Standard Metrics Launches AI Analyst

Today, Standard Metrics is excited to announce the launch of our AI Analyst, an agent that can access and analyze data across the Standard Metrics platform to streamline portfolio analysis.

Earlier this year, we introduced the first version of an AI-powered analyst built on the quantitative and qualitative data already stored in Standard Metrics. That initial release focused on analyzing a single portfolio company at a time. We put it in the hands of customers, watched how they used it, and learned a lot.

Now, we’re taking the next step.

With today’s launch, the AI Analyst works portfolio-wide. Customers can use natural language to ask questions about their entire portfolio and get clear, actionable answers in seconds.

In this post, we’ll share what we learned from the first release, what’s changed in this new version, and what’s coming next.

What we learned: moving beyond a single portfolio company chat

When we first launched our v1 analyst tool in June, our goal was to move quickly: to understand whether customers found it valuable, how they used it, and which features to build next based on their feedback and usage. Hundreds of prompts later, we’ve learned:

• Customers needed an in-app, natural language AI. We offer many tools for easy data exportability from Standard Metrics, including a hosted MCP sever as well as AI-powered embedded BI for chart-building and visualizations. However, many of our customers also wanted to ask natural language questions of the data they have stored on Standard Metrics without having to leave the platform.
• Different roles wanted the tool for different purposes. Operations employees wanted streamlined report preparation and quick search-ability across different data sets on Standard Metrics. Finance teams wanted it to run ad hoc data requests, flag companies that needed assistance, and track valuation metrics. Investors, meanwhile, wanted a tool that worked for board meeting prep as well as ideation around follow-on investments.
• Portfolio-wide analysis was missing. While our customers enjoyed individual company analysis, they also wanted to get answers that spoke to a whole portfolio (e.g. following up “How is XYZ company doing?” with “And how does that compare to the rest of the portfolio?”).

What we launched: the new AI Analyst

After a December closed beta with a small set of customers, we’re excited to launch the AI Analyst to everyone. With portfolio-wide visibility, Standard Metrics’ new AI Analyst helps our customers query the quantitive and qualitative data they have stored on Standard Metrics with simple, natural language questions. From predicting capital needs to monitoring risks to summarizing performance for LPs, portfolio analysis just got a lot easier.

A few things the AI Analyst can do:

Streamline portfolio reviews and cut down on the work required to craft data-driven company commentary

Analyze across multiple data sources, perform calculations, and accurately gauge when it does and doesn’t have the data to answer a question

Simplify ad-hoc analysis and external LP reporting

What’s next

Enabling portfolio-wide analysis was a key unlock for our customers, but we’re not done. Over the next year we want to add:

• New data sets. We plan to add investment data, operational data, fund analytics data, and Global Benchmarking data to the tool’s knowledge base in the coming months.
• Unstructured documents. On top of the new quantitative data being added to the AI Analyst, soon it will also start working across your qualitative notes data in documents that are stored on Standard Metrics.
• Chart-building functionality. Want to move from easy answers to easy charts without leaving Standard Metrics? The AI Analyst will soon be able to create charts matched to your queries.

If you’re a current Standard Metrics customer, we’d also love to hear how we could better improve the AI Analyst experience for you. If you’re not yet a customer but are looking to learn more, fill out the form below and we’d love to get in touch.

We know many VC and PE firms love Excel: it’s where modeling, valuations, portfolio tracking, and more all come together. That’s why, alongside our in-app analysis and visualization tools, we built an Excel Add-In. With one simple formula (SM.GET) firms can pull structured data directly from Standard Metrics into Excel without manual exports or messy spreadsheets.

Today, we’re excited to announce the next step of our commitment to Excel interoperability: Excel Push (SM.PUSH). This new functionality allows VCs to push data that has been adjusted in Excel back into Standard Metrics without the headaches of manual work.

How does the Standard Metrics Excel Add-In work?

Many of our VC and PE customers live in Excel for their core workflows. We’ve seen them use the tool for everything from modeling a company’s financial performance to running valuations to preparing an LP report to QA-ing data to layering portfolio company data stored on Standard Metrics with other sources.

