Companies · AI / ML
London · England, United Kingdom · AI / ML · founded 2023 · https://www.atla-ai.com/
Diligence memoA one-page analyst read on Atla — recommendation, valuation, rhythm, risks.→Atla: limited disclosed financing to assess.
Synthesized from the figures below est. — every claim rests on a number shown on this page.
Atla is one of 2067 AI / ML companies tracked from London, England, United Kingdom, on record since 2023. 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”
The improvement engine for AI agents
Find and fix your agent’s most critical failures in hours, not days. Atla helps developers cut time spent on manually reviewing traces. Atla’s LLM judge evaluates your agent step-by-step, uncovers error patterns across runs, and suggests specific fixes—so you know exactly what to fix and why. Atla supports the most popular agent frameworks teams build with, including LangChain, CrewAI, and OpenAI Agents. With real-time monitoring, automated error detection, and prompt experimentation, Atla gives teams the visibility and control needed to confidently ship agentic systems that work. We’re a team of researchers, engineers, entrepreneurs and operational leaders. Our expertise in evals was honed through training our own purpose-built LLM Judges, Selene and Selene Mini, which are available open-source and have been downloaded 60,000+ times.
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.
Atla 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 |
|---|---|---|---|---|
| RefineTrain AI AI agents rewriting & optimising your internal documentation for LLMs | AI / ML | — | — | 79% |
| Vela AI Agents For Complex Scheduling Coordination | AI / ML | — | — | 79% |
| Atrisa AI Agents for Analog Circuit Design | AI / ML | — | — | 78% |
| Confident AI The LLM Eval and Observability Platform for AI Quality | AI / ML | — | — | 77% |
| Lemma Production Monitoring for AI agents | AI / ML | — | — | 77% |
| Parea Test, evaluate & observe your LLM applications | AI / ML | — | — | 76% |
| Reworkd The simplest way to extract web data at scale | AI / ML | — | — | 76% |
| Rulebase AI agents for financial services. | AI / ML | — | — | 76% |
See where Atla sits in the wider market — its sector, location and stage cohorts, each with their own leaderboards and capital-flow timelines.
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