How much did Rakam raise with this deck?

According to the catalogue facts from pitchdeckhunt.com, Rakam raised $225,000 in 2016. The deck itself does not mention the amount raised or the terms of the investment, which is common for decks used in early-stage networking or as a teaser for follow-up meetings.

What is Rakam's core value proposition?

Rakam positions itself as a 'unified analytics platform' that prevents companies from having to build their own in-house data solutions. As shown on slides 3 and 4, they aim to give businesses 'full control' over their data while maintaining the ease of a pre-built platform, effectively bridging the gap between SaaS and custom infrastructure.

Who were the founders of Rakam at the time of this deck?

The team slide (slide 8) lists two co-founders: Nurkan Kirkan, the COO, who previously worked at Yandex, and Burak Emre Kabakcı, the CEO, who has a background with Hazelcast. The deck emphasizes their technical pedigree in data-heavy environments.

What kind of traction did Rakam have in 2016?

Rakam demonstrated significant technical scale for an early-stage startup. Slide 7 highlights that the platform was processing over 5 billion events per month and experiencing 20% month-over-month growth within its open-source community, alongside logos from companies like Ola and Twentify.

What are the biggest missing elements in this pitch deck?

The deck is missing several standard 'must-have' slides: a Business Model (how they make money), a Competitive Landscape (how they beat Mixpanel or Heap), a Financial Projections slide, and a clear 'Ask' slide detailing how much capital they are seeking and how it will be spent.

Rakam Pitch Deck (2016): 9-Slide Breakdown

See all 9 slides of the Rakam pitch deck — a 2016 Early (Other) deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

Rakam’s 2016 pitch deck is a masterclass in brevity, utilizing just 9 slides to communicate a complex technical value proposition. The deck identifies a specific pain point in the data lifecycle: the inevitable transition from third-party SaaS tools to expensive, custom in-house solutions. By positioning itself as a 'unified analytics platform' that offers 'full control,' Rakam targets the gap between restrictive off-the-shelf software and high-overhead custom builds. Despite missing critical financial projections, a business model, and a specific funding 'ask,' the deck successfully highligh…

Key takeaways

The deck identifies a $125B market opportunity by 2019, citing Gartner as the primary source for this projection on slide 2.
Rakam defines the problem as a lack of scalability in existing analytics products, forcing companies to build costly in-house solutions (slide 3).
The solution is presented as a 'One-Click Cloud Deployment' compatible with major providers like AWS, Azure, and Google Cloud (slide 5).
Product architecture is simplified into a flow from 'Rakam API' to 'Rakam BI,' serving departments like Marketing, HR, and Finance (slide 6).
Traction is the deck's strongest point, claiming +5 Billion monthly events and 20% MoM growth in the open-source community (slide 7).
The company lists notable early users or customers including Ola, Qordoba, and Yellow Pages on its traction slide (slide 7).
The team slide features two co-founders with backgrounds at Yandex and Hazelcast, but lacks a broader advisory or engineering team list (slide 8).
The deck completely omits a business model, financial ask, use of funds, or competitive landscape analysis.

Introduction: The 9-Slide Technical Teaser

Rakam’s 2016 pitch deck is an example of a technical 'traction-first' presentation. At only 9 slides, it does not attempt to provide a comprehensive business plan. Instead, it focuses on the massive scale of data the platform was already handling and the technical problem it solves for growing enterprises. In the mid-2010s, the 'modern data stack' was still evolving, and Rakam’s pitch for a unified, full-stack solution was a direct response to the fragmentation of the market.

The Hook and the Market (Slides 1-2)

Slide 1: Title Slide The deck opens with a clean, dark aesthetic featuring a network visualization. The tagline is simple: "A platform for ALL your company's data." It includes contact information and an AngelList link in the top right corner, signaling its use as an outreach tool.

Slide 2: Market Rakam uses a classic 'big circle' market slide. It cites a current market size of $25B, growing to $125B by 2019. The source is attributed to Gartner, with a footnote mentioning "Thousands of analytics tools." While the numbers are large, the slide lacks a breakdown of the Serviceable Addressable Market (SAM) or Serviceable Obtainable Market (SOM), staying at a very high level of abstraction.