Standard Metrics supports many of those processes in-platform, but also wanted to give our customers maximum flexibility to work with their data in the tools they were already most comfortable with. Two years ago, we launched our Excel Add-In to solve this problem.

Standard Metrics customers simply need to install the Add-In via the Microsoft App Store (a 20-second install that works on both PCs and Macs), quickly get API credentials via the Standard Metrics platform, and log in with those credentials on Excel.

Once connected, the simple formula SM.GET helps users get relevant information for different portfolio companies (such as Cash in Bank in Q1 2025 across different currencies, as shown below).

Used at scale to get multiple metrics stored on the Standard Metrics platform (e.g. a company’s cash in bank, net burn, runway, etc.), this simple formula becomes the base for elaborate dashboards and tear sheets for a firm’s portfolio that incorporate thousands of data points into a unified view in seconds. We offer many pre-built templates for customers to get started and these templates can also be easily adjusted and customized to each firm’s unique needs.

As Casey Franklin, a controller at Polychain Capital, shared, the Excel Add-In “serves as a one-stop-shop evaluation report” and is “a game changer.” Caroline Haun, Operations Director at 8VC, echoed that sentiment, noting that the feature “is going to save me two days’ worth of time.”

How is the new Excel Push function improving this process?

Excel Push allows VCs to send financial data from Excel back into Standard Metrics. Using the formula SM.PUSH, VCs can update metrics stored in Standard Metrics directly from Excel.

This feature was designed as a response to feedback from our customers who work in Excel for processes like financial modeling, valuations, and analysis and who wanted to push that refined or calculated data back into Standard Metrics. This will also enable private equity and growth customers with their own data teams to push in financial statement data from Excel.

Excel Push supports financial metrics (both actuals and budgets), multiple date formats, and multiple currencies. The new Push functionality maintains the security protections built into the initial Excel Add-In launch (like required authentication, specific user configurations, input validation, and rate limiting to prevent abuse). SM.PUSH can also be used in alongside SM.GET in the same workbook, and SM.GET will continue to work exactly the same as at initial launch.

How can we help?

Standard Metrics is excited to support bi-directional Excel interoperability, enabling our customers to work wherever they chose and avoid the headaches of manually updating data across different platforms.

The Excel Add-In, now complete with both Get and Push capabilities, can be downloaded via the Microsoft App Store. Feel free to reach out to your CSM if you have further questions or feedback, or schedule a demo with our team if you’re not yet a customer and would like to learn more.

We released the first version of Standard Metrics’ MCP last month, giving our team and customers a look at what’s possible when Standard Metrics data is leveraged in LLMs.

VC firms can ask Claude, for instance, questions about their portfolio companies (“List the top five SaaS companies in my portfolio by revenue growth”) and get quick answers, without having to build reports. They can layer online search capabilities into analysis (“Who are the biggest competitors to these companies right now?”) to take reporting a step further, without needing to open up a browser. VCs can also utilize LLM functionality beyond question-and-answer (”Write a summary of these five companies’ performance for internal portfolio review”) to nail portfolio reviews, without having to manually review data across spreadsheets, email, and documents.

We immediately set out to onboard all of the firms we support to unlock this functionality. However, local MCPs have two shortcomings which made this process difficult:

1. Demanding setup: Local MCPs require a technical setup process that involves Terminal window installations, code, and JSON configuration changes.
2. Manual maintenance: Local MCPs require creating and actively managing developer keys, which makes uninterrupted MCP use difficult across stakeholders.

Given these limitations and customer demand, we decided to invest in a new and improved MCP. Today, we’re proud to share what that looks like.

What’s new: Standard Metrics’ hosted MCP server

Our new hosted MCP helps all the firms on Standard Metrics use their data in any MCP-compatible tool, regardless of technical ability, and to implement it in seconds, no code required. Prior to this update, our customers connected with tools like Cursor and Anthropic’s GPT, Claude, and now it’s even easier.

Our new MCP also makes the credentials process easier and more secure. Users no longer need to create API keys, save them in a secure location, and then bring them into a JSON config file to be able to use the MCP. Instead, they are now pre-authenticated through their Standard Metrics login.

