# Fluency > Enterprise Work Intelligence Platform Fluency is an enterprise work intelligence platform that builds a living map of how work actually happens across an organization. It captures execution patterns across all tools and systems in real time, without integrations, so C-suite executives and Heads of AI can deploy AI with confidence, optimize rollouts continuously, and prove ROI against a verified baseline. ## Product Description Fluency sits between an organization's existing systems and its AI program. A lightweight agent observes how work moves across email, CRM, ERP, spreadsheets, and collaboration tools. It converts that signal into a continuously-updated picture of how every workflow actually runs today, which processes are best suited for automation, and what each deployed AI initiative is worth in hard dollars against the pre-deployment baseline. Three questions drive the platform: Where should we deploy AI next? Is the AI we already deployed working? What is it worth? Fluency answers all three from the same evidence base. ## The Work Ontology The work ontology is the foundation everything else in the platform is built on: https://usefluency.com/ontology A work ontology is a living, continuously updating graph of how an enterprise actually operates, built by observing work at the point of execution. It has three layers. Activities are the work itself, captured as it happens. Actors are who is doing it, resolved to one identity across every system. Artifacts are what the work produces or consumes, tracked as they move across screens, tools, and teams. The edges between them are the handoffs. The ontology does not log what someone clicked. It infers why they were doing it, where processes are repetitive, and where they break down. That correlation to business outcomes is what separates a work ontology from process mining or task mining. Because it updates continuously, agents built on it traverse the live graph at the moment they act, so they adapt when the business changes rather than shattering the way brittle RPA does. ## Platform Features - Platform Overview: https://usefluency.com/features/overview How the pieces fit together, from observation at the point of execution through to deployed, self-adapting automation. - Work Explorer: https://usefluency.com/features/explorer Real-time map of how work actually moves through the enterprise. Captures execution across all tools and surfaces the workflows, handoffs, and variation patterns that process maps miss. Everything Explorer captures becomes part of the work ontology. - Opportunities: https://usefluency.com/features/opportunities Ranked list of AI and automation candidates, scored by projected hours saved, execution frequency, and pattern confidence. The golden path and the highest-leverage automations are read directly from the ontology, replacing intuition-driven prioritization with evidence. - Automations: https://usefluency.com/features/automations Committed opportunities deployed as autonomous agents that build and run automations end to end. Agents traverse the live ontology as they work, and their work feeds back into the ontology like any other work. - Assistant: https://usefluency.com/features/assistant Natural-language interface to the work intelligence layer. Ask questions about how work is running and get answers grounded in the live work ontology. ## Who Fluency Is For - COOs and transformation leaders: operational visibility and AI targeting for the people responsible for enterprise-wide transformation programs. Replaces guesswork with a continuously-updated picture of where AI will compound. - CFOs and finance leaders: hard-dollar measurement for AI investments. Connects workflow compression and capacity gains to P&L impact so finance can defend every line of the AI budget. - CIOs and IT leaders: deployment governance and post-launch verification. Shows which workflows have changed, which systems are actually being used, and which investments are not delivering. - Chiefs of AI: identification of the highest-value automation candidates, and verification that what already shipped is working. ## Glossary Definitions for the terms that decide whether an enterprise AI program compounds or stalls: https://usefluency.com/glossary - Work Ontology: https://usefluency.com/glossary/work-ontology A work ontology is a living, continuously updating graph of how an enterprise actually operates, built by observing work at the point of execution. Its nodes are the actors, activities, and artifacts of work, and its edges are the handoffs between them, with intent inferred for every action. - Work Intelligence: https://usefluency.com/glossary/work-intelligence Work intelligence is the operational layer that provides a current, evidence-based picture of how work actually moves through an enterprise. It is used to target AI deployment, verify post-deployment outcomes, and continuously update the baseline as the business changes. - Work Intelligence Platform: https://usefluency.com/glossary/work-intelligence-platform A work intelligence platform shows large enterprises where AI will have the biggest impact by finding the best work to automate first. It observes work at the point of execution, deploys AI agents, and measures results against an observed baseline. - Enterprise AI: https://usefluency.com/glossary/enterprise-ai Enterprise AI is AI deployed