The Problem and Solution (Slides 3-5)

Slide 3: Problem The problem is framed as a scalability issue: "Analytics products don't grow fast like your business." The slide uses a three-step visual flow: starting with a basic analytics service, moving to multiple fragmented solutions, and finally being forced to "Build in-house Solution." This identifies a specific 'pain point'—the high cost and complexity of building custom data infrastructure when SaaS tools fail to scale.

Slide 4: Solution (Part 1) The solution is defined as a "Unified analytics platform that gives you the full control over your data." The visual shows Rakam sitting in the middle of a bidirectional flow between the Data Scientist and the Business user. This positioning suggests that Rakam serves both the technical and non-technical stakeholders within an organization.

Slide 5: Solution (Part 2) This slide focuses on deployment, titled "One-Click Cloud Deployment Analytics Solution." It displays logos for Amazon Web Services, Google Cloud Platform, Microsoft Azure, Heroku, DigitalOcean, and Docker. This is a crucial 'de-risking' slide for technical investors, as it proves the platform is infrastructure-agnostic and easy to integrate into existing workflows.

Product and Traction (Slides 6-7)

Slide 6: Product The product slide provides a high-level architectural overview. Data flows from four sources—User Events, Data on Database, Third Party Data, and Offline Data—into the "Rakam API." From there, it moves to "Rakam BI," which then distributes insights to Marketing, HR, Finance, and general Business units. This slide effectively communicates the 'full-stack' nature of the product, covering everything from ingestion to visualization.

Slide 7: Traction This is the most compelling slide in the deck. It features two large metrics: "+5 Billion Events / monthly" and "20% MoM Growth (Open Source Community)." Below these numbers are logos of companies using the platform, including Ola (a major ride-sharing company), Qordoba, Twentify, Scorp, and Yellow Pages. For a $225k raise, handling 5 billion events is an impressive technical milestone that suggests the product is battle-tested.

The Team and Closing (Slides 8-9)

Slide 8: Team The team slide, titled "WE Slide 9: Closing The final slide repeats the call to action: "Let’s create your company-wide analytics platform together." It provides the website URL and contact email. Notably, there is no mention of the funding amount, the valuation, or the specific goals for the next 18 months.

What Works in the Rakam Deck

Technical Credibility: By highlighting the 5 billion monthly events and the founders' backgrounds at Yandex and Hazelcast, the deck establishes immediate trust in the product's ability to perform at scale. · Clear Problem Identification: The 'Build vs. Buy' dilemma is a well-known struggle for CTOs. Rakam identifies this exact moment of friction and positions itself as the alternative to a costly in-house build. · Infrastructure Flexibility: Showing compatibility with all major cloud providers and Docker (Slide 5) addresses a major barrier to entry for enterprise software. · Visual Simplicity: The deck avoids 'wall of text' slides. It uses iconography and simple flowcharts to explain complex data pipelines, making it accessible to non-technical investors.

What is Missing from the Rakam Deck

Business Model: There is no explanation of how Rakam makes money. Is it a per-seat license? Is it based on data volume? Is there a premium support model for the open-source version? · Competitive Analysis: The analytics market in 2016 was crowded with players like Mixpanel, Amplitude, and Segment. The deck fails to explain how Rakam differs from these well-funded competitors. · The Ask: A pitch deck is a fundraising tool, yet this deck does not state how much money is being raised or what that money will be used for (e.g., hiring more engineers, sales, or marketing). · Financial Projections: There are no charts showing revenue growth, burn rate, or future projections. The traction shown is purely technical (events and community growth) rather than financial.

What a Founder Should Copy

The Traction Slide Layout: If you have impressive technical metrics (like API calls, events, or users), present them as Rakam did on Slide 7—large, bold numbers with clear labels. · The Problem Flow: Slide 3 is an excellent way to visualize the 'evolution of a problem.' It shows the investor that you understand the customer's journey and the exact point where existing solutions fail. · The 'One-Click' Promise: If your software requires installation, showing a 'One-Click' or 'Integrations' slide with recognizable logos reduces the perceived 'friction' of your product. · Minimalist Design: Rakam keeps the focus on the data. The consistent use of a limited color palette and plenty of white space makes the deck feel professional and modern.