We currently recommend leveraging the Claude MCP for its accuracy, reliability, ease of setup, and mobile-compatibility, and we have updated our developer docs with instructions to help our customers get started

By eliminating these friction points, we’re delivering on our commitment to interoperability in the private markets, empowering every one of our customers with the ability to pull and analyze portfolio data efficiently. With accessibility solved, we’d like to re-highlight what’s possible with MCPs, and why we’re encouraging every one of our customers to set up the one we’ve built.

Why we’re investing more in our MCP

As our last blog post explained, MCPs enable compatible LLMs to pull, analyze, synthesize, and update data across compatible tools. Since VCs often manage data across systems, we knew investing here would help more customers save time and frustration when it comes to portfolio analysis. We’ve assembled a few videos below to showcase what this looks like in practice. These videos should serve as inspiration for what MCPs can unlock.

Example 1: Portfolio Analysis and Benchmarking

Required tools:

– Claude Desktop and Standard Metrics

Additional data sources / tools:

– PDF-version of Emergence Capital’s benchmarking report

What did Claude achieve?

– Claude pulls quarterly ARR data for AI companies on Standard Metrics, computes 2024 ARR growth, and ranks their performance with additional insights. It then parses Emergence Capital’s report to select the right benchmark and outputs a color-coded performance chart to visualize how each company stacks up.

Example 2: Board Meeting Prep

Required tools:

– Claude Desktop, Standard Metrics, Google Calendar Connector, Notion Connector

What did Claude achieve?

– Claude searches Google Calendar for board meetings happening next week, matches those meetings to companies on Standard Metrics, synthesizes how each are performing, then drafts what should be covered in each company’s board meeting. It then updates the right Notion database with all this info.

Example 3: Portfolio Company Reporting

Required tools:

– Claude Desktop and Standard Metrics

What did Claude achieve?

– Claude identifies the company in question in Standard Metrics, pulls the latest budget vs. actuals data, and analyzes performance versus plan with supporting internal notes. It also uses web search to explain the regulatory change driving the outperformance.

To figure out other ways our MCP can be leveraged, simply determine if the end output involves data that’s accessible via one of our API endpoints, and if Claude will be able to understand the ask as one that Standard Metrics data can help answer. Tip: if you’re trying to make a tool call, use Standard Metrics nomenclature when prompting (e.g. “AI sector companies” vs “AI companies”) to help Claude efficiently and accurately pinpoint what data to reference for analysis. Using generic language risks confusing Claude, prolonging analysis.

Customers can elevate portfolio analysis a step further by connecting Claude Desktop to Gmail, Google Drive, iMessage, AirTable, and dozens of other compatible tools not leveraged in the above examples. For a comprehensive list of MCP-compatible tools, see here.

Looking ahead

In summary, our hosted MCP server solves the two core problems that limited adoption of our first release. Setup now takes seconds instead of hours, and authentication/credentials are handled more automatically, improving accessibility and usability.

As more software platforms adopt the Model Context Protocol, Standard Metrics data will be able to flow across even more tools without investors having to manually copy-paste data between systems. The examples we show above (benchmarking, board prep, budgets vs actuals) are just the beginning of what’s possible.

We built Standard Metrics to give VCs better data infrastructure and we’re thrilled to release this new and improved MCP version to the market. Get set up today by following the instructions in our developer docs and let us know how you’re using it by tagging Standard Metrics on LinkedIn.

We upgraded to a hosted MCP! To read about Standard Metrics’ latest MCP developments, click here. For details on how to set up the new hosted MCP, click here.

Standard Metrics has been committed to building interoperable software to facilitate better collaboration between stakeholders in the private markets since day one. We released an API in 2022, an Excel plug-in in 2023, and more recently have integrated with Derivatas, Omni Analytics, and Snowflake. Continuing on this trend, we’re proud to release our new Model Context Protocol (MCP). Your venture-capital data is already in Standard Metrics; now it’s in your AI chat window, too.

Our new MCP server lets compatible clients like Claude Desktop pull Standard Metrics data into secure and permission-aware context so you can ask questions in natural language and get instant, chart-ready answers in the tools you’re already using.

