Rasgo Financial Model

• Feature store that sits on top of existing cloud data warehouses (ELT architecture, not a separate warehouse).
• Three core workflow pillars:
• TRANSFORM: Profile data, calculate feature importance, apply transforms via click or code using the Rasgo transform library.
• SHARE: Version/track features, enable discovery and reuse across data science teams, expose lineage to business stakeholders.
• SERVE: Promote features to production in a single click/command; monitor data drift; integrate natively with MLOps and data pipelines.
• Open-source Python library (`pyrasgo`) as a community/top-of-funnel entry point.
• Key technical differentiators: 140X reduction in cost to compute; 17X+ faster feature query performance; 30-minute Snowflake deployment; dev features immediately production-ready.

Not explicitly stated in deck. Inferred from context:

• Enterprise SaaS subscription, likely seat-based or consumption-based, given the enterprise customer profile (Fortune 500 companies, energy firms, financial trading firms). Rationale: all named customers are large enterprises; the product integrates into existing cloud data warehouses suggesting per-seat or usage-tier pricing is standard in this space.
• Open-source `pyrasgo` library (70k downloads) likely serves as a PLG (product-led growth) funnel into paid enterprise contracts. Rationale: common pattern for ML infrastructure tools (dbt, Great Expectations, etc.).
• Distribution channel: direct enterprise sales (CEO background in GTM at Platfora, Kinetica, Domino Data Lab, Confluent).

All from slide 2 - "11 Month Highlights":

• Investment: $25.1M total raised (Insight Partners & Unusual Ventures)
• Team growth: 330% - grew from 3 to 13 employees
• Paying clients: 5 paying clients secured
• Open-source downloads: 70k pyrasgo downloads
• Product validation: 400+ user interviews; won head-to-head battles vs. competition

Named enterprise clients/validators:

• AES (energy - Head of Data Science + Analytics quoted)
• Farmer's Fridge
• Guardant (health)
• Stanley Black & Decker
• An unnamed F500 manufacturing company
• An unnamed financial trading firm (Christian Peressin, Managing Partner)
• Snowflake Technology Alliance partnership (Director of Technology Alliances quoted)

No revenue figures (ARR, MRR, ACV) disclosed.

Competitive landscape:

• Positioned as disrupting legacy ETL/ELT tooling: Alteryx, Informatica, Trifacta, Paxata (data engineering side) and fragmented data science tooling (Apache Spark, pandas, scikit-learn).
• Integrated with (not competing against) cloud data warehouses: Snowflake, BigQuery, Redshift; and workflow tools: dbt, Apache Airflow.

Moat claims:

• ELT-native architecture eliminates duplicate data warehouse - cited 140X cost reduction, 17X query speed.
• Feature versioning, experiment time travel, lineage tracking - stickiness through institutional ML knowledge lock-in.
• OSS community (pyrasgo, 70k downloads) as distribution moat.
• Snowflake Technology Alliance validation and co-sell potential.

Founders:

• Jared Parker - CEO; GTM leadership background at Platfora, Kinetica, Domino Data Lab, Confluent. Owns marketing, sales, alliances, finance, ops.
• Patrick Dougherty - CTO; MS in Analytics; data scientist by trade; built data science practice at Slalom from ground up. Owns product and engineering.

Funding: $25.1M total raised. No current ask amount, round size, or use-of-funds breakdown disclosed in deck.

• Archetype + why: Enterprise SaaS ARR model with a PLG (product-led growth) conversion layer. The business has a dual-motion: open-source `pyrasgo` downloads drive organic awareness → direct enterprise sales convert to paid contracts. Revenue is almost certainly ACV/ARR-based subscription given the enterprise customer base. SaaS ARR model is the right archetype.

• Forecast horizon & granularity: 3 years (Year 1–3), monthly for Year 1, quarterly for Years 2–3. Early-stage company with only 5 paying clients - monthly granularity matters for cash planning in Year 1.

• Key drivers & assumptions:
• New logos per quarter: 5 paying clients in ~11 months as baseline; initial ramp of 1–2 new enterprise logos/quarter, accelerating to 3–5 by Year 2. Rationale: small direct sales team, enterprise sales cycles typically 3–6 months.
• Average Contract Value (ACV): $60k–$120k/year per enterprise client. Rationale: early-stage B2B ML infrastructure; F500 names suggest ability to command 6-figure deals, but 5-client base implies pricing is still being established; comparable tools (Tecton, Feast enterprise) typically price in this range.
• Net Revenue Retention (NRR): 110–120%. Rationale: feature store becomes deeply embedded in ML pipelines; expansion natural as more data science teams onboard; no churn data in deck but enterprise ML tooling typically has high retention once embedded.
• Gross logo churn: 5–10% annually. Rationale: early-stage product risk; no deck evidence of churn direction.
• Gross margin: 70–80%. Rationale: SaaS software with cloud infrastructure costs; ELT-native architecture (no separate compute to run) should yield high margins vs. managed compute alternatives.
• Headcount / S&M: 13 employees at time of deck; S&M grows in line with new logo targets; assume 2–3 quota-carrying AEs in Year 1 at ~$150k OTE each.
• R&D spend: ~40–50% of OpEx in Year 1 given early product stage and CTO-led team.
• PLG conversion rate (OSS → paid): 0.1–0.3% of OSS downloads convert to a paid trial/pipeline. Rationale: 70k downloads → 5 paying clients is roughly 0.007% at deck date, but many downloads are likely individual hobbyists; enterprise conversion funnel is separate from raw download count.
• Cash burn / runway: derive from OpEx build; $25.1M raised suggests significant runway, but headcount growth (330% in 11 months) implies aggressive burn.

• Scenarios (Base / Bull / Bear - which variables flex):
• Bull: New logo adds 4–5/quarter by Year 2; ACV scales to $150k+; NRR reaches 125%; Snowflake co-sell accelerates pipeline.
• Base: New logo adds 2–3/quarter by Year 2; ACV $80k; NRR 115%.
• Bear: Sales cycles elongate; only 1–2 new logos/quarter; ACV stays at $50–60k; churn 10%.
• Flex variables: new logo adds, ACV, NRR, gross margin.

• Required sheets / outputs:

1. Assumptions - all drivers with scenario toggles.
2. ARR Bridge - beginning ARR, new ARR, expansion ARR, churned ARR, ending ARR.
3. P&L (Income Statement) - Revenue, COGS, Gross Profit, S&M, R&D, G&A, EBITDA, Net Income.
4. Headcount Plan - by department (S&M, R&D, G&A), with loaded cost.
5. Cash Flow & Runway - cash burn, months of runway given current raise.
6. KPI Summary - ARR, logo count, ACV, NRR, gross margin, burn multiple, CAC payback.
7. Dashboard - charts: ARR bridge waterfall, ARR vs. headcount, logo growth, gross margin %.