LiftigniterWinter 2014Acquired☆Watch⊞Compare

Liftigniter is one of 2067 AI / ML companies tracked from San Francisco, CA, USA, on record since 2013. 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”

Machine Learning personalization for dynamic digital properties -…

LiftIgniter uses cutting edge data science to help publishers and retailers optimize their websites and mobile apps in real-time. Machine learning personalization puts the perfect piece of "content"​ (video, article, item to buy) in front the user at the exact moment when they are most likely to engage or convert - no tags or manual work required. The machine learns and updates itself so that's it's always perfectly in sync with your users and your "content."​ We average 50% improvements in CTR, engagement and conversation - with only a day's work on your part. Imagine creating a truly dynamic, real-time digital property that enables the perfect experience for that user impression at that moment in time. That is LiftIgniter! Our team of PhDs has direct experience building state-of-the-art personalization systems at the petabyte scale for some of the largest companies on the planet. Unless you plan on hiring your own team with deep experience (not likely since very few people with that experience exist), you should contact us to talk about how we can supercharge your digital experiences.

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.

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.

Liftigniter 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.

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.

See where Liftigniter sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.

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