Why we built an MCP server

Many of our customers have an elaborate tech stack of tools ranging from LLMs to AI-powered note-taking apps, where they may want to leverage Standard Metrics’ quantitative and qualitative portfolio company data. Tools like Claude Desktop and others already let you connect with MCP servers where the tools can securely tap into third-party data like Standard Metrics’. We knew an MCP bridge to the Standard Metrics API would:

• Eliminate copy-paste friction: No more juggling CSVs or browser tabs to explore Standard Metrics’ data in different platforms
• Respect firm-level permissions: Keep sensitive metrics locked to authorized users while maintaining robust permissions in the Standard Metrics platform
• Deliver truly conversational analysis: Ask natural-language questions in the chat interfaces you’re already working out of, and get chart-ready answers in seconds

On top of in-app integrations like our embedded portfolio intelligence and AI portfolio company analyst, we also wanted meet users where they already do their prompting.

What you can do with the MCP server

Broadly, the MCP integration allows you to use Standard Metrics data in compatible tools of your choice, layering the data you have stored on the Standard Metrics platform with the functionality of tools outside of our platform.

Ask Claude, for example, questions about your Standard Metrics data (“List the top five SaaS companies in my portfolio by revenue growth”) and get the answers you need in natural language responses. Layer these tools’ real-time online capabilities into your analysis (“Who are the biggest competitors to these companies right now”) or take advantage of their response styles functionality (”Write a summary of these five companies in my voice for a quick email to another member of my firm”).

Functionality also goes beyond your favorite LLM to other platforms leveraging MCPs like note-taking apps and relationship and deal flow tools. Ask Notion, for example, to pull Standard Metrics data into all relevant documents automatically, eliminating manual workflows and enhancing data retrieval.

Behind the scenes, these tools are using the MCP server to call the Standard Metrics API endpoints, and then synthesize the response into plain-English insights, tables, or exportable visuals.

Configuring with Claude Desktop

While the MCP will allow you to connect to a variety of tools of your choice, many of our customers are particularly excited about the ability to connect with Claude Desktop. A quick how-to:

• OAuth2 handshake: Generate your Client ID and Secret:
  • On the left hand menu, click on Settings
  • Click on Developer Settings
  • Click “Add Application” in the top right
  • Fill in the application name and description
  • Click “Create Application”
  • Copy your Client ID and Client Secret and store them securely (you won’t be able to see the secret again!)
• Configuration setup: Add your Client ID and Secret to your claude_desktop_config.json running the MCP with our provided Docker image or pip-installable.
  • See our GitHub for a detailed configuration example.
• Start using it: Once you restart Claude Desktop, you can ask things like “Show me the latest financial data for AirChair”

What’s next

This release is just step one. We plan to continue to improve on our customers’ AI experiences with Standard Metrics data via additional data streams to external LLMs and improving the experience of our in-app AI portfolio analyst.

Have ideas or feedback? We’d love to hear how you’re using the MCP to supercharge your workflows. Drop us a note or open an issue on GitHub: after all, the project is fully open-source.

Standard Metrics serves as a centralized hub for VC firms’ data (from investment details to portfolio company financials). Historically, we’ve offered our customers various out-of-the-box reports to visualize this data. We’ve also prioritized easy exports along with an Excel add-in and robust API to power quick reporting on other, more customizable data visualization platforms.

Despite these existing tools, our customers continued to demand a higher degree of reporting customizability within the Standard Metrics platform. They wanted to share data in many different formats (presentations, one-sheets, one-off analysis, custom charts, etc.) for many different stakeholders without leaving Standard Metrics.

We considered building out each of the various elements needed to power these tools in-house, but wanted to give our users this functionality as quickly as possible. So we instead began our search for an embedded analytics tool to integrate into our platform — one that would enable our customers to go beyond what we provide out-of-the-box and would help customers easily build their own custom cuts of data, visualizations, and dashboards. One of our key criteria was a flexible UI to empower technical and non-technical users across the firm to answer their most critical portfolio questions. We also needed a tool with an ability to integrate with multiple data warehouses, strong data security, easy data exportability, and AI-related features. Omni, a BI provider fresh off a Series B led by ICONIQ, met the mark.

We’re excited to discuss how this integration will change the Standard Metrics platform, and further our commitment to AI.

