Companies · AI / ML
San Francisco · CA, USA · AI / ML · founded 2025 · https://attunehq.com
Diligence memoA one-page analyst read on Attune — recommendation, valuation, rhythm, risks.→Attune: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Attune is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2025. 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”
Faster builds, zero effort.
Attune makes blazingly fast build tools that require zero migration effort. As AI enables developers to write code faster than ever, slow builds are increasingly becoming the bottleneck in development velocity. Developers regularly lose hours of productivity every day waiting for their builds. Imagine being able to write code in 1 minute, but needing to wait 30 minutes to compile, deploy, and test it. Existing solutions either require painful migrations to new tools (e.g. Bazel) or rely on just using bigger servers to run your builds. Instead, we do native, first-class integrations into compilers and build systems. With Attune, you don't need to integrate a new tool. Just make a one-line change from `cargo build` to `hurry cargo build` for 20x faster builds. Eliza (ex-Google) and Xin worked together for 3 years at FOSSA, where Eliza was Head of Engineering and Xin was Head of Product. Eliza brings a decade of programming languages and build systems expertise, while Xin brings a decade of experience building developer tools products, most recently as VP of Product at Teleport (S15). Having experienced the pain of slow builds firsthand across hundreds of production codebases, we're building the solution we've always wanted.
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.
Attune 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 |
|---|---|---|---|---|
| Blacksmith The fastest way to run your GitHub Actions | Gaming | — | — | 76% |
| Luminal Making AI run fast on any hardware. | AI / ML | — | — | 76% |
| Floot The easiest way for non-coders to build apps with AI. | AI / ML | — | — | 76% |
| Toolify Build internal tools with AI | AI / ML | — | — | 75% |
| Aurorin CAD Claude code for Mechanical Engineers | AI / ML | — | — | 75% |
| nCompass Technologies Optimize performance on GPUs - 10x faster | AI / ML | — | — | 75% |
| WarpBuild Accelerate software development with WarpBuild's 10x faster CI infra. | SaaS / Software | — | — | 74% |
| ZeroEntropy Artificial Specialized Intelligence | AI / ML | — | — | 74% |
See where Attune sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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