SuggestrWinter 2022Inactive☆Watch⊞Compare

Suggestr is one of 2067 AI / ML companies tracked from Singapore, Singapore, on record since 2021. 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”

Amazon-level personalization for Shopify merchants

Suggestr is a self-serve, no-code personalization solution for the 20M+ e-commerce brands that sell on platforms like Shopify. Marketplaces like Amazon drive up to 30% of their sales from personalized surfaces (recommendations, bundles, frequently bought together, et cetera). They have large data science teams that build such in-house recommendation engines. However, DTC brands can't afford to do this, and they don't have enough customer data to make the conventional recommendation approaches work. Suggestr uses innovative multi-modal AI technology that sees and understands products (needs 100x less data), allowing it to work with brands of all sizes (SME, mid-market, enterprise). The solution takes less than 30 minutes to integrate and helps merchants increase online store revenues by up to 10% with AI-driven upsells and cross-sells.

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.

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

If you work at Suggestr, claim this profile or suggest a correction. We aggregate from public filings, so help us keep your description, website and links accurate.