Introducing our new portfolio intelligence offering

Standard Metrics will now enable firms to explore, manipulate, visualize, report, and monitor their portfolio companies’ data via a focused reporting and dashboarding solution, all within our platform. On top of saving time and making analysis easier, keeping data on platform also adds to data security (maintaining permissions and traceability unlike an export) as well as metric governance (where data follows the logic and definitions defined in Standard Metrics).

This tool is built with flexibility and easy UX in mind, so non-technical users can also build the visualizations they need. Users can easily drag-and-drop the metrics they want examined into the formats they want them examined in, for example, pulling time into an X-axis and monthly revenue into a Y-axis to form a line chart showing a portco’s revenue over time.

New features are helping our customers to:

• Pull unique cuts of their data to highlight the metrics that matter most to them, and store these cuts in perpetuity to refer back to (rather than building net new each quarter).
• Turn their portfolio company data into easily-built, custom visualizations to instantly answer one-off asks from LPs or support unique analysis.
• Supplement charts with notes explaining qualitative reasons for quantitative performance. These notes can be shared across the firm, so that everyone from analysts to GPs are on the same page about portfolio performance.
• Share insights with others outside the firm via downloadable charts for presentations, automatic email or slack deliverables, and view-level access invites to dashboards.

AI-powered insights

Leveraging Omni’s AI chatbot, we now enable our customers to ask questions of their data using natural language. For example, simply ask “How does XYZ company’s monthly revenue look over time?” for the helper to build the same chart for you. And because our AI chat is backed by Omni’s built-in semantic model, we’re able to better control the AI’s input and outputs to provide more reliable AI answers built on the context of your business.

Have an idea about a further cut you want visualized (e.g. How does that monthly revenue compare to other companies in my portfolio)? You can also ask the AI bot consecutive questions.

What’s next

With Omni, we’re speeding up data reporting and exploration on Standard Metrics, empowering non-technical users to build insights, and giving the answers to questions that VCs have about their portfolio’s financial health, with just a few clicks.

On top of Omni’s AI-powered insights, Standard Metrics also launched our own AI-powered portfolio company analyst this quarter to help with data summarization at the company level. In real language, ask the bot how a company has performed over time or how it compares to other companies within its industry.

Our advanced analytics currently allows users to analyze their companies performance based on data in Standard Metrics. In the future, we plan to incorporate more fund level analytics and act as an integration hub for all data across a firm.

Get in touch if you’d like to learn more.

Our AI Analyst got an upgrade. Read about the new, portfolio-wide AI Analyst here.

Standard Metrics is excited to launch an AI portfolio company analyst that incorporates both the quantitative and qualitative data customers have stored on platform to help answer questions on their companies via natural language queries.

Standard Metrics has long powered portfolio reporting for leading VC and PE firms, including Bessemer Venture Partners, Accel, General Catalyst, and 8VC. Our platform helps investors automate portfolio reporting, centralize financial and qualitative data, and collaborate more effectively with their stakeholders. Over time, we’ve expanded the platform with features like a robust API for interoperability and embedded BI tools for in-app analysis, helping firms turn static data into actionable insights.

We believe this tool will continue to improve the portfolio reporting experience, helping firms cut down on analysis time required to deep dive into a portfolio company in preparation for LP and internal meetings (e.g. follow-on investment decisions), empowering non-technical teammates with easy-to-use natural language Q&A functionality (vs. queries, spreadsheets, and long documents), and enabling in-platform understanding of a portfolio company’s performance (vs. importing or exporting data for analysis on external LLMs).

What we built

Standard Metrics’ AI portfolio company analyst is just like ChatGPT or Claude, except it knows all about your portfolio companies. Instead of shifting through five different financial statements from a company, ask it: “What is the cash balance for this company over the past 5 quarters?” Instead of reading hundreds of pages of company notes, ask it: “Can you give me a quick overview on this company? Just a couple bullet points to get me up to speed?”

Additionally, we’ve already seen some customers use our AI portfolio company analyst to help with follow-on investment decisions, asking questions like “Do you think we should participate in their next round?” or “What would be a fair price here?”

