Arched - 2026 Pricing, Features, Reviews & Alternatives

Arched is a cloud-based AI platform built for Architecture, Engineering, and Construction (AEC) firms in India that bid on government contracts across roads, bridges, irrigation, and urban infrastructure. The platform monitors 500+ central, state, and municipal procurement portals, matches published tenders against a firm's credentials and project history, and surfaces only eligible opportunities — eliminating irrelevant results from manual portal monitoring. Deployment is web-based, with custom onboarding, a configured two-week pilot, and data residency options available per contract requirements.

Core capabilities span the full government contracting workflow. Automated document parsing extracts BOQs, eligibility criteria, specifications, and submission requirements from tender documents. An eligibility pre-screening engine scores opportunities against the firm's registered credentials, certifications, financial qualifications, and past project data before surfacing them. Real-time alerts notify teams when matching tenders are published. Competitor intelligence tracks contract award histories and L1 pricing patterns across monitored portals. Credential gap mapping identifies qualification shortfalls and flags strategic pursuits that would qualify the firm for higher-value contracts. Bid assembly tools track submission requirements, provide access to schedules of rates and live material prices, and draft methodologies, clarifications, and proposals in the firm's own voice using its internal document archive.

Arched serves AEC firms of all sizes — from contractors without dedicated business development teams to large EPC firms pursuing multi-crore infrastructure contracts. Firm knowledge compounds over time as the platform learns from every bid, proposal, and closed project to improve future matching. All firm data remains private: it is encrypted in transit and at rest, never used to train shared models, and firms retain the ability to export or delete all data on demand.