Workhelix is the measurement and intelligence layer for enterprise AI. We show you where AI can drive the most productivity across your roles and tasks, how your people are using it today, and how good that usage is. That lets you scale what's working and close the gap between AI's potential and your reality.
To get started, we need LLM usage data (interaction volume by job category) and basic job or HRIS information to map roles to tasks. No PII is required, and your HRIS data doesn't need to be perfect. We can begin with metadata, flat-file transfers, or anonymized data, and about 30 days of usage is typically enough for a meaningful starting analysis.
We use a task-based approach. We know what tasks each role performs and how much time AI can accelerate them, so we can measure value at the task level. For causal measurement, we use staged rollouts and control groups, or synthetic controls when A/B testing isn't possible. We then connect usage to the KPIs you already track.
We're built for speed to insight. We start with what you already have (job descriptions, HRIS data, and about 30 days of LLM usage) and can begin with metadata, flat files, or a data-only analysis before any larger commitment. Once we have your actual usage data, we no longer estimate. We can see exactly what's happening.
- Our methodology is grounded in peer-reviewed research published in Science.- Our proprietary taxonomy of 250,000 tasks is built from 500M+ public job records, O*NET, and Bureau of Labor Statistics data.- We measure both the quantity and the quality of AI usage, not just who is using it.- We connect every insight to a specific action, such as a campaign, prompt library, or targeted enablement.
Yes. Customers use Workhelix to:- Decide where to focus AI investment- Find and scale power-user behaviors- Prioritize which custom GPTs to curate- Compare tools through A/B testing- Decide where to hire versus where to automate- Redesign roles based on which tasks should be AI-assisted versus human-only
Access is typically limited to your AI Center of Excellence and key leadership, with RBAC controls defining who sees what. The teams that get the most value treat Nucleus as a standing agenda item in their AI governance cadence, reviewing opportunity gaps, launching campaigns, and tracking adoption monthly or quarterly.
Workhelix is the measurement and intelligence layer, not a build partner. We help you define KPIs, identify opportunities, and turn insights into action through campaigns, prompt libraries, and targeted enablement. For building agents or AI tools, we can refer you to professional services partners who specialize in implementation.
Nucleus is delivered as a tech-enabled service, not a self-serve SaaS install. A Forward-Deployed Strategist and Forward-Deployed Engineer own onboarding end to end:Weeks 1–2: SOW and data-sharing agreement, stakeholder kickoff, InfoSec review (runs in parallel)Weeks 2–3: HRIS file delivery, AI tool data extraction, PII redaction pipeline setup, platform provisioningWeek 3+: Nucleus pipeline build and test, first AI Opportunity Map deliveredOngoing: dashboard go-live, team training, monthly usage reports, and a recurring steering committee cadenceExact timing depends on your data readiness and InfoSec review process, which is why that step runs in parallel with contracting rather than after it.
Nucleus engagements typically need six roles from the customer organization at some point: an engagement project manager, an AI platform owner (to provide LLM usage access), an HR systems analyst (HRIS access), legal/information security (governance and contract review), a technical decision maker, and an executive sponsor. Not all need to be involved daily. Most of the ongoing work sits with the project manager and platform owner.
No. Nucleus Lite runs on HRIS data and LLM usage metadata only. Many customers start there and add Core once data governance is squared away.
No. Workhelix doesn't require PII. We only collect interaction volume by job category. Any prompt content goes through PII detection, named entity recognition, and classification before it leaves your environment. We also offer anonymized, abstracted prompt data and a private VPC setup with no outbound internet access.
Yes. We've never met a customer with perfect HRIS data. Our goal is to identify the jobs that should be using AI, and that doesn't require a perfect dataset.
We're experienced at working through this and we’re happy to dedicate a full call to your security and legal teams..
We have experience with redaction and pseudonymization for large European companies. Many customers start within a single team to demonstrate value before expanding.
No. Managers never see individual prompt content. All data is redacted, anonymized, and aggregated before it surfaces in Nucleus. Role-based access controls (RBAC) limit who can view analytics, typically the AI Center of Excellence and select leadership rather than frontline managers. We surface patterns, not individual activity.
Yes. Flat-file ingestion is a lower-risk entry point, and some enterprise customers have preferred it given InfoSec constraints. Once trust is established, you can move to a more automated pipeline.
The value comes from discovering best practices and spreading them across your workforce. The companies that win will be the ones that find where AI is driving real productivity and scale it, not the ones that simply deployed a tool.
We provide systematic measurement of both tangible and intangible benefits, using a causal approach with staged rollouts.
Yes. We use a task-based approach. We know which tasks each role performs and how much time AI can accelerate them, so we can measure value at the task level. Even modest per-person gains add up to material financial impact.