Frequently asked questions

How much did Rakam raise with this deck?: According to the catalogue facts from pitchdeckhunt.com, Rakam raised $225,000 in 2016. The deck itself does not mention the amount raised or the terms of the investment, which is common for decks used in early-stage networking or as a teaser for follow-up meetings.
What is Rakam's core value proposition?: Rakam positions itself as a 'unified analytics platform' that prevents companies from having to build their own in-house data solutions. As shown on slides 3 and 4, they aim to give businesses 'full control' over their data while maintaining the ease of a pre-built platform, effectively bridging the gap between SaaS and custom infrastructure.
Who were the founders of Rakam at the time of this deck?: The team slide (slide 8) lists two co-founders: Nurkan Kirkan, the COO, who previously worked at Yandex, and Burak Emre Kabakcı, the CEO, who has a background with Hazelcast. The deck emphasizes their technical pedigree in data-heavy environments.
What kind of traction did Rakam have in 2016?: Rakam demonstrated significant technical scale for an early-stage startup. Slide 7 highlights that the platform was processing over 5 billion events per month and experiencing 20% month-over-month growth within its open-source community, alongside logos from companies like Ola and Twentify.
What are the biggest missing elements in this pitch deck?: The deck is missing several standard 'must-have' slides: a Business Model (how they make money), a Competitive Landscape (how they beat Mixpanel or Heap), a Financial Projections slide, and a clear 'Ask' slide detailing how much capital they are seeking and how it will be spent.

Cover slide of the Rakam pitch deck — Early Stage (Other) 2016 — Rakam pitch deck, slide 1 (2016)

Rakam pitch deck: the facts

Company: Rakam
Year: 2016
Stage: Early Stage (Other)
Slides: 9
Sector: Analytics / Data Infrastructure
Deck type: Seed / Teaser Deck
Outcome: Raised $225,000
Headquarters: Not stated in deck

Rakam pitch deck PDF

The full Rakam 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 Rakam pitch deck was used for

This deck is a 9‑slide early‑stage fundraising presentation from 2016 for Rakam, a product analytics platform that began as an open‑source, modular analytics infrastructure. The catalogue entry indicates the company raised $225,000 in 2016 with this lean deck, focused on the “build vs. buy” dilemma in data analytics and how Rakam’s open‑source traction supported a commercial product vision. At the time, Rakam positioned itself as a full‑stack, customizable analytics layer that enterprises could deploy on‑premise or in the cloud instead of building their own analytics stack. Later materials show the company evolving into a SaaS product analytics solution on top of customers’ data warehouses.

Business model: Product analytics SaaS platform that connects directly to a customer’s data warehouse and lets non‑technical teams run behavioral and product analytics (segmentation, funnels, retention, flows) without writing SQL.

Round: Early Stage (Other) as catalogued for the 2016 deck.
Year: 2016
Founded: 2016
Headquarters: San Francisco, California, United States
Industry: Analytics / Database Software / Business Intelligence

Raised: $225,000 (reported by pitch‑deck catalogue and teardown article; no independent funding announcement or investor list located).

What happened after the Rakam deck

Following its early positioning as an open‑source, customizable analytics platform and a reported $225,000 raise in 2016, Rakam matured into a data‑warehouse‑native product analytics SaaS serving data‑driven companies across several industries and was eventually acquired by LiveRamp.

Rakam evolved from an open‑source, on‑premise analytics stack into a SaaS product analytics platform that runs directly
The company built a customer base across industries such as e‑commerce, gaming, ride‑sharing, fintech, and consumer good
By 2021, one SaaS revenue database estimated Rakam’s annual recurring revenue at approximately $290,600, describing the
Later company and investor profiles report that Rakam was acquired by LiveRamp, a data connectivity platform, although p

What the Rakam deck got right

Strong, differentiated product positioning around data‑warehouse‑native analytics. Rakam’s core product story—product analytics directly on the customer’s data warehouse, enabling non‑technical teams to run complex behavioral reports without SQL—is a clear, differentiated narrative that bridges technical architecture with business value.
Open‑source and modular origins support technical credibility. The company entered the market with an open‑source, modular analytics platform that enterprises could deploy and customize, which provides credible technical validation and early adoption signals for investors evaluating the 2016 deck.
Broad cross‑industry applicability. External descriptions emphasize that Rakam serves multiple verticals (gaming, e‑commerce, ride‑sharing, fintech, consumer goods), illustrating a broad applicability of the platform and giving investors confidence that the TAM goes beyond a single niche.
Compelling solution to bridge non‑technical users and SQL‑level data. Later materials highlight Rakam’s ability to compile user‑friendly queries (segmentation, funnel, retention) into efficient SQL and run them on the data warehouse, which is an elegant solution to the common gap between data engineering and business users.
Clear evolution from early technical vision to mature, acquirable product. Profiles and documentation show continuity from the early open‑source platform into a commercial SaaS offering with dbt integrations and verticalized reporting, suggesting that the original product vision in the 2016 deck had a clear path to maturation and eventual acquisition.
Concise, thematically focused deck framing around build‑vs‑buy. The reported 9‑slide, lean deck suggests a focused narrative centered on the build‑vs‑buy dilemma, which can help investors quickly grasp both the pain point and Rakam’s solution without being overwhelmed by excessive detail.

