- Help your teams strengthen evaluation discipline, improve release decisions, and keep GenAI delivery moving without lowering the bar
Choose Your On-Ramp...
Choose the right on-ramp for your GenAI Evaluation as a Service journey—whether you’re looking to rapidly align and mobilize, solve targeted challenges, or scale your GenAI Evaluation as a Service holistically.
An Accelerated Alignment & Action Planning Sprint
A fast-paced leadership alignment and action planning sprint to:
- Baseline your current GenAI evaluation service maturity
- Identify the biggest evaluation, release, and monitoring gaps
- Align on the release priorities that matter most
- Define your path forward
- Identify near-term Quick Wins
Build the Release Discipline GenAI Scale Demands
Confidently scale your GenAI Evaluation as a Service with a tailored TOM that helps you turn scattered checks and inconsistent scorecards into a trusted, enterprise-grade release discipline.
Targeted Evaluation-Driven Delivery Quick Wins
- Baseline your current evaluation service and rollout gaps
- Fix a high-priority evaluation, release, or monitoring bottleneck
- Clarify the delivery priorities that matter most
- Align on practical actions to move forward
- Deliver focused progress in a matter of weeks
Outcomes you can expect
Prepare your teams, processes, and standards to support GenAI releases with stronger gates, clearer scorecards, and more dependable evaluation coverage.
Create a more uniform approach to how GenAI quality, safety, and task success are measured across teams, use cases, and release decisions.
Reduce duplication and manual effort by making evaluation easier to run, reuse, and embed across the delivery lifecycle.
Give leaders and teams stronger assurance that GenAI changes are being tested rigorously and released with evidence, not hope.
Turn evaluation as a service into better model decisions, stronger solution quality, and more meaningful business results.
Complimentary Resources
Curious About What “Great Looks Like”?
Review our “GenAI Evaluation as a Service” Whitepaper
Want to See How You Compare?
Complete our GenAI Evaluation as a Service Scan or Assessment
Want an easy way to come up to speed?
Click here to listen to our GenAI Evaluation as a Service Podcast
Want to dig deeper?
Click here to check out our library of YouTube videosFrequently Asked Questions
- Why do we need GenAI Evaluation as a Service now?
Because ad hoc evaluation doesn’t scale—teams need a reusable way to assess GenAI quality across solutions. - What outcomes should we expect from this work?
Consistent evaluation, faster cycles, reusable support, and stronger quality signals. - What happens if we don’t build evaluation as a service?
Teams duplicate effort, apply uneven standards, and improve too slowly to scale well.
- What do you mean by “GenAI Evaluation as a Service”?
A shared service that gives teams reusable ways to evaluate GenAI solutions. - What are the main deliverables from this work?
A service model, reusable methods, and scalable quality support. - What do “Quick Wins” look like in Evaluation as a Service work?
Standardize criteria, improve reusable tests, and reduce duplication across teams.
- Does this only apply to large GenAI portfolios?
No—it helps anywhere multiple teams need shared, repeatable evaluation support. - Can this work across different GenAI use cases?
Yes—it supports copilots, assistants, workflow tools, knowledge experiences, and other evaluated solutions. - Does this cover more than model testing?
Yes—it covers usefulness, consistency, readiness, and improvement signals—not just model testing.
- How do you decide what the service should provide first?
We start with evaluation support that cuts duplication and improves the most important decisions. - How do you keep this from becoming too heavy or centralized?
We design the service to be reusable and easy to use, not another bottleneck. - How do you connect the service model to solution improvement?
We make sure evaluation outputs drive priorities, learning, and better quality decisions.
- Who should be involved from our side?
Product, engineering, and evaluation leaders, plus owners of quality standards and service delivery. - How do you keep evaluation support consistent across teams?
We define shared methods and service expectations so teams get consistent evaluation support. - How do you sustain this after the initial work is done?
We establish a scalable service model that improves quality as demand grows.