Match the right models to the right jobs—and know when to reroute, retire, or simplify. Standardize evaluation, selection, and monitoring to improve quality, cost, latency, and risk without creating model sprawl.
LLM evaluation gets expensive and hard to defend when model decisions aren’t governed like a business-critical control system. Teams score differently, exceptions multiply, and quality, cost, latency, and risk start drifting in different directions. That’s when executives start asking questions like:
Are we...
…making model decisions based on evidence—not vendor hype, team preference, or whoever ran the last bake-off?
…simplifying the stack before complexity, latency, and costs become a real issue?
…building one evaluation backbone that keeps model sprawl, routing drift, and exception creep under control?
…able to catch drift early, reroute intelligently, and protect the user experience before bad outputs hit production?
…working from one evaluation truth across quality, cost, latency, and risk?
Our Solution - Build the model decision discipline GenAI scale demands
Built to turn model selection from ad hoc judgment into an enterprise discipline, our Enterprise LLM Evaluation Playbook helps you standardize how models are tested, selected, routed, monitored, and improved—so quality, cost, latency, and risk decisions get stronger as scale grows.
Your LLM Evaluation Playbook @ a Glance
- Structured 1:1 discovery sessions to surface priorities, model decision pain points, and scaling constraints
- A targeted readiness scan to isolate the highest-impact evaluation, routing, monitoring, and governance gaps
- An executive brief covering enterprise LLM evaluation best practices, decision disciplines, and business implications
- Introducing scalable methods to evaluate, select, route, and govern the right multi-model LLM stack
- Exploring applied Use Cases, adoption best practices, and key “Watch Outs”
- Aligning on an actionable scaling plan
- Identifying and prioritizing the evaluation, routing, monitoring, and governance gaps driving the most friction, cost, and decision risk
- Exploring our 15 Enterprise LLM Evaluation Acceleration Guides
- Leveraging a GenAI Strategist-led planning session to define your action plan
- Defining Your Enterprise LLM Evaluation Vision & Strategy
- Evaluation Data & Test Set Design Best Practices
- Model Catalog, Recommendation, and Routing Best Practices
- LLM Evaluation Pilots
- LLM Monitoring & Drift Response
- LLM Evaluation Governance
- Co-deliver quick wins to “make it stick” and accelerate your target-state delivery goals
- Configuring and customizing your LLM Evaluation scaling playbook
- Operationalizing the decision rights, review cadences, and governance needed to run your LLM Evaluation TOM
- Optimizing and evolving your TOM as models, costs, use cases, and risk expectations change
- Configuring and customizing your LLM Evaluation metrics and insights plan
- Operationalizing the scorecards, alerts, and review processes needed to compare models with confidence
- Optimizing and evolving your insights so quality drift, cost creep, and routing issues surface earlier
- < 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 LLM evaluation discipline, model decision gaps, and supporting resources
- Tailor the plan to the evaluation priorities, routing decisions, and evidence gaps that most affect model choice
- Deliver Quick Wins, build capability, and scale priority solutions through one integrated plan
- Identify your priority stakeholders, communication needs, and model evaluation gaps
- Configure and deliver a tailored LLM Evaluation communications plan, custom Comms Hub, and role-specific enablement assets
- Build and sustain momentum with explainers, demos, videos, and proof points.
- Define your quarterly LLM Evaluation review, optimization, and adaptation process
- Enable quarterly strategy and scaling plan updates, with rapid response to major market, innovation, model, and competitor shifts
- Keep your LLM Evaluation approach evergreen by continuously improving how models are compared, where routing decisions need to change, and how performance, cost, and risk expectations evolve
- Identify where your teams need targeted coaching to overcome evaluation, routing, governance, or execution gaps
- Deliver tailored expert support, working sessions, and practical guidance
- Help your teams strengthen evaluation rigor, improve routing and model decisions, and keep your LLM Evaluation efforts moving forward
Choose Your On-Ramp...
Choose the right on-ramp for your LLM Evaluation journey—whether you’re looking to rapidly align and mobilize, solve targeted challenges, or scale your LLM Evaluation holistically.
An Accelerated Alignment & Action Planning Sprint
A fast-paced leadership alignment and action planning sprint to:
- Baseline your current LLM evaluation maturity
- Expose the biggest model decision, routing, and governance gaps
- Align on the priorities that matter most
- Define your path forward
- Identify near-term Quick Wins
Build the Model Decision Discipline GenAI Scale Demands
Confidently scale your LLM Evaluation with a tailored TOM that helps you turn fragmented model choices into a more disciplined, trusted, enterprise-grade decision system.
Targeted LLM Evaluation Quick Wins
Rapidly solve a targeted LLM Evaluation challenge, including:
- Baseline your current evaluation and comparison gaps
- Address a high-priority model selection, routing, monitoring, or simplification issue
- Clarify the evaluation priorities that matter most
- Align on practical actions to move forward
- Deliver focused progress in a matter of weeks
Outcomes you can expect
Improve how clearly you measure model performance against the tasks, standards, and outcomes that matter most.
Give leaders and teams stronger assurance that model choices are grounded in evidence rather than guesswork.
Reduce the time it takes to evaluate options, compare results, and move from testing to action.
Create a more repeatable evaluation approach so model decisions are based on clearer, more reliable signals.
Turn evaluation insights into better model decisions, stronger solution performance, and more meaningful business results.
Complimentary Resources
Curious About What “Great Looks Like”?
Review our “LLM Evaluation” Whitepaper
Want to See How You Compare?
Complete our LLM Evaluation Scan or Assessment
Want an easy way to come up to speed?
Click here to listen to our LLM Evaluation Podcast
Want to dig deeper?
Click here to check out our library of YouTube videos
Frequently Asked Questions
- Why do we need stronger LLM evaluation now?
Because you can’t scale GenAI confidently if you can’t measure model and solution quality well. - What outcomes should we expect from this work?
Higher quality, stronger consistency, faster learning, and clearer evidence of what works. - What happens if we don’t improve LLM evaluation?
Teams rely on opinion and inconsistent testing instead of decision-grade evaluation.
- What do you mean by “LLM evaluation”?
A way to measure response quality, consistency, usefulness, and solution performance. - What are the main deliverables from this work?
Evaluation criteria, sharper signals, and a path to better performance. - What do “Quick Wins” look like in LLM Evaluation work?
Clarify quality measures, tighten test coverage, and improve review consistency.
- Does this only apply to highly mature GenAI programs?
No—it helps early and mature teams improve quality, speed, and confidence. - Can this work across different GenAI solutions and use cases?
Yes—it works across copilots, assistants, workflow tools, knowledge experiences, and other GenAI solutions. - Does this cover more than model benchmarking?
Yes—it covers real-world performance, usefulness, consistency, and testing discipline—not just model benchmarks.
- How do you decide what to evaluate first?
We focus on the evaluation gaps that most improve trust, value, and decisions. - How do you keep LLM evaluation from becoming too academic or heavy?
We focus on the measures and tests that improve decisions and speed learning. - How do you connect evaluation to real solution improvement?
We turn evaluation signals into tuning priorities, design changes, and smarter model choices.
- Who should be involved from our side?
Product, business, and engineering leaders, plus owners of solution quality and performance. - How do you keep evaluation from becoming inconsistent across teams?
We define shared criteria, testing routines, and review methods teams can use consistently. - How do you sustain this after the initial work is done?
We make evaluation a repeatable capability for learning, improvement, and confident scaling.