Companies · AI / ML
San Francisco · CA, USA; Remote · AI / ML · founded 2024 · https://www.kater.ai
Diligence memoA one-page analyst read on kater.ai — recommendation, valuation, rhythm, risks.→kater.ai: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
kater.ai is one of 2067 AI / ML companies tracked from San Francisco, CA, USA; Remote, on record since 2024. By capital raised it ranks mid-pack (ahead of 62% of sector peers), and mid-pack by modeled valuation est..
Ranking is computed against this company's own sector cohort — reported capital is fact; valuation tiers are modeled.
AI analyst read est. — model-extracted from this company's public description, not a verified fact. 30%
operates a technology-led product inferred from public copy
Grounded in: “You are a venture analyst”
Deliver complete data analysis from a single question
1. You explain your problem. 2. Kater identifies the most important data questions to ask. 3. Kater writes the code. 4. You get insights in seconds rather than weeks. Kater.ai flips the script on enterprise analytics by making every user an expert analyst. It uses a continuous classification engine to turn a single business question into a contextualized package of questions that is specific to your needs. Kater puts the power of data into the hands of business experts while ensuring they use trusted data that is specific to their persona. No more waiting for data analysts. No more wasted time on analysis misfires and rework. Yvonne was a data engineer and analyst who built the entire data stack at CREXi. Robin led engineering in Microsoft. Data is the new oil. Companies are data-rich, insight-poor. We're helping companies become insight-rich. This is the future of data.
As reported in public records reported — not modeled.
Solid bars are reported offering amounts reported; hatched bars are the modeled post-money valuation est. — both on one shared scale so you can read raise-vs-worth at each round directly. Use the toggles to overlay data labels and the niche-peer / market average value lines.
No round amounts on record to chart.
No staged rounds to sequence.
Round size and date are reported; the stage label is inferred from round size. Valuation is modeled from stage benchmarks. Directional, not a quoted figure.
Not enough modeled valuation points to chart a trajectory.
Benchmarked against 2067 companies in AI / ML. Each bar is a median (the middle company, not an average — outliers don't skew it). Two yardsticks: real money raised (reported on Form D) and modeled value (our estimate est.). These are whole-sector medians across all stages, except the per-stage row.
Raised more than 62% of sector peers (real $). Modeled value above 62% of peers (estimate).
Stage is inferred from round size est., not reported on the filing — a round's dollar size maps to a bucket: Pre-Seed <$1.0M · Seed $1.0M–$4.0M · Series A $4.0M–$15M · Series B $15M–$40M · Series C $40M–$100M · Series D+ $100M–$400M · Growth/Late >$400M.
| Stage | Amount · real | Announced | Post-money · est | Value · est | Conf. |
|---|---|---|---|---|---|
| No rounds recorded. | |||||
Predictive signals are modeled est. from this company's own cadence and step-up, plus sector benchmarks — directional, not advice. Peer set and a CSV export live in your analyst workspace.
kater.ai is an official record sourced from the U.S. Securities and Exchange Commission (SEC). U.S. data is aggregated from SEC Form D filings.
Nearest neighbours across the whole database — matched on sector, stage and capital scale, and on shared operators (officers or directors named at both companies in public filings). A discovery shortlist, not a valuation cohort — verify before acting, the same way modeled figures are directional.
| Company | Sector | Stage | Raised · real | Value · est | Why similar |
|---|---|---|---|---|---|
| Accord | AI / ML | — | — | — | same sector |
| Acely | AI / ML | — | — | — | same sector |
| Aedilic | AI / ML | — | — | — | same sector |
| Aemon | AI / ML | — | — | — | same sector |
| Affogato AI | AI / ML | — | — | — | same sector |
| Aftercare | AI / ML | — | — | — | same sector |
| Agentic Labs | AI / ML | — | — | — | same sector |
| Ai Aiba | AI / ML | — | — | — | same sector |
Matched by meaning, not labels — a local language model reads each company's name, sector and description and ranks the closest in that learned space. This catches look-alikes that cross sector boundaries; the structured list above explains its matches, this one trusts the text. Directional, like every modeled signal here.
| Company | Sector | Stage | Value · est | Match |
|---|---|---|---|---|
| Sharpe Agents for quantitative research and financial data science. | AI / ML | — | — | 76% |
| Secoda Secoda is the AI layer for your Analytics | AI / ML | — | — | 75% |
| GetDot Your AI Data Analyst | AI / ML | — | — | 75% |
| Patterns Next-Gen Financial Analysis and Reporting with AI Agents | AI / ML | — | — | 75% |
| Datasaur Datasaur builds a data labeling workforce management platform for NLP. | HR / Worktech | — | — | 75% |
| Intelligent Analytics Corp | AI / ML | Pre-Seed | $55.2M | 75% |
| Datost The proactive AI data analyst | AI / ML | — | — | 75% |
| Crunched Excel AI analyst for Power Users | AI / ML | — | — | 75% |
See where kater.ai sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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