Hyperbots Inc.

Founded in 2023 and incorporated in Dover, Delaware with an engineering hub in Bengaluru, the company targets mid-market enterprises - the segment large enough to drown in finance operations but too lean to hire its way out. Its platform spans the two spines of corporate finance: procure-to-pay (P2P) and order-to-cash (O2C), plus expense management and analytics.

What sets the product apart from a generic chatbot is where the intelligence comes from. Hyperbots reports pre-training its models on more than 30 million finance documents and building HyperLM, a large language model trained exclusively on finance and accounting data rather than the open web. The company reports 99.8% document accuracy and up to 80% straight-through processing in production.

In May 2025 it raised a $6.5M Series A co-led by Arkam Ventures and Athera Venture Partners, following a $2M seed in 2024. The money is going toward U.S. go-to-market and the launch of HyperLM. Hyperbots says it is on track to serve 100+ U.S. clients in 2025 across healthcare, media, manufacturing, retail, EV infrastructure, construction, oil & gas, pharma and real estate.

The founding team came to the problem personally. CEO Rajeev Pathak lived the accounts-payable and accounts-receivable grind as an entrepreneur; Ram Jayaraman watched finance teams struggle up close; Niyati Chhaya brought the AI research. That origin shows up in the product's bias toward the unglamorous, high-volume tasks - the parts of finance that rarely make a demo but consume the week.

Finance is one of the last enterprise functions to be automated, and the reason is the data. Accounting inputs are unstructured and inconsistent - a vendor's invoice looks nothing like the next vendor's, remittances split across payments, and a general ledger has its own coding logic. Rules-based automation breaks on the exceptions, and general-purpose AI hallucinates on numbers that have to reconcile to the cent.

Hyperbots frames its work as converting unstructured data into structured, ERP-ready fields with high reliability. It uses a multimodal mixture-of-experts approach that combines large language models, vision-language models and layout models, so the system understands both the words on a document and where they sit on the page.

How it differs. Rather than wrapping a general model, Hyperbots built a finance-specific one. HyperLM is trained only on finance and accounting data - invoices, POs, contracts and GL structures. Its co-pilots name the exact jobs they do: remittance and bank-statement extraction, intelligent payment matching, exception handling, GL coding and ERP posting.

It also avoids the rip-and-replace tax. Hyperbots ships pre-built connectors for the ERPs mid-market companies already run, so adoption doesn't require a migration. And it has publicly promoted unlimited access over per-seat pricing - a decision aimed at getting whole finance teams onto the tool rather than rationing access.

Model. Hyperbots is a B2B SaaS company. It sells subscription access to its agentic finance co-pilots to mid-market enterprises and integrates with the ERP and finance systems those companies already use. Notably, it has promoted an unlimited-access model rather than per-seat pricing, so adoption isn't throttled by license counts.

Customers. The buyers are finance and accounting teams - CFOs, controllers and AP/AR staff. Hyperbots reports being on track for 100+ U.S. clients in 2025 across a wide industry spread: healthcare, media, manufacturing, retail, EV infrastructure, construction, oil & gas, pharma and real estate.

Where it fits. Hyperbots sits in the finance-automation market alongside players such as AppZen, Vic.ai, HighRadius, Tipalti, Stampli, Bill.com, Ramp and Rossum. Its distinguishing bet is combining structured and unstructured data handling in one agentic platform, powered by a finance-specific LLM rather than a general model bolted onto accounting.

Expertise. The company pairs applied AI research - multimodal extraction, domain-trained language models - with deep finance-operations knowledge. That combination is the point: accuracy in accounting is not a nice-to-have, because numbers have to reconcile and audit trails have to hold.