Aevus PrecisionRx Pitch Deck (2020): 15-Slide Seed Deck

Aevus presents a seed-stage pitch for a clinical decision support platform specifically targeting Type-2 Diabetes (T2D). The deck outlines a clear problem: adverse drug reactions and prolonged therapeutic gestation, which they claim costs the healthcare system $3.5 billion annually. With a team heavily weighted toward data science and Johns Hopkins alumni, the company proposes a SaaS model charging $5,000 per active physician user per year. The deck highlights a successful $127,000 pre-seed raise from regional investors and outlines a roadmap leading to a Series A in early 2022. While the tec…

Key takeaways

• The company is seeking a target raise of $420,000 for its seed round according to the source listing.
• Aevus identifies a $3.5 billion annual healthcare cost drain caused by Type-2 Diabetes adverse drug reactions (Slide 2).
• The team consists of six members, including four with data science or machine learning expertise and three Johns Hopkins alumni (Slide 3).
• The revenue model is a SaaS subscription priced at $5,000 per Active Physician User (APU) per facility per year (Slide 4).
• The Total Addressable Market (TAM) is estimated at $2.5 billion, covering all primary care and specialty doctors in the US (Slide 5).
• Aevus has already raised $127,000 in pre-seed funding from i2E inc, OCAST, Piyush Patel, and Dr. Lee Bird (Slide 7).
• The development lifecycle aims for a large-scale trial in July 2021 and Series A financing in February 2022 (Slide 6).
• The product promises a 48-hour report turnaround time and direct EMR integration (Slide 4).

Frequently asked questions

What specific problem is Aevus solving?

Aevus focuses on the implications of incompatible medications in Type-2 Diabetes treatment. According to Slide 2, these include adverse drug reactions like blurred vision and renal disorders, a prolonged therapeutic gestation period of 18-24 months, and 125,000 annual hospital admissions. They quantify the financial impact as a $3.5 billion annual drain on the healthcare system.

How does Aevus plan to generate revenue?

As shown on Slide 4, the company utilizes a SaaS model. They charge $5,000 per Active Physician User (APU) per facility per year. This fee includes an unlimited number of tests, direct EMR integration, and point-of-care prescription guidance. They define APU as an internal KPI used to track customer acquisition.

Who are the target customers for this platform?

Slide 4 identifies three primary segments: Integrated Healthcare Systems (e.g., Kaiser Permanente, Veterans Affairs), University Hospitals (e.g., Johns Hopkins, Stanford), and Stand-Alone Physician Facilities with six or more physicians. Their Serviceable Obtainable Market (SOM) specifically targets West Coast integrated systems, valued at $250 million (Slide 5).

What is the current stage of the product's development?

According to the 'Development Lifecycle' on Slide 6, Aevus reached 'Proof of Concept Ready' in July 2020 using Cerner data. The roadmap indicates they expected to be 'Prototype Ready' in October 2020 and aimed for an 'MVP ready' status by April 2021, followed by live studies in July 2021.

What is the composition of the founding and management team?

The team (Slide 3) is technically oriented, featuring CEO Yash Sagar Santani (Johns Hopkins MS Finance), an Ops lead, a Business Strategy lead, and three data/ML specialists. Notably, Michael Melnick holds a PhD in Brain and Cognitive Sciences, and Bellari Kanjilal has over 12 years of experience in AI and data science.

Aevus PrecisionRx pitch deck: the facts

Company

Aevus PrecisionRx

Year

2020

Stage

Seed

Slides

15

Sector

HealthTech / Clinical Decision Support

Deck type

Seed Deck

Headquarters

United States (implied by US market focus and Oklahoma-based investors like i2E…

Aevus PrecisionRx pitch deck PDF

The full Aevus PrecisionRx deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the Aevus PrecisionRx pitch deck was used for

This is Aevus PrecisionRx’s 2020 seed pitch deck for its machine-learning–powered prescription guidance platform for Type 2 Diabetes, branded Precision Rx, which integrates with EHRs to help physicians select optimal drug classes. The deck targets a $420k seed raise planned for November 2020 to fund continued product development into Spring 2021 and support engineering of their MVP. The company positions itself as a data-driven clinical decision support solution for endocrinologists and primary care physicians, delivered as a cloud-based SaaS integrated into large health systems’ workflows. The deck emphasizes big data–driven drug response modeling, workflow integration with Cerner and other EMRs, and an early focus on US integrated health systems as initial customers.

