Problem

Enterprise AI teams can build impressive demonstrations but often struggle to make agents dependable in production. The hard problems are supplying institutional context, enforcing user-level permissions, evaluating output quality, retaining ownership of data and models, and improving performance without locking the business to one model vendor or a fixed application. The economic pain is operational: unreliable answers require human rework, while frontier-model inference can be expensive and slow. Context’s main use case is converting complex, document- and system-heavy work—especially support, engineering, diligence, risk, and compliance—into repeatable, authorized agent workflows.

Product / Service

Context is an enterprise AI execution platform that combines a workspace, agent engine, context graph, and evaluation tools. It connects to company data through more than 800 permissioned connectors, lets agents read and write under existing access rules, and allows people and agents to work together in the same environment. Customers can run any model and deploy in their VPC, on premises, or in an air-gapped environment, keeping compute, identity, traces, rubrics, and tuned models under their control.

The platform is designed to improve with use: accepted work becomes training data for models the customer owns, while rubrics and golden sets automatically evaluate each run. Context reports that its self-improving approach produced 40-times-faster turnaround and 28-times-lower cost per case in an internal F100 benchmark, alongside 94% task-completion accuracy at a reported $7 compute cost per case versus $200 for a raw agent.

Market

Context competes in enterprise AI agent platforms and agentic workflow infrastructure, with overlap with vertical AI applications and enterprise knowledge-work automation. Its own comparison names OpenAI Codex, Anthropic Cowork, and vertical tools such as Harvey, Hebbia, and Rogo as alternatives or adjacent solutions. Context’s differentiation is positioned as an execution layer that works across models and frameworks, rather than a single-vendor assistant or a closed application for one domain.

The company has evidence of commercial deployment rather than being merely pre-revenue: its site identifies Qualcomm, FPL, Stripe, Palantir, and Itaú as teams using the platform, and reports that Context was deployed across Qualcomm’s global customer-engineering organization for AI-assisted support and engineering workflows. The Qualcomm case study claims 1,600 production workflows, 98% accuracy, and the customer’s own frontier LLM. The evidence gathered here does not provide a current revenue figure or total customer count; historically, 2023 reporting described Context.ai as having paying customers and having raised a $3.5 million seed round.