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Start here for the decision-making essentials: what Context.ai does, who it is for, how it is accessed, and the first-party sources behind this profile.
Custom pricing based on deployment model, usage, support requirements, and implementation
Context.ai (Explore Interfaces Inc.) is an enterprise AI agent execution platform combining a workspace, execution engine, context graph (Unify), and evals. It supports hosted, VPC, or air-gapped deployment, multiple model providers, 800+ connectors, and inherited SSO permissions.
Read-only public captures of Context.ai’s homepage. Screenshots are dated, never live embeds, and open full-screen.
Short answers to the questions buyers and builders commonly ask about Context.ai. Each answer cites the shared ledger below, where every source is listed once.
Build, run, and improve AI agents on your infrastructure; combines workspace, execution en · Step-level model routing that directs each step to the cheapest model that clears the conf · Measures task pass rate, unit cost, and steps to resolution over a production deployment. · Converts captured traces, rubric grades, and expert feedback into measurable improvement t
Build, run, and improve AI agents on your infrastructure. Context combines a workspace, execution engine, context graph, and evals under your controls.
Workflows with repeatable steps that depend on documents, multiple systems, or expert judg
Context is a good fit when a workflow has repeatable steps but still depends on documents, multiple systems, or expert judgment.
Prebuilt agents for industry-specific workflows across Semiconductors, Financial Services, · Reinforcement learning as a service for production AI agents. · Terminal-first coding agent usable interactively, in scripts, or headless in CI. · Generating living runbooks, decisions, and exceptions documentation reviewed by named owne
Prebuilt agents for industry-specific workflows, systems, and controls.
Held-out evals expose measured regressions but do not guarantee performance on cases the s · Generated wiki pages cannot broaden access to material a reader was not already allowed to
Held-out evals expose measured regressions; they do not guarantee performance on cases the suite does not represent.
Custom pricing based on deployment model, usage, support requirements, and implementation
Pricing depends on the deployment model, usage, support requirements, and implementation scope.
800+ connectors across 18 categories including data warehouses, documents, CRMs, ticketing · Every coding session produces a full trace gradeable against the team's rubrics and usable · Slack integration enabling channel-native agent workflows with inline approval and threade · Bring your own agent framework or use the included one
800+ connectors across data warehouses, documents, CRMs, ticketing, and internal systems.
This profile connects the jobs Context.ai is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
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Context: build, run, and improve AI agents
Build, run, and improve AI agents on your infrastructure; combines workspace, execution en · Legal entity: Explore Interfaces Inc. · Headquartered at 55 2nd St., Suite 1925, San Francisco, CA 94105, US
Build, run, and improve AI agents on your infrastructure; combines workspace, execution engine, context graph, and evals
Managed deployment, customer VPC, on-premises (air-gapped) appliance, and disconnected-operation environments
Supports commercial and open-weight models including Claude, GPT, Gemini, Kimi, or open weights
Identity through customer's IdP, customer-managed keys, audit on every action, and permissions inherited at every connector call
800+ connectors across 18 categories including data warehouses, documents, CRMs, ticketing, and internal systems
Context (legal entity: Explore Interfaces Inc.)
55 2nd St., Suite 1925, San Francisco, CA 94105, US
Workflows with repeatable steps that depend on documents, multiple systems, or expert judgment (diligence, case processing, research, reporting, support, compliance, engineering operations)
Prebuilt agents for industry-specific workflows across Semiconductors, Financial Services, Consulting, Telecom, Public Sector, Industrials, Business Operations, Insurance BPO, and Legal
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Context introduced Applets, small purpose-built interfaces that agents can generate for specific enterprise tasks such as claims review, replacing months-long customization of general-purpose applications with task-specific screens an agent can produce in an a
View source [19]Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
These tools share a workflow, capability or audience with Context.ai. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Custom pricing based on deployment model, usage, support requirements, and implementation scope (no public monthly plans)
Step-level model routing that directs each step to the cheapest model that clears the configured rubric
Credentials are brokered to connectors at runtime rather than stored in runbooks or prompts; configurable per-deployment credential storage, rotation, and revocation
Measures task pass rate, unit cost, and steps to resolution over a production deployment.
Converts captured traces, rubric grades, and expert feedback into measurable improvement through routing, distilled models, and eval-gated releases.
Reinforcement learning as a service for production AI agents.
Terminal-first coding agent usable interactively, in scripts, or headless in CI.
Sessions execute in Agent Sandboxes with scoped credentials rather than shared runners with standing secrets.
Only approved repositories, memories, and connected systems are mounted into a session.
Trains open-weight student models on verified production data, deployed behind Context Inference.
Supports distillation, routing, and policy optimization runs against an approved training split and grader.
Candidate releases are gated by a held-out evaluation suite configured by the customer team.
Command-line interface for a coding agent with the team's context graph mounted.
Every coding session produces a full trace gradeable against the team's rubrics and usable as training signal.
Held-out evals expose measured regressions but do not guarantee performance on cases the suite does not represent.
Wiki drafts documentation from traces of completed work, not blank templates
Unify provides a hierarchical .context filesystem agents traverse like a directory tree
Evals scores every run automatically against expert-authored rubrics and catches regressions the same day
Step-level model routing sends each step to the cheapest model that clears the rubric
Accepted outputs become training data for customer-owned custom models
Hybrid retrieval grounds every query in the full breadth of institutional information rather than a single index
Generating living runbooks, decisions, and exceptions documentation reviewed by named owners
Hosted, customer VPC, or air-gapped via the on-prem Context appliance
Enterprise-grade authorization with IdP identity, customer-managed keys, audit on every action, and inherited permissions at every connector call
Slack integration enabling channel-native agent workflows with inline approval and threaded progress
Bring your own agent framework or use the included one
Generated wiki pages cannot broaden access to material a reader was not already allowed to use
800+ permissioned connectors