This is the perfect time to start measuring. WH can guide where to initially steer AI, create usage baselines, and help spread high value usage. We can also help you define the right KPIs, drawing on what we've seen move across industries as AI adoption improves.
We'd encourage you not to wait, because we can help you define them. We've seen enough deployments across functions to have strong hypotheses about which KPIs move as adoption grows. In the meantime, our leading-indicator metrics give you something to show stakeholders while you build toward more rigorous measurement.
It depends on the function. KPIs that tend to move as AI adoption goes up include:Customer service: resolution rate and handle timeSales: deal velocity and pipeline coverageEngineering: code commit frequency and PR cycle timeBroadly: revenue per employeeWe can help you connect our usage data to the KPIs you already track.
It's a fair question, and leaders in every industry are wrestling with it. Our value metric is a leading indicator of productivity potential, not a headcount reduction tool. Most customers use it to redeploy capacity toward higher-value work, increase output with the same team, and decide where to hire versus where to automate. We encourage leaders to start with how to get more done with the team they have, before making structural workforce decisions.
We settle this before measurement starts, because speed, quality, and output volume move independently — a junior agent that resolves issues faster but less accurately is easy to mistake for a win. We identify the KPI that matters for each function, connect AI usage to that specific outcome, and use causal measurement to isolate which dimension of productivity is moving and by how much.
Lite works from usage metadata alone and estimates value at a fixed rate. Core adds redacted prompt content, which turns those estimates into observed time savings and adds a quality dimension to adoption tracking.
Yes. We can offer a contained pilot as a starting point.
We suggest starting with a pilot. We've worked with multiple customers who needed to build an internal business case before committing to a broader engagement.
The annual AI acceleration opportunity for a large organization typically runs into the hundreds of millions or more. Our pricing is sized relative to that opportunity and by employees covered.
Yes. We can start with data analysis only and defer commercial discussions until you've seen the value.
No. Scaling up the number of agents you deploy doesn't multiply your cost. Pricing is designed to scale with your organization, not with usage or agent count.
Yes. The task-based framework comes from research co-founders Erik Brynjolfsson, Andrew McAfee, and Daniel Rock have published in Science, NBER, and MIT Sloan Management Review, among others.
Usage tells you activity happened. The AI Acceleration Score tells you whether that activity happened on the work where AI actually helps, based on a task-level model, not a job-level guess.
Ideally, we'll use your task list with time estimates. If you don't have one, we can supplement with external data. The approach is worked out together based on the data you have available.
Our model connects insights to specific, actionable interventions: campaigns, prompt libraries, and targeted enablement. The output is always "here's what to do next," not just "here's what we found."
We emphasize full transparency in our calculations. Our methodology is grounded in peer-reviewed research published in Science, co-authored with OpenAI when GPT-4 launched. We can share the paper and walk any technical stakeholder through the full methodology.
It's a starting point, and it's designed to be calibrated rather than accepted as-is. For example, a sales rep's day includes time on the road that a task list may not capture. We validate task descriptions with your subject matter experts and end users, who have the context to make adjustments.
We hear this often. Consider three things:Our team brings 25 years of published, peer-reviewed labor economics research, and that independence carries weight with a board or CFO.Measuring AI productivity is all we do.The real question is whether building it is the highest-value use of your internal engineering and data science talent right now.
The formula isn't the defensible asset. The data is. Our taxonomy was built over years from 500M+ public job records, O*NET, the Bureau of Labor Statistics, and extensive validation. Our supervised learning model is trained on that data. You could replicate the logic in a few months, but not the data asset or the peer-reviewed research. And your data science team's time carries a high opportunity cost.
Most companies can see the quantity of usage: who's sending prompts and how many. Those dashboards usually can't answer two questions: how much should people be using AI, and is the usage any good? Our opportunity scoring provides the benchmark, and our quality scoring shows whether prompts are actually driving business value.
We score prompt content across multiple dimensions: complexity, specificity, whether the interaction is iterative and multi-turn versus a one-off query, and whether it maps to high-opportunity tasks for that role. A prompt asking for a simple definition looks very different from one where someone builds an analysis, iterates on it, and extracts a decision. We surface both volume and quality, so you can tell high-value AI users from light or low-value ones.
The methodology is grounded in peer-reviewed research published in Science.We can walk any technical stakeholder through the full calculation.We're building a feature that lets managers calibrate assumptions, such as time allocation.
It depends on the metadata each tool provides. Some tools, like Gong, don't offer the granularity needed for meaningful analysis. Cursor has specific data for software engineering but requires an agreed-upon measurement approach. We focus on GPT-provider technology because it offers the data quality and transparency reliable measurement requires, and it's typically the most widely adopted and most expensive AI technology in an organization. As long as we're given the tool's schema, we can ingest data from any tool.
