Image source: SiliconANGLE.
Colare announced a $2.5M pre-seed round on October 26, 2026, alongside the launch of its simulation-based screening platform. Afore Capital, Garage Capital, and Acadian Ventures backed the round.
The company is aimed at industries that struggle to hire specialized engineers: aerospace, defense, energy, and advanced manufacturing. Co-founder and co-CEO Esther Thomas argues that the bottleneck is engineering time. Technical interview rounds can only be run by engineers, often senior ones, so every candidate who reaches a panel pulls experts away from their work. AI-polished resumes make it easier for weaker candidates to get that far.
Colare’s answer is to test candidates on the job before the interview. Employers upload a candidate list and send the assessment to the whole pool. Colare uses AI to map the role to hands-on tasks spanning areas such as CAD, printed circuit layout, thermal analysis, and finite element analysis, built around real flight and manufacturing constraints. Its model then analyzes each candidate’s process and output and produces a detailed brief for HR teams and engineers.
The hard part is that engineering problems rarely have one right answer. Thomas points to tradeoffs such as mass versus stiffness or cost versus reliability, which can’t be checked against a simple answer key. Colare says it scores decisions against rubrics written by senior engineers who have shipped real hardware, with domain-specific depth for each discipline.
Customers range from startups to large aerospace and advanced manufacturing companies, and roughly half are in aerospace and defense. In one example, a two-person chemical manufacturing startup with no one qualified to assess an electrical engineer used Colare to evaluate applicants directly. In another, a manufacturing subsidiary’s candidate failed the simulation before reaching a final interview with the CEO. These accounts come from the company.
The new funding will go toward expanding the assessment library into more niche deep-tech disciplines and building models that can predict how engineers will handle problems they haven’t seen before. Thomas says the goal is for hiring to rest on demonstrated skill rather than pedigree.