What could have been stronger

Unclear verification of the reported $225,000 round. Despite the catalogue stating a $225,000 raise in 2016, no external funding announcement or investor list was found; public SaaS data sources even claim the company is entirely bootstrapped, creating an unresolved discrepancy about whether institutional capital was involved and on what terms.
Limited evidence of quantified traction in the deck. Available historical descriptions focus on product capabilities and open‑source positioning, but do not show that the deck quantified traction (active deployments, event volumes, logos, or revenue), which would make it harder for investors to benchmark adoption against other analytics tools.
Positioning may be too technical and undifferentiated for generalist investors. External descriptions emphasize Rakam’s technical depth—data warehousing, ETL, custom metrics, open‑source modules—but there is no sign the 2016 deck simplified this for non‑technical investors with a clear competitive landscape or straightforward positioning versus familiar tools like Mixpanel or Amplitude.
Limited articulation of concrete use of funds. No externally visible references tie this specific deck to a clearly defined use of funds (e.g., product roadmap, GTM plan, hiring plan) beyond general early‑stage growth, which makes it harder to see how the requested capital linked to concrete milestones.
Business model evolution from open‑source to SaaS not clearly spelled out. Rakam’s open‑source and on‑premise origins suggest a complex deployment and pricing story, yet there is no corroborated evidence that the deck clearly addressed monetization (transition from open‑source to paid SaaS, pricing tiers, or enterprise upsell), leaving potential uncertainty around business model scalability.

How an investor would read this deck

What draws attention

Clarify the discrepancy between sources that describe Rakam as bootstrapped and the catalogue claim of a $225,000 2016 raise, and obtain primary documentation or confirmation of the round.
Evaluate how Rakam’s open‑source, modular analytics stack translated into defensible product advantages versus existing analytics tools (Mixpanel, Amplitude, BI platforms) at the time, including lock‑in and switching costs.
Assess customer traction and usage at the deck’s date: number of paying customers, event volumes analyzed, and representative case studies from the early adopters mentioned later (e.g., Rappi, Ola, Foodpanda, Aptoide).
Understand the go‑to‑market motion from open‑source repositories and BetaList exposure into a sustainable SaaS business—self‑serve vs. sales‑led, target segments, and channel strategy.
Probe how the architecture choice (running analytics directly on the customer’s data warehouse, rather than ingesting data) impacts margins, performance, and scalability, and whether this was already the strategic direction in 2016.

Risks that stand out

Funding history ambiguity—external SaaS data platforms claim Rakam is entirely bootstrapped, while the pitch‑deck catalogue reports a $225,000 raise in 2016, suggesting either misclassification or a very small, possibly pre‑seed round that may not have been widely recorded.
Competitive intensity—Rakam operates in a crowded product analytics and BI landscape, with major incumbents and specialized tools, making differentiation around the data‑warehouse‑native approach critical.
Technical and implementation complexity—the original open‑source, on‑premise focus and later data‑warehouse‑native design require meaningful data engineering effort from customers, which can slow adoption compared with simpler client‑side analytics tools.
Business model evolution risk—shifting from an open‑source, customizable platform to a more opinionated SaaS product analytics offering can alienate early technical adopters if not managed carefully.
Dependence on data‑warehouse ecosystems—Rakam’s value proposition is closely tied to modern cloud data warehouses and dbt‑style analytics engineering; changes in these ecosystems or vendor competition could pressure margins or positioning.

