How Fiber AI Uses Elasticsearch to Scale Sales Automation to $1M ARR

Fiber AI was co-founded by Aditya Agashe and Neel Mehta, two Forbes 30 Under 30 honorees who set out to automate the most time-intensive parts of B2B sales: finding the right prospects and initiating contact. Their platform enables sales development and business development teams to search a proprietary database aggregating data from more than 50 providers — including BuiltWith, Crunchbase, and G2 — and automatically generate personalized outreach messages.

The core engineering challenge was speed. Fiber AI’s database contains 850 million LinkedIn profiles, data on 40 million companies, and 13 million job postings. To deliver useful search results, the system needed to handle complex multi-variable queries — such as finding companies in India with between five and ten product managers — at sub-second latency. Early testing of ClickHouse, other columnar databases, and vectorized SQL solutions on PostgreSQL all fell short. A query that took Elasticsearch one second took other tools 30 minutes.

With Elasticsearch at the core, Fiber AI built a search layer that can accept a broad query like “rev ops” and automatically map it to “revenue operations” through hundreds of configured synonyms and stop-word rules. The team also uses Elasticsearch’s data tiering (hot/warm architecture) to manage the database as it scaled from gigabytes to dozens of terabytes, cutting monthly infrastructure costs by over 80% without degrading query performance.

The operational results are concrete. Fiber AI’s customers report a 40–50% increase in the share of prospects that are actually targetable for outreach — reducing the time spent building prospecting lists from two to three hours to near-zero. Monthly infrastructure costs dropped from mid-five figures to under $10,000. The company closed deals with Ramp, DocuSign, Flatfile, Lumos, and Secureframe in its first months of operation.

Fiber AI is now adding auto-response features that include meeting links and FAQs, with continued integration of Elastic’s AI capabilities. The team credits Elasticsearch not just as infrastructure but as the primary driver of their ability to ship new search features weekly — capabilities that would otherwise have required months of custom development.