inside large organizations under three constraints absent from consumer AI: integration into existing systems of record, governance against company and regulatory policy, and verification of outcomes against a hard-dollar baseline. - Adoption Metrics: https://usefluency.com/glossary/adoption-metrics Adoption metrics measure whether an AI tool is being used: seat counts, logins, prompts per user, satisfaction scores. They are frequently reported as ROI and are not ROI, because usage says nothing about whether the work improved. - Agent Grounding: https://usefluency.com/glossary/agent-grounding Agent grounding is connecting an AI agent to a current model of the work it is acting on, so it decides from what is true now rather than from rules written when it was built. - AI Readiness: https://usefluency.com/glossary/ai-readiness AI readiness is whether the underlying work is in a state where automating it will help. A process that is broken, undocumented, or highly variable does not improve when AI is applied to it, it gets faster at being broken. - AI Rework: https://usefluency.com/glossary/ai-rework AI rework is the correction, verification, and redoing of AI output that happens after the AI interaction ends. It is invisible to usage dashboards because it falls outside the interaction they measure. - AI Transformation ROI: https://usefluency.com/glossary/ai-transformation-roi AI transformation ROI is the hard-dollar return from deployed AI, measured against a verified baseline. It includes hours saved, cycle-time compression, exception rate reduction, and error reduction, denominated in operating dollars rather than in adoption metrics like seat counts or logins. - Application Rationalization: https://usefluency.com/glossary/application-rationalization Application rationalization is the exercise of deciding which of an enterprise’s software applications to keep, consolidate, or retire. It usually fails on evidence, because licence and login data show what is provisioned rather than which systems carry work. - Automatic Process Discovery: https://usefluency.com/glossary/automatic-process-discovery Automatic process discovery maps how work runs by observing it, rather than by interviewing the people who do it or reconstructing it from system logs. The output is the best-outcome path plus every variation, kept current as execution changes. - Baseline: https://usefluency.com/glossary/baseline A baseline is the measured record of how a process ran before anything was changed, used as the reference point for whether a deployment improved it. A baseline that stops updating stops being a reference point. - Context Graph: https://usefluency.com/glossary/context-graph A context graph captures how work happened and why decisions were made along the way, rather than only the outcome a system recorded. It preserves the coordination, precedent, and evidence that made a decision make sense. - Execution Data: https://usefluency.com/glossary/execution-data Execution data is the record of how work actually moves through an organization: the layer between the inputs a business captures and the outputs it reports. It covers workflow patterns, collaboration structures, and where work slows down. - Execution Intelligence: https://usefluency.com/glossary/execution-intelligence Execution intelligence is real-time visibility into how work is actually executed across every tool, team, and system, rather than how it is documented, planned, or reported after the fact. - Golden Path: https://usefluency.com/glossary/golden-path The golden path is the variant of a process associated with the best outcomes, read from the distribution of how that process actually runs rather than from how it was designed. It becomes the reference way of operating and the route deployed agents follow. - Incrementality: https://usefluency.com/glossary/incrementality Incrementality is the measure of whether outcomes actually improved against a true baseline, as distinct from whether a tool was used. Applied to AI, it asks whether the work got faster, cheaper, or better because of the deployment. - Intent Inference: https://usefluency.com/glossary/intent-inference Intent inference is determining why an action was taken rather than recording what was clicked. It is what allows two people who reach the same outcome through different tools and in a different order to be recognized as doing the same work. - Process Drift: https://usefluency.com/glossary/process-drift Process drift is the gradual divergence between how a process is documented and how it is actually performed. It is why documentation goes stale almost immediately, and it is only visible against a baseline that keeps updating. - Process Mining: https://usefluency.com/glossary/process-mining Process mining reconstructs how a process ran from the event logs that systems recorded. It is usually bought as the discovery phase of a larger program: find the inefficiencies, then redesign, automate, and measure. - Real-Time Process Assurance: https://usefluency.com/glossary/real-time-process-assurance Real-time process assurance is oversight built into execution rather than run as a separate layer of periodic audits. It means seeing workflows as they unfold, flagging deviations as they happen, and holding an audit-ready trail of every action. - Task Mining: https://usefluency.com/glossary/task-mining Task mining records how