Questions this deck invites

Can the founders provide signed term sheets or wires that confirm the reported $225,000 raise in 2016, along with the cap table and investor identities for that round?
What were Rakam’s key traction metrics at the time of the deck (monthly events processed, active workspaces, paying customers, expansion revenue), and how did these compare to benchmarks in product analytics?
How did Rakam monetize its open‑source and on‑premise offerings in 2016—what pricing model was in place, and how was the transition to the later SaaS/data‑warehouse product structured?
Which specific customer pain points (maintenance burden of DIY analytics, cost of commercial tools, data residency or privacy needs) drove adoption, and how did Rakam measure ROI for early customers?
What was the long‑term strategic path that led to the eventual acquisition by LiveRamp, and how early were data‑connectivity or marketing‑tech integrations part of the product vision?

What founders can take from the Rakam deck

Anchoring a pitch around a clear, real dilemma—in Rakam’s case, the build‑vs‑buy decision that many data‑driven companies face for analytics infrastructure—can make a technical product immediately relatable to investors and customers.
Demonstrating open‑source traction and community adoption is a powerful way to de‑risk early technology bets, especially in developer‑focused products, but it must be paired with a well‑explained path to monetization.
Positioning analytics directly on the customer’s data warehouse (rather than ingesting data into a vendor‑controlled store) can be a strong differentiator if the deck clearly articulates benefits around governance, cost, and performance.
Early‑stage decks for complex data tools benefit from translating technical capabilities (modular analytics stack, custom ETL, data lake features) into concrete user workflows and non‑technical outcomes, such as faster product experiments or clearer customer journey insights.
Open‑source and SaaS hybrids should explicitly explain how free deployments lead to paid tiers—whether through premium features, support, hosting, or enterprise functionality—to address investor concerns about revenue scalability.
Maintaining a consistent funding narrative across public profiles, databases, and pitch materials can prevent confusion and strengthen credibility during future rounds or strategic exits.

Rakam pitch deck: common questions

What does Rakam do?

Rakam is a product analytics platform that connects directly to a company’s data warehouse and lets teams run segmentation, funnel, retention, and other behavioral analyses without needing to write SQL. Originally, it was positioned as an open‑source, full‑stack analytics platform that companies could deploy and customize for their own analytics services.

When was Rakam founded and where is it based?

Multiple profiles and articles state that Rakam was founded in 2016 and is based in San Francisco, California, with additional presence in Istanbul, Turkey.

How much did Rakam raise with its 2016 pitch deck and what round was it?

The pitch‑deck catalogue and a teardown article report that Rakam raised $225,000 in 2016 using a 9‑slide early‑stage deck focused on the build‑vs‑buy dilemma in analytics and leveraging its open‑source traction. Public databases like Latka describe Rakam as bootstrapped with no outside funding, but this conflicts with the pitch‑deck catalogue reference to the 2016 raise. No primary funding announcement or investor list for the $225,000 round could be located.

Who is Rakam’s product for?

Rakam’s platform is aimed at product, growth, and data teams in data‑driven companies who want behavioral analytics directly on their own data warehouse without duplicating data. It has been used in sectors like gaming, e‑commerce, ride‑sharing, fintech, and consumer goods.

What happened to Rakam after this early‑stage period?

Later corporate profiles and a LinkedIn description state that Rakam was acquired by LiveRamp, a data connectivity and marketing technology company. No detailed acquisition announcement (date, price) was surfaced in the retrieved materials, but multiple profiles list the status as acquired by LiveRamp.

Sources

Funding and outcome facts on this page were researched on 2026-08-22 from the pages below.

Rakam pitch deck slides

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What each slide of the Rakam pitch deck says

Slide 1

. angel.co/rakamio | emre@rakam.io ill rakam | A platform for ALL AA your company's data. /

Slide 2

angel.co/rakamio | emre@rakam.io Currently EN RANE By 2019 : *According to Gartner. Thousands of analytics tools.

Slide 3

angel.co/rakamio | emre@rakam.io | PROBLEM Analytics products don't grow fast like your business. Ld] = @ 8 Start using Using More Build in- an analytics Analytics house service Solutions Solution

Slide 4

angel.co/rakamio | emre@rakam.io | SOLUTION Unified analytics platform that gives you the full control over your data. ° i = I, & ye oy 8 Data Rakam Business Scientist

Slide 5

angel.co/rakamio | emre@rakam.io | SOLUTION One-Click Cloud Deployment Analytics Solution wi" amazon D HF webservices Google CloudPlatform EB Microsoft Azure EH HEROKU is) DigitalOcean &