FAQ

Context is the execution layer for enterprise AI. Four products work as one closed system: Workspace, where your team and agents complete tasks side by side; Engine, the runtime that gives agents identity, tools, and compute inside your perimeter; Unify, the institutional-context layer agents draw on; and Evals, the system that turns accepted work and corrections into measurable improvement.

Each product is useful alone. Together, every completed task makes the next one more accurate and cheaper to serve.

Copilots assist one person inside one app, and the learning evaporates when the session ends. Context captures the workflows that run your business and the expert judgment behind them, then puts agents to work on them end to end — with humans steering and approving. Every action becomes a trace your firm keeps and learns from.

Vertical tools lock to a single interface — a legal chatbot, a finance matrix. Real work doesn't live in one interface, and the moment it leaves the product, the record breaks. Context follows the work itself: any team, any workflow, one system of record that compounds across all of them.

A runbook defines a task in plain English: the steps, the systems involved, and what good looks like. Operators, engineers, and compliance read the same document, and agents execute from it directly — no prompt engineering, no translation layer.

Hosted by us, in your VPC, on-prem, or fully air-gapped. In self-hosted deployments, identity and compute stay on your side of the perimeter — agents do the work where the data already lives, and nothing sensitive leaves your authority.

Through Engine. Each agent runs in its own sandboxed environment — a full Linux, Windows, or macOS workstation — inherits scoped identity from your IdP, and reaches your tools through 800+ permissioned connectors. Every action is authorized before it touches data, tools, or settings.

No. Agents hold zero standing credentials. Access is brokered per action through the connector layer and scoped by the same roles and policies that govern your team, so revoking access is as simple as updating a policy in your IdP.

No. Your traces, corrections, and institutional context belong to your firm. They improve your deployment — retrieval, routing, rubrics — and never leave your control or feed anyone else's models.

Every agent action produces a structured trace: what was done, under which authorization, with what result. Reviewers can replay a run step by step. The same records that satisfy audit are the raw material the platform learns from.

Through Unify. Institutional context accumulates in a traversable filesystem agents navigate like a code repo, with a .context/ directory at every level where agents write what they learn during execution. Between tasks, sleep-time distillation turns raw traces into structured context, so the next run starts further ahead.

Evals scores every run against rubrics your own experts author. Accepted work becomes golden sets; corrections expose failure modes; rubrics drive routing and model improvement. Quality is measured against your definition of good — not a public benchmark.

The best model for each task. Context is model-agnostic and routes per task. Because your context and evals live in the platform rather than in any one model, you can swap models the day a better one ships — and keep all the learning.

Most teams run their first production workflow within a few weeks: pick one process, connect the systems it touches, and have your experts review early runs. From there, each completed task compounds — better context produces better execution, and better execution produces richer context.

Teams whose work is high-volume, document-heavy, and judgment-dependent: financial services, legal, insurance operations, consulting, telecom, the public sector, and more. If the work runs on procedures plus expertise, Context can execute it.

A platform subscription plus usage that scales with completed work, so cost tracks value delivered. Talk to our team for a quote shaped to your deployment model.

Request a guided demo and we'll run Context on one of your real workflows — your tools, your documents, your definition of done.