Bland vs Nfig

Bland lets regulated teams run AI phone agents on live inbound and outbound calls without stitching together separate LLM, speech, and telephony vendors. You build agents that follow your playbooks, hand off to humans with full context, and keep working across voice, SMS, iMessage, and web chat with shared memory between channels. Healthcare, insurance, finance, and logistics are the industries the site calls out first. Bland runs on its own speech models and infrastructure, which is why the homepage reports more than 615 million calls resolved on its stack.

Most voice stacks quote a low platform fee and then bill LLM tokens, speech-to-text, text-to-speech, and telephony as separate line items. Bland rolls LLM, STT, and TTS into one per-minute rate with no token charges, and it was built around SOC 2, HIPAA, and PCI controls rather than retrofitted later. That trade-off shows up in the customer mix: Kin Insurance, Mutual of Omaha, and Samsara are listed on the site, not indie app builders chasing the cheapest minute rate.

Operations teams use Bland to replace IVR phone trees, run AI call centers at scale, and cover nights and weekends without adding headcount. Developers start on a self-serve plan with a REST API and Norm, a builder that wires pathways, calendars, and CRM hooks from plain prompts. Enterprise teams get forward deployed engineers, a 30-day deployment framework, and options for VPC, on-prem, or air-gapped installs when audio cannot leave their network.

Nfig is an agent API that turns natural language into browser actions so AI agents can browse, click, type, and finish tasks on live websites. You send instructions like open a page, search a product, or click a button, and Nfig runs them in a remote cloud browser through its Node.js SDK.

Generic web scrapers pull static HTML. Nfig targets interactive flows that need logins, pop-ups, file uploads, or changing page layouts, with self-healing steps when a site shifts structure. That focus on authenticated, resilient browser control is the trade-off: it is not a general LLM chat API, and it is built around step-based browser automation rather than bulk data extraction alone.

Developers building AI agents for sales demos, in-app customer training, or multi-site research are the core audience. The SDK connects to Playwright-backed remote sessions, docs live at docs.nfig.io, and signup runs through app.nfig.io with a Discord community for support.