Business model: Cloud-based B2B SaaS clinical decision support / prescription guidance platform sold to large health systems and integrated health networks, integrating into electronic health records to assist primary care and specialist physicians treating Type 2 Diabetes.

Investors

i2E (via TBFP program)

Founded

2019

Round: Pre-seed / early seed (i2E TBFP funding described as early capital; the $420k is framed as a seed round following a pre-seed completed in February 2020).

Year: 2020 (targeted seed raise planned around November 2020; i2E TBFP funding reported in 2021 investment history as having been made previously).

Raising: $420,000 targeted seed raise planned around November 2020 to fund continued product development into Spring 2021 and engineering of the MVP, as described in a 2020 LinkedIn article by a company representative and in the labeled seed deck.

Raised: At least $52,000 (TBFP investment reported by i2E; described as supporting development of the Precision Rx platform).

Headquarters: Oklahoma City, Oklahoma, United States (reported addresses include 505 E Sheridan Ave., Suite 2117, Oklahoma City, and 3301 12th Ave SE, Suite 913, Norman, Oklahoma).

Industry: HealthTech / Healthcare AI / Clinical Decision Support focused on prescription guidance for Type 2 Diabetes.

Use of funds as presented: According to the 2020 LinkedIn article, the $420,000 seed round was intended to continue product development into Spring 2021 and to enable engineering of the Precision Rx MVP; the deck itself emphasizes further model training, testing, optimization, and inter-model benchmarking.

What happened after the Aevus PrecisionRx deck

Publicly available sources describe Aevus Precision Diagnostics (Aevus PrecisionRx) as an active Oklahoma-based startup developing Precision Rx, an AI-powered prescription guidance platform for Type 2 Diabetes, with early funding support from regional investor i2E and ongoing product development; however, there is no independently verified public information confirming closure of the specific $420

• Launch of Aevus Precision Diagnostics as an Oklahoma City–based company in 2019 focused on AI-driven prescription guidan
• Securing at least one publicized early funding tranche (TBFP-style investment of $52,000) from i2E to support developmen
• Initiating clinical workflow modeling, doctor shadowing, and data extraction efforts, including parsing approximately 30

What the Aevus PrecisionRx deck got right

• Sharp problem definition in a large, well-understood disease area. The deck presents a clear, specific problem in Type 2 Diabetes prescribing—an 11-drug-class landscape with a ‘one size fits all’ approach and underused EHR data—and positions Precision Rx as a big-data and supervised ML solution directly addressing this gap.
• Strong focus on in-EHR workflow integration. Slides and external descriptions emphasize deep integration into EHR workflows, including EMR data extraction, doctor interviews, and generation of prescription guidance reports within Cerner and other systems, demonstrating strong attention to real-world clinical workflow fit.
• Meaningful early traction via doctor shadowing and EHR data parsing. The deck reports substantive early work, such as shadowing six local doctors for 2.5 months and parsing approximately 30,000 patient records from Cerner exports to build and document model methodologies, which provides tangible traction beyond pure concept.
• Aligned enterprise SaaS go-to-market towards integrated health systems. Aevus articulates a clear top-down go-to-market strategy targeting large integrated health systems, university hospitals, and private integrated networks, aligning the SaaS model and EHR integration focus with enterprise buyers who control prescribing workflows.
• Focused initial scope on Type 2 Diabetes. The deck and supporting materials communicate a focused product scope—Type 2 Diabetes prescription guidance—as the initial disease area, which helps concentrate data collection, model development, and regulatory thinking before expanding to other chronic conditions.
• Early external validation from regional investor and directories. External validation from regional investor i2E’s TBFP investment and inclusion in institutional directories (e.g., CB Insights, university business directories) signal third-party recognition of the company’s potential and increase credibility for seed investors.

