Before building a platform, the founders started and operated their own care delivery business. This gave them firsthand empathy for the challenges of their target customers (health system operators), from dealing with thin margins to implementing EHRs and experiencing clinician burnout.
In the fast-evolving AI landscape, building for current capabilities means a product will be obsolete upon launch. Ambience actively predicts AI advancements 18 months out and designs its products for that future state, treating the present as a constantly shifting foundation.
Initially adopted for clinician retention, AI tools are now proving hard financial ROI. By unlocking new operating margin, AI allows health systems to reinvest in talent and technology. This creates a compounding flywheel that separates top organizations from those at risk of consolidation.
The concept of a 'correct' clinical output is ambiguous. It requires resolving contradictory chart data, capturing a physician's unstated decision-making, and navigating areas like billing codes where two human experts often disagree. This is a reasoning problem, not just a data problem.
Advanced AI models are ineffective in clinical settings without a robust data layer. Ambience had to solve fundamental problems like pulling messy context from inconsistent EHRs and preserving 'decision traces,' which are often destroyed by existing systems with mutable data structures.
Instead of replacing clinicians, AI's promise lies in offloading work to virtual assistants. These agents will prepare pre-visit summaries, ask patients questions beforehand, and manage post-visit follow-ups like checking on prescriptions and lab tests, acting as a force multiplier for the human care team.
The 'bot-on-bot' conflict between provider billing AI and payer denial AI is unsustainable. An AI system that deeply understands the clinical encounter creates a verifiable source of truth. This could make the ROI on both revenue cycle and payment integrity teams negative, forcing collaboration.
With AI handling much of the coding, the most valuable engineers are no longer just prolific coders. Companies now prioritize platform engineers who can make deep architectural choices and product engineers who can embed with customers to excel at requirements gathering, which becomes the new bottleneck.
While many AI tools see low adoption (~20%), Ambience wins enterprise deals by demonstrating over 75% of clinicians use its product daily for 80%+ of visits. This high, sticky utilization is a crucial proof point that resonates with health system leaders and proves the tool's indispensability.
To overcome the slow pace of building on legacy EHRs, Ambience created a proprietary data layer. This layer pulls and structures data from various systems of record, making it AI-ready. This reduces the incremental cost of building new use cases and allows them to scale from 2 to 24 products rapidly.