We can use job title and location heuristics as a backup to estimate compensation where direct data isn't available.
Workhelix is primarily cloud-based and doesn't currently carry FedRAMP authorization. Potential entry points include quasi-federal organizations (such as USPS or Amtrak) with fewer FedRAMP requirements, and consulting-led engagements using CSV exports. We'd scope the right approach based on your specific requirements.
Typically 30 days of LLM usage data is enough for a meaningful starting analysis. More history improves trend detection and causal modeling, but you don't need a year of data to see where the gaps are.
This is a real and growing challenge as AI moves from chat-first to workflow-embedded. For agentic usage, we rely on OpenAI's compliance API metadata to attribute activity back to the originating role, even when there's no manual prompt. The attribution model continues to evolve as agentic usage matures, and it's an active area of product development.
We understand the ambition. Redesigning the workforce without understanding current adoption patterns sets the stage for failure. You need to know where AI is working, where it isn't, and who your power users are before you can thoughtfully redesign roles and workflows. That's the foundation Workhelix provides.
Muscle memory is a real barrier. We surface best practices from your existing power users and help spread those behaviors to the rest of the team. Rather than relying on top-down mandates, we make the case for change with concrete examples from peers in the same role.
We can run A/B tests, for example comparing ChatGPT and Copilot performance for your highest-opportunity job families. That kind of analysis gives you data to make bigger bets, not just iterate.
That's exactly the problem we solve. We provide intentional, purposeful identification of high-value opportunities instead of a scattershot approach, so deployment decisions are driven by data, not enthusiasm.
We can map high-value versus low-usage GPTs and give you a prioritization framework for curation. We also identify which power-user behaviors are worth scaling and run targeted campaigns to spread them to the right teams.
This is when measurement is most valuable. If even 10% of your organization is using an AI tool, you already have a power law: a small number of people doing most of the interesting work. If we can identify them and replicate their behaviors across 15–20% of the organization, usage and value created both double. You don't need to be mature. You need to have started.
Absolutely. Agents are the future, but the foundation is understanding how your people use AI today. The measurement infrastructure we help you build now (the task taxonomy, the usage baseline, and the power-user map) is what you'll need to evaluate agent performance later. Starting earlier means you'll have a baseline to compare against.
Yes, and we recommend it. Research shows employee retention improves when AI is introduced transparently and tied clearly to individual roles (Brynjolfsson's "Generative AI at Work"). We only see redacted and summarized prompt content, and no raw activity is exposed. RBAC controls ensure analytics are only accessible to appropriate roles. A clean approach is to limit platform access to your AI Center of Excellence and key leadership.
No. Customers typically don't have issues with employee awareness of Workhelix in their environment. Prompt privacy is protected, since we only see redacted, summarized content, and value metrics are controlled through RBAC. Most customers find that transparency accelerates adoption rather than creating resistance.
Skills-based platforms inventory what your people know. We measure what your people are actually doing with AI: the usage, the quality, and the gap between potential and reality. The two are adjacent but solve different problems.
Process mining tools map workflows at the process level and show how work flows through systems. We operate at the task and role level, showing which specific tasks each person does, which of those are AI-suitable, and whether AI is actually being used for them. The two are complementary: process mining shows you the workflow, and we show you the human-AI opportunity within it.
Our focus is giving you the visibility to identify and accelerate opportunities. For agent building, we can refer you to professional services partners who specialize in implementation. We're the measurement and intelligence layer, not the build partner.
The flywheel works like this:1. Identify where your biggest opportunity gaps are.2. Find the power users inside those teams.3. Understand what they're doing that works.4. Turn that into prompt templates or use-case guides.5. Run targeted campaigns (email, Slack, workshops) to the employees most likely to benefit, based on their opportunity score.6. Measure whether adoption goes up, and repeat.Nucleus is designed to be a continuous operating model for AI adoption, not a one-time report.
Access is typically limited to the AI Center of Excellence and key leadership, not deployed broadly to all managers at first. RBAC controls let you define exactly who sees what. The teams that get the most value treat Nucleus as a standing agenda item in their AI governance cadence. They review opportunity gaps, launch campaigns, and track adoption trends monthly or quarterly.
Yes, that's one of the most powerful applications of the data. Nucleus looks at underlying tasks and flags which should be AI-assisted and which should stay human-only. For example, public quarterly earnings reports stay human-only, while routine policy lookups move to AI. We can also cross-reference HR data and prompt content to detect when a job has substantively changed, which is the signal to start rethinking role design, not just tool access.
Yes, it's one of the most interesting signals in the platform. When we overlay your HRIS task data against actual LLM usage, we can show which tasks from a job description appear in prompts and which don't. That gap is actionable either way. Either AI isn't being used for high-opportunity tasks, or people are using AI for things that aren't in the job description at all.
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