individuals perform work at the desktop, capturing the steps that system logs miss. It runs for a fixed observation window and then stops, so the picture it produces is accurate on delivery and degrades from then on. - Unstructured Work: https://usefluency.com/glossary/unstructured-work Unstructured work is the execution that happens outside the systems that log it: ad-hoc work in spreadsheets and email, project work that is not a linear process, exceptions handled manually, and legacy applications with no modern logs. ## Industries - Financial Services: https://usefluency.com/industries/financial-services - Healthcare: https://usefluency.com/industries/healthcare - Manufacturing: https://usefluency.com/industries/manufacturing - Retail: https://usefluency.com/industries/retail ## Customers Fluency works with Fortune 500 enterprises and global firms across financial services, insurance, professional services, and manufacturing, including Aon, PVH Corp, HMH, Johns Lyng Group, BoardRoom Australia, KordaMentha, and Specsavers. - Johns Lyng Group (Nick Carnell, CEO): Fluency is foundational in how we understand where to focus, where we get the best impact, and where AI will actually create value. - Johns Lyng Group (Jesse Gill, CTO): Fluency is foundational to how we think about AI investment. It gave us the clarity to define what to build and how to prove the return. - BoardRoom Australia (Mark Saliba, COO): Post-implementation impact was instantaneous. Clear visibility into everyday operations, increased productivity, and measurable improvements across the business. ## Key Facts - Founded: 2023 - Headquarters: 1475 Folsom St, San Francisco, CA, USA - Australia office: Melbourne, VIC, Australia - Certifications: SOC 2 Type II - Contact (sales): sales@usefluency.com - Contact (support): support@usefluency.com - Trust center: trust.getfluency.com.au - LinkedIn: https://www.linkedin.com/company/getfluency - YouTube: https://www.youtube.com/@usefluency - Twitter/X: https://twitter.com/usefluency ## Funding - Pre-seed: A$1.5M (2025, led by Bradley Tabone and Archangel Ventures) - Seed: $6M (2026, led by Accel) ## Blog (most recent posts) - We just raised $6M (2026-02-09): https://usefluency.com/blog/fluency-raises-6m-seed-accel Two first-time founders, a $6M seed round led by Accel, and a mission to make enterprise AI actually measurable. - Fluency vs Consultants vs Celonis vs Mimica vs Worktrace (2026-02-05): https://usefluency.com/blog/fluency-vs-competitors Enterprises are deploying AI blindly. Success comes down to three questions: where to deploy, how to optimize, and how to prove ROI. - Trending: What Context Graphs Mean For Enterprise (2026-01-05): https://usefluency.com/blog/context-graphs Context graphs are being called AI's trillion-dollar opportunity. What are they, and what does this mean for transformation leaders? - The Agent-Native Enterprise: Visibility, Workflows, and the Path Forward (2026-01-06): https://usefluency.com/blog/visibility-for-agents AI agents will transform enterprise operations but can't work in the dark. Here's the infrastructure layer enterprises are building now. - Why AI on Broken Workflows Makes Things Worse (2025-12-29): https://usefluency.com/blog/ai-transformation-failure Most AI transformations fail because enterprises automate broken workflows. How to become truly AI-ready. - How to Actually Measure AI ROI Across Multiple Systems (2025-12-26): https://usefluency.com/blog/how-to-measure-roi-across-systems System-level integration for ROI measurement is fundamentally broken. Here's why system-agnostic work intelligence is the only way to prove AI ROI. - Why AI ROI Remains Invisible (2025-12-21): https://usefluency.com/blog/why-roi-remains-invisible Companies measure AI adoption, not AI impact. Without execution baselines, ROI stays invisible. - The Three Types of Work Data (And Why You Only Have Two) (2025-12-19): https://usefluency.com/blog/three-types-of-work-data Enterprises track inputs and outputs but miss the 95% in between: execution data. Without it, you can't tell if AI is working. - The Hidden Cost of AI: Rework No One Is Measuring (2025-12-18): https://usefluency.com/blog/the-cost-of-ai-rework AI dashboards track usage, not productivity. The hidden cost is rework that erases the gains you think you are getting. - AI Doesn't Have a Capability Problem. It Has an Incrementality Problem. (2025-12-17): https://usefluency.com/blog/ai-incrementality-problem Enterprise AI is scaling fast, but usage metrics hide the real question: did outcomes improve versus a true baseline? - Insurance and the Drive to AI (2025-12-02): https://usefluency.com/blog/ai-in-insurance Insurance carriers are racing toward AI transformation. Most stall between pilot and scale. - Why AI Transformation Projects Stay Stuck in Pilot (2025-12-02): https://usefluency.com/blog/why-transformation-projects-stay-stuck The problem is not technology or adoption. It is a decision problem rooted in missing visibility. - Case Study: How an $11B Manufacturer Cut $10M in App Costs (2025-12-23): https://usefluency.com/blog/enterprise-application-rationalization-case-study Mapped invoice workflows across 4,000 applications and built a $10M+ cost reduction business case in 6 weeks. - Why AI Pilots Should Measure Impact, Not Adoption (2025-11-26): https://usefluency.com/blog/why-AI-pilots-should-measure-for-impact AI pilots