What could have been stronger

• Limited evidence of live pilots or signed health-system partners. Neither the deck OCR nor external descriptions provide clear evidence of executed pilots or live deployments with named health systems, despite targeting large integrated networks (e.g., Kaiser, Ascension); this leaves investors uncertain about real-world validation beyond doctor shadowing and internal modeling work.
• No disclosed ML performance metrics or clinical validation outcomes. While the deck describes parsing 30,000 patient records from Cerner EHR and initiating machine learning model construction, there is no explicit disclosure of model performance metrics (e.g., AUROC, accuracy, calibration) or comparative benchmarks, making it hard to assess the true effectiveness of their drug response modeling.
• Unclear regulatory strategy for AI clinical decision support. The OCR and external narratives emphasize Type 2 Diabetes prescription guidance but do not detail the regulatory pathway (e.g., whether Precision Rx will be classified as a medical device or clinical decision support exempt from FDA oversight) or any engagement with regulators, which is a critical concern for AI-based clinical tools in the US.
• High-level market sizing with limited focus on realistic early adopters. The market sizing slide appears to count broad categories such as the total number of US doctors and primary care physicians in integrated systems without narrowing to realistic, reachable early-adopter segments, risking overestimation of practical TAM for an early-stage, integration-heavy product.
• Integration complexity not fully quantified or de-risked. Although Aevus’s business model relies on deep EHR integration (e.g., Cerner, major health systems), the deck text available does not specify technical integration timelines, resource requirements for customers, or partnerships with EHR vendors, leaving integration risk and deployment friction underexplored.

How an investor would read this deck

What draws attention

• Evidence of robust access to EHR data, including the reported parsing of ~30,000 patient records from Cerner exports and doctor shadowing with six local physicians, shows early clinical immersion and data availability for model training.
• The focus on Type 2 Diabetes prescription guidance addresses a large, well-documented clinical and economic problem where trial-and-error prescribing leads to suboptimal outcomes and costs, aligning with health-system priorities.
• Positioning Precision Rx as an in-EHR, workflow-embedded tool for primary care and specialists in integrated health systems matches an enterprise SaaS play with potentially high account values, especially with per-physician pricing.
• The team’s ability to secure at least one publicized pre-seed / TBFP-style funding tranche suggests some external validation and institutional interest in their approach.

Risks that stand out

• AI-based clinical decision support for diabetes is a competitive and evolving space, with academic and commercial systems being developed and even cleared by regulators; Aevus must demonstrate differentiated performance and workflow fit to avoid being commoditized.
• Dependence on deep EHR integration with large health systems means long sales cycles and complex deployments; without established EHR vendor partnerships or implementation playbooks, sales and adoption could be slower than anticipated.
• Regulatory and legal frameworks for AI in clinical decision support are tightening; if Precision Rx is later deemed to require clearance, timelines, capital needs, and risk profiles could change materially.
• The business model relies on convincing integrated health systems and physicians to trust algorithmic prescription guidance; any perceived errors, biases, or misalignment with guidelines could hinder adoption or trigger liability concerns.

Questions this deck invites

• What concrete clinical or pilot deployments (if any) has Aevus run with named health systems since preparing this 2020 seed deck, and what outcome metrics (e.g., reduction in medication switches, improved HbA1c) have been observed?
• How is Precision Rx classified from a regulatory perspective (e.g., non–device clinical decision support vs. Software as a Medical Device), and what legal or regulatory opinions has the company obtained to de-risk commercialization in the U
• What are the current machine learning performance metrics for the drug response models (including generalization across sub-populations), and how were these validated (retrospective vs. prospective studies)?
• How does the company plan to handle integration with multiple EHR vendors beyond Cerner (e.g., Epic, Allscripts), and what implementation timelines and costs should health-system customers expect?
• What is the status and use of proceeds from the previously mentioned pre-seed funding and any subsequent capital, and how much runway remains relative to their product and regulatory milestones?