Our AI portfolio company analyst is:

• Trend-optimized: Our AI portfolio company analyst incorporates historical company performance data from everything from tear sheets to financial metrics, so insights are built from long-term performance vs. one-off documents.
• Privacy-minded: Chats are specific to each user, so two users at the same firm will not be able to see each other’s chats.
• Customizable: Everyone at your firm can use Standard Metrics’ AI portfolio company analyst, but users will only be able to use it for the companies they have access to.
• User-focused: The AI portfolio company analyst automatically saves your conversation history, accessible anytime via the top bar. Each new chat begins a fresh conversation thread.
• Part of our core offering: Standard Metrics’ AI portfolio company analyst is available for no additional charge to any firm user already on our platform.

What’s next

We are very excited to continue to iterate and expand on our AI portfolio company analyst. We’re partnering with our customers over the next few months to learn more about how they are using it and what features they’d like to see next.

If you’re a customer, we’d love to hear from you about how you’re using the tool and how we could improve it. If you haven’t yet joined Standard Metrics but would like to learn more, we’d also love to connect.

Seven months ago, we launched our Global Benchmarking product, designed to give our users a deeper understanding of private company financial performance within current market context. In this product, we leverage aggregated and anonymized financial data from 8,000+ companies so that startups can benchmark their performance against peers and firms can better contextualize the performance of their portfolio. Benchmarks include key metrics such as runway, burn per FTE, gross margin, and more.

Our initial launch was focused on point-in-time analysis. While we wanted to speak to the right now, we also realized via conversation and close partnership with our customers that historical performance is critical for understanding the full context of a company’s financial metrics. Is an anomalous performance in revenue growth a fluke of a single quarter or a long-term trend, for example?

We’re excited to announce the launch of Trends, a new feature in our Global Benchmarking product that allows you to see a company’s historical performance. You can now see, over time, how a company’s performance compares to data from a large sample of anonymized startups in the same sector and revenue scale.

Trends in action

One major benefit of Trends is that it can help firms and companies better understand long-term performance versus point-in-time phenomena.

Looking into historical data can show signs of stabilization across a market after periods of fluctuation. For example, median headcount – a metric tracked in Trends – has shifted significantly over the past few years. A team benchmarking their company to Q2 2024 or Q3 2024 data alone might have wondered if we were in the beginnings of a hiring surge and if their portfolio companies should shift towards a more aggressive hiring strategy. But with historical data, firms and their companies can see consistent fluctuations in headcount that suggest a steady hiring plan might be more prudent. And indeed, by Q4 2024, we see that the Q3 uptick looks more like a stabilization in hiring than a true long-term change. Companies aren’t continuing to cut as they did in 2022 and 2023, but they are maintaining carefully right-sized teams for a leaner operational baseline.

Comparing a single portfolio company to its relevant revenue band and sector on metrics like headcount is a powerful way to establish a relevant benchmark for performance. In the chart below – the visuals you will find in Trends – we can see that our example company (Barker Inc., a SaaS startup with between $20-100M in annualized revenue) has consistently higher headcount than the median of other SaaS startups in the same revenue band, despite broader trends towards a more moderate employee baseline. 

Despite this consistently high headcount, however, revenue growth over time has been spike-y, consistently underperforming peers. Their performance over time on these metrics together may suggest that a conversation between the company and its investor about belt tightening may be in order.

Wrap up

Trends is the latest feature that we hope will continue to improve investor relations between firms and their portfolio companies. With Trends, companies can compare themselves to their peers to understand where they are under or out performing over time. Firms, meanwhile, can contextualize their portfolio’s performance to find portcos that may need help or to find opportunities for follow-on investments. Curious to learn more? Check out our Global Benchmarking tool or get in touch below.

Shine Capital is an early-stage venture capital firm with offices in New York and San Francisco. 

The Shine Capital team sought a founder-friendly portfolio monitoring solution that would automate data collection and reporting. They were looking for a platform that would formalize portfolio tracking and optimize internal processes to address ad-hoc requests from various stakeholders, including Limited Partners. 

“We’re excited to partner with Standard Metrics to institutionalize our portfolio reporting process,” said Amanda Niu, CFO at Shine Capital. “We were impressed by Standard Metrics’ easy-to-use interface for both founders and the Shine team. Their ability to automate data collection and store historical data will be immensely helpful to our team’s portfolio review. We were also glad that a number of our portfolio companies had already been onboarded onto the Standard Metrics platform, which expedited the onboarding process.”