fail when enterprises measure adoption rates instead of workflow change. - Why AI Use Case Discovery Should Start with Visibility (2025-11-26): https://usefluency.com/blog/ai-use-case-starts-with-visibility Without visibility into how work actually happens, even the best AI strategy is built on incomplete data. - The New Data Layer for Enterprise Execution (2025-11-11): https://usefluency.com/blog/the-new-data-execution-layer For the first time, enterprises can see how work actually happens in real time. - The Difference Between Process Mining and Work Intelligence (2025-12-24): https://usefluency.com/blog/process-mining-vs-work-intelligence Process mining shows what happened in your systems. Work intelligence shows why work happened the way it did, and helps you transform it. - How Operations Leaders Identify Process Inefficiencies (2025-11-04): https://usefluency.com/blog/how-leaders-identify-process Work intelligence reveals where processes break down and how to fix them systematically. - Why Work Is the Missing Data Layer in Enterprise AI (2025-11-02): https://usefluency.com/blog/why-work-is-the-missing-layer Enterprises have customer, financial, and product data. The richest operational dataset, work itself, has never been captured at scale. Until now. - The 30-Day Executive Playbook to Prove AI Transformation ROI (2025-10-03): https://usefluency.com/blog/30-day-roi-playbook A systematic 30-day playbook to baseline, deploy, and prove ROI on GenAI, automation, and any enterprise initiative. - Automatic Process Discovery and Optimization (2025-10-06): https://usefluency.com/blog/automatic-process-discovery Manual process mapping is slow and instantly outdated. Automatic discovery continuously observes work and maps workflows in real time. ## Pricing Custom enterprise pricing. Contact sales at sales@usefluency.com or visit https://usefluency.com/demo. Fluency is SOC 2 Type II certified. Custom MSAs available on the Enterprise tier. ## FAQ Q: What is Fluency? A: Fluency is a work intelligence platform that shows C-suite executives and Heads of AI exactly how work gets done across the enterprise, so they know where AI will drive real impact. It automatically captures execution across all teams, tools, and systems in real time without integrations, identifies high-ROI AI opportunities, and measures the ROI of each deployment to scale only what is working. Q: How does Fluency work? A: A lightweight agent aggregates how teams execute work across all applications: CRM, ERP, Excel, email, Slack, everything. It automatically maps all workflows and ad-hoc tasks to give intelligence on where and how AI is best brought in. No integrations, no IT projects, no manual process mapping. Q: How does Fluency measure AI transformation ROI? A: Fluency captures a baseline of how each targeted process ran before deployment, then compares post-deployment outcomes against that baseline continuously. ROI is reported in hard dollars: hours saved, cycle-time compression, exception rate changes, and error reduction. Adoption metrics like seat counts and prompt volumes are not used as ROI inputs. Q: How is Fluency different from process mining tools like Celonis? A: Process mining reconstructs how work ran from system event logs. Fluency captures the work that happened around and between the systems, including the parts the event logs missed. Most automation opportunity lives in those gaps. Fluency also covers agentic AI targeting and post-deployment verification, which process mining tools do not. Q: Does Fluency spy on employees? A: No. Fluency does not show individual productivity scoring, time spent off-task, or idle time. All data generated by an employee is visible to that employee. Fluency filters out non-work activity and requires double opt-in consent at all times. Q: What security certifications does Fluency have? A: Fluency is SOC 2 Type II certified. All data is encrypted in transit and at rest. Visit trust.getfluency.com.au for the full trust center. Q: Does Fluency require integrations or IT projects? A: No. Fluency captures work natively across all existing applications without any integration work. For organizations with advanced needs, API integrations and flexible export options are available, but immediate insights work out of the box. Q: Who uses Fluency? A: COOs and transformation leaders use it to prioritize AI initiatives. CFOs use it to measure and defend ROI. CIOs use it to deploy and govern change without friction. Heads of AI use it to identify the highest-value automation candidates and verify that deployed AI is working. It is built for Fortune 500 complexity. Q: Where is Fluency based? A: Headquarters at 1475 Folsom St, San Francisco, California, with an office in Melbourne, Australia. Q: What is the difference between Fluency and a productivity tool like Microsoft Copilot? A: Copilot and similar tools measure their own usage. Fluency measures whether that usage changed how work actually gets executed. It is the verification and targeting layer that sits above any individual AI tool, showing which ones are compounding value and which are not. Q: How long does it take to see value from Fluency? A: Because there are no integrations, initial workflow maps can be generated within days of deployment. Hard-dollar ROI figures against a baseline are typically available within one quarter of a first AI initiative going live.