What founders can take from the Aevus PrecisionRx deck

• A narrowly focused, disease-specific AI clinical decision support product (here, Type 2 Diabetes prescription guidance) can make a more compelling early-stage story than a broad, unfocused AI platform, especially when paired with clear workflow descriptions and target users.
• Embedding detailed clinical workflows (doctor interview, EMR extraction, modeling, prescription guidance report) into the deck helps investors understand operational integration points and how the product will actually be used.
• Early immersion in clinical environments—such as structured doctor shadowing and continuous feedback loops—strengthens the credibility of product insights and helps refine both UX and feature priorities.
• Concrete data work (e.g., parsing 30,000 patient records from a Cerner export and documenting model methodology) is a valuable traction signal in AI healthcare decks, especially when revenue is not yet present.
• Clearly articulating a top-down enterprise sales motion into integrated health systems, rather than only individual physician sales, better aligns with how EHR-integrated solutions are typically bought and scaled.
• Explicitly stating a targeted raise amount (e.g., $420k) tied to a defined time window and specific milestones (engineering the MVP by Spring 2021) provides investors with a clearer view of capital efficiency and near-term goals.

Aevus PrecisionRx pitch deck: common questions

What does Aevus PrecisionRx do?

Aevus PrecisionRx (also referred to as Aevus Precision Diagnostics) is developing Precision Rx, a machine-learning–powered prescription guidance platform that integrates with electronic health records to help endocrinologists and primary care physicians select the most appropriate drug classes for Type 2 Diabetes patients.

How much was Aevus trying to raise with this 2020 seed deck?

According to a 2020 LinkedIn article by a company representative, Aevus planned to raise $420,000 in a seed round around November 2020 to fund continued product development into Spring 2021 and to complete engineering of its MVP. The publicly available seed deck on SlideShare is explicitly labeled with this $420k target raise. There is no independently verified public confirmation that this specific $420k seed round was closed or who invested.

What is Aevus’s business model and who are its customers?

Aevus has publicly described Precision Rx as a cloud-based B2B SaaS platform sold to university hospitals and integrated health networks, integrating directly with their EHR systems and providing drug-class recommendations at the point of care. The deck’s OCR and supporting descriptions indicate a per-physician pricing model (reported as $5,000 per active physician user) and a top-down sales motion through large health systems’ management.

When and where was Aevus PrecisionRx founded?

External profiles indicate that Aevus Precision Diagnostics (Aevus PrecisionRx) was founded in 2019 and is based in the Oklahoma City area, with reported locations including an Oklahoma City headquarters and a Norman, Oklahoma address. The company’s LinkedIn and directory listings place it in the hospitals and healthcare / healthcare AI sector focused on Type 2 Diabetes clinical decision support.

What traction had Aevus achieved at the time of this seed deck?

The available deck OCR and public descriptions emphasize product concept, workflow design, data parsing from Cerner EHR exports, and early model construction using approximately 30,000 patient records. There is no public evidence in the retrieved sources that Aevus had achieved large-scale commercial deployments or significant recurring revenue at the time of the 2020 seed deck; progress appears focused on doctor shadowing, workflow digitization, and initial machine learning modeling.

Sources

Funding and outcome facts on this page were researched on 2026-08-21 from the pages below.