After implementing Standard Metrics, the Shine Capital team will no longer have to spend time manually collecting, consolidating, and reporting on portfolio company performance.  Ongoing KPIs, forecasts, financial statements, and other documents will be structured, stored and easily accessible for portfolio reviews. Shine Capital will also be leveraging Standard Metric’s Benchmarking for portfolio reviews and to help provide guidance to their founders.

“We’re very excited to support Shine Capital as a trusted source for portfolio monitoring and LP reporting” said John Melas-Kyriazi, co-founder and CEO of Standard Metrics. “We deeply admire Shine Capital’s investment approach and commitment to partnering with creative entrepreneurs.”

About Shine Capital: 

Shine is an early-stage venture capital firm that partners with creative, convincing, and relentless entrepreneurs to create iconic businesses. Shine leads inception, seed, and Series A financings and backs partner companies as they continue to perform, maintaining perfect alignment with entrepreneurs. Shine invests in thematic applications of technology that tap into trends across technology, markets, and culture to gain conviction to place non-obvious, high-potential bets. 

About Standard Metrics

Standard Metrics provides a collaboration platform for investors and their portfolio companies. VC firms such as General Catalyst, Salesforce Ventures, and Bessemer Venture Partners use Standard Metrics to collect financial and operational metrics from their portfolio companies. Data collection and structuring is automated. Standardized, auditable metrics are delivered to VC back office teams for easy analysis. On the company side of the platform, startup teams save time by structuring updates once and pushing them to multiple investors.

At F-Prime, a leading global venture firm investing in transformative healthcare and technology, access to accurate and timely portfolio data is essential. With over 370 companies in its portfolio, having a centralized and reliable data source is crucial.

By eliminating manual workflows, automatically structuring key data, and contextualizing themes with global benchmarking, Standard Metrics will equip F-Prime’s investment and finance teams with a centralized “source of truth” for portfolio metrics. This will enable them to confidently manage their data while focusing on what matters most: identifying new opportunities and driving due diligence.

Andrew Bognanni, Director of Finance, shares, “Our goal is to seamlessly track key investment data across our portfolio and funds to improve internal and external collaboration, increasing our proactive support to all partners.” 

With Standard Metrics, F-Prime aims to elevate its data collection and benchmarking capabilities while setting a new standard for operational efficiency and reporting in venture capital.

We’re excited to announce our strategic partnership and integration with Derivatas, a leading provider of venture capital valuation software and services. This partnership aligns with our goal of providing a comprehensive system of record and analysis platform for venture capital firms.

Why We Built This Integration

Maintaining accurate, up-to-date portfolio company valuations is a ubiquitous hurdle in the private markets. Collecting the critical underlying financial metrics (revenue, EBITDA, burn, etc.) from portfolio companies is time-consuming, collating those KPIs with investment-level data is manual, and having to maintain up-to-date information across disparate providers is unproductive.

With Standard Metrics and Derivatas partnering together, firms can automate their valuation workflows, more effortlessly maintain a source of truth, and trust that their valuation data is error-free.

How it Works

With the click of a button, portfolio company metrics, fund information, and relevant documents from Standard Metrics will sync with Derivatas’ valuation module, ensuring accurate valuations with minimal manual effort.

This integration delivers several key advantages for our shared venture capital customers:

1. Time Savings: eliminate manual data entry and reduce the time spent on quarterly valuation processes
2. Worry-free reporting: automated data synchronization minimizes the risk of data entry errors
3. Enhanced Analysis: enable more timely and accurate portfolio performance analysis for GPs/LPs

Looking Ahead

This partnership represents our commitment to providing venture capital firms with the most comprehensive and efficient tools for portfolio management. By combining Standard Metrics’ powerful portfolio monitoring capabilities with Derivatas’ expertise in valuations, we’re turning the traditionally onerous quarterly valuation process into a stress-free, streamlined experience for mutual customers.

Contact success@standardmetrics.io or inquiries@derivatas.com to learn how this integration can streamline operations for your firm today.