• Data Driven Prescription Management
• Aevus Precision Diagnostics founded as OKC company...
• Aevus Precision Rx | hospitals and health care
• Aevus Seed Deck 2020 | Target Raise=: $420k
• Aevus PRx Seed Round - What it means and why we're raising it
• Aevus Precision Diagnostics
• 2025 Diabetes Management: Technology, Pharmacotherapy & Individualized Care
• Aevus Precision Diagnostics

Aevus PrecisionRx pitch deck slides

What each slide of the Aevus PrecisionRx pitch deck says

Slide 1

Aevus (4 rl 2, <=> PrecisionR, PRea> Automated Clinical Decision Support for Type-2 Diabetes

Slide 2

Broad Based Prescriptions 11 Drug classes to treat Type-2 Diabetes THE PROBLEM 'One Size fits all' approach no longer works PR No Big-Data driven solution Petabytes of data on EHR just being stored and not used for aiding physicians' day-today activities Individual Incompatibility 'Biological algorithm'' can take into consideration only a limited no. of factors prior to making prescription decision

Slide 4

THE SOLUTION P& Physician Ordered Prescription Guidance Platform for Type 2 Diabetes p B(@) &H}I Big Data Analysis EMR Data Extraction Doctor Performs Diagnosis Drug Response Modelling Prescription Guidance Report Doctor conducts patient Precision Rx extracts Precision Rx parses the Precision Rx models out Precision Rx generates a interview, patient info (clinical patient data projected drug responses prescription guidance clinical investigations & observations, lab results, through a Supervised ML for the 11 T2D Drug report sorting all 11 T2D diagnoses ADR history) from the EMR Model, classes for the current drug classes based on patient with Type-2 relevant for making a benchmarking the p…

Slide 5

NC Magic behind the lens | \ ; J A . o# . | < \ | y x v le EB A 3 # / - Yash Sagar Santani Puneet Chadha Christin Bivens Pallav Prakash Michael Melnick Bellari Kanjilal CEO Ops &Biz Dev Data Science Business Strategy ML Architect Data Scientist Unive alumnus University alumnus M University alumnu Rochester alumnus Quantitative and MS Finance I (MS Health Care REAR MBA Enterprise Ri PhD, Brain and Data/Engineerin * Computer Scienc Management '18) « mics (Big Data nd Health C Cognitive Sciences) vi di Engin « Biotechnologist 15) Management o Neuroscientist (data » peak agi se A Marketing, Business Skillet: Data Scien + Skillet: Business + Skillset: Machine en a” Stack Selection, Code Developm…

Slide 6

N Expert feedback & Domain Expertise Dr. Rachel M. Shing Dr. Yasmin Sarfraz Dr. Riaz Sirajuddin Ales Varabyou Emergency Ob-Gy Cardiologis Computational Biology Physic COMANCHE VETERANS AFFAIRS HEARTSOLUTIONS OF JOHNS HOPKINS COUNTY MEDICAL CENTER, DKLAHOMA UNIVERSITY MEMORIAL OKLAHOMACITY HOSPITAL

Slide 9

MARKET SIZING PR Addressable Market* Total Primary Care Doctors & Speciality Doctors in the US Primary Care Physicians in Integrated Health Care Systems Primary Care Physicians in Integrated Health Care Systems on the West Coast Kaiser Permanente, Adventist Health, Banner Health, Franciscan Health System Sum total of all doctors across the United States Major health systems in the US: Kaiser Permanente, Ascension, Hospital Corporation of America, VA frimary.care-physicians by-field/and

Slide 10

TRACTION PR Progress Till Date & Steps Moving Forward Doctor Shadowing 6 local doctors - regularly treat T2D patients Shadowing, questioning, continuous feedback loop for 2.5 months Clinical Workflow ML modelling Construction Initiated First static digitized version of Parsed through 30,000 patier workflow for selecting optimum e s HbATC band & drug class for Cerner EHR Export T2D patient Neatly documented Findings, outcomes, model methodology for treating T2D selection + Cerner Data Tier 2.0 patients MOVING FORWARD Model training, testing, optimization and inter-model benchmarking ems Innovation

Slide text above is read directly from the Aevus PrecisionRx deck PDF embedded on this page.

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