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Trusted by teams at
Qualcomm logoFPL logoStripe logoPalantir logoItaú logo

A unified foundation for robust, self-improving agents.
The best way to get AI into production.

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Case study

Qualcomm goes from 23% to 98% AI support accuracy with Context

Modules for every role, tool, and use case.

Workspace

WorkspaceDrafting weekly customer health review

Reviewing 12 strategic accounts to flag ARR at risk this quarter.

Pulled usage and tickets
Flagged 2 at-risk accounts
Drafted summary memo
Plain-English workflows anyone can author
Same surface for your team and your agents
Full audit on every run

Engine

S
sarah.chen@firm.com
via Okta
your VPC · us-east-1
+792 more
Identity inherited from your IdP at every action
800+ connectors to the tools your team uses
Hosted, in your VPC, on-prem, or air-gapped

Unify

.context graph
approver: $5M+ deals
exception: 3+ tickets
tier-1: Slack first
cite primary sources
red flag: 20% usage drop
tone: concise voice
format: chart + bullets
+ captured 2s ago
Your firm's knowledge available to every agent
Agents capture what they learn for the next run
Permissions follow each user's grants

Evals

Rubric pass rate
23% →94%
raw+docs+runbooks+rubrics
Rubrics ensure agent quality at every run
Accepted outputs become reusable standards
Routing improves automatically as the library grows

All in one unified platform.

Fig. 2Context Workspace
Momentum factor decay · overnight

  • Momentum factor underperformed the 7-day rolling avg by 23 bps last night. Pull the feature pipeline + check vendor feed health.

  • On it. Diffing the feature graph against the last successful EOD run and pinging feed-health for each upstream vendor.

    Root cause: FactSet schema drift on fundamentals.fcf_yield. Pipeline imputed NaN, the momentum signal got noisier, and attribution moved 23 bps. Wrote the postmortem.

    Pin the schema check?

    Adds a type-equality assertion on fundamentals.fcf_yield and notifies #trading-ops on the next drift.

  • Pin it. And add a watch rule so we catch the next one before EOD.

  • Pinned. Schema check is live; vendor-schema-drift-watch is armed.

ChatResize to read

Product updates and engineering notes

Built for production work.

The Context on-prem appliance with the Qualcomm AI 100 Ultra accelerator visible inside.

Run anywhere.

Hosted. Your VPC. Air-gapped. The on-prem Context appliance.

acme-q4-diligence
Acme · Q4 review
Draft the diligence memo for Acme — focus on Q4 risks and growth signals.
Pulling Acme's Q4 financials, support tickets, and customer calls.
acme-q4-financials.csv+247 rows
Drafting risk signals and growth opportunities from the calls.
diligence-memo.docx+89 lines
Done — 3 risk signals, 2 growth opportunities flagged.
Ask anything (⌘L)
Research
Models
Claude 4.5 Sonnet
GPT-5
Gemini 2.5 Pro
Kimi K2
Llama 4 (custom)

Use any model or agent.

Claude, GPT, Gemini, Kimi, or open weights. Bring your own agent framework, or use ours.

Enterprise-grade authorization.

Identity through your IdP. Customer-managed keys. Audit on every action. Permissions inherited at every connector call.

Audit loglive
S
sarah.chenSnowflake
select · 47 tables in finance.sales
09:42
M
marcus.leeGoogle Drive
edit · Q4-memo.docx
09:38
P
priya.shahJira
comment · ENG-4421
09:36
A
ana.martinezSlack
post · #risk-review
09:34
acme-q4-diligence
diligence-memo.docx
Acme Q4 Diligence
Summary
Acme closed Q4 above plan on revenue, with margin compression from a one-time integration spend. Pipeline coverage for Q1 is healthy at 3.1x.
Risk signals
•Top-5 customer concentration up to 41%.
•Churn in mid-market segment ticked to 4.8%.
•DSO extended by 6 days versus Q3.
acme-q4-financials.xlsx
A
B
C
1
Metric
Q3
Q4
2
Revenue
$1.04M
$1.23M
3
OpEx
$0.71M
$0.88M
4
Margin
31.7%
28.5%
5
Pipeline
$3.1M
$3.9M
6
Churn
3.2%
4.8%
7
NPS
47
52

A complete working environment.

Documents, spreadsheets, decks, kanbans, and file viewers built in. Your team and agents work on the same files in the same environment.

Faster, cheaper, better

Self-improving models, agents, and skills deliver better outcomes at scale.
Task completion on your team's rubric
Context94%
Claude Cowork62%
OpenAI Codex57%
Devin49%
From internal benchmarks on specialized enterprise workflows.
40
×
Faster turnaround
28
×
Lower cost per case

Custom models trained on your work

Your team's accepted outputs become training data for models you own and serve, and they beat general-purpose agents on your specific tasks.

Evals gate every change

Rubrics and golden sets validate every runbook, model, and context change against past work before it ships. Regressions are caught automatically.

Step-level model routing

Each step routes to the cheapest model that clears your rubric. Frontier models handle only the genuinely novel, so cost falls without losing quality.

Continuously improve with every layer of context your team adds.

$20094%task-completion accuracy
23%$7compute cost per case
Raw agent
Off the shelf
A frontier model with no context. Where it stays, frozen at deployment.
+ Domain documents
The “what”
Facts, files, and relationships. Necessary, not sufficient.
+ Plain-English runbooks
The “how”
Procedures, approvals, and the order work actually happens in.
+ Rubrics and golden sets
Your standard
Your team's definition of good, captured and held to.
100 production workflows at Qualcomm. Same base model in every stage.

An agent platform you control

Codex and Cowork route every task to one lab's frontier models, in the lab's cloud. Vertical tools lock you to a single interface. Context is the execution layer you run: on your compute, across any model, with the learning loop staying yours.

Context compared with Codex, Cowork, and vertical AI tools
Enterprise permissioning
Context
Agents inherit each user's permissions from your IdP. Every action is authorized before it touches data.
Codex
Scoped to a provider account
Cowork
Scoped to a provider account
Vertical tools
Per-app roles on one surface
Model choice
Context
Any model. Claude, GPT, Gemini, Kimi, or open weights, whichever wins the task.
Codex
OpenAI models only
Cowork
Anthropic models only
Vertical tools
The vendor's fixed stack
Cost over time
Context
Accepted work distills into cheaper models you own. Metered in CCUs, decoupled from any model vendor.
Codex
Frontier inference, cost scales with use
Cowork
Frontier inference, cost scales with use
Vertical tools
Flat per seat or per query
Deployment
Context
Your VPC, on-prem appliance, or air-gapped. Compute and identity stay on your side.
Codex
Provider's multi-tenant cloud
Cowork
Provider's multi-tenant cloud
Vertical tools
Multi-tenant SaaS
Agent choice
Context
Run any agent or framework on the platform, not a single vendor's.
Codex
One vendor agent
Cowork
One vendor agent
Vertical tools
A closed, fixed workflow
Internal systems and retrieval
Context
800+ permissioned connectors and an institutional-context engine grounding every run.
Codex
A handful of connectors
Cowork
A handful of connectors
Vertical tools
Source connectors for one domain
Humans and agents together
Context
People and agents share one environment, filesystem, and deliverables.
Codex
A solo coding agent
Cowork
An assistant surface
Vertical tools
Single-user chat
Evaluation suite
Context
Rubrics, golden sets, and dashboards built in. Every run scored automatically.
Codex
None built in
Cowork
None built in
Vertical tools
None built in
Data ownership
Context
Traces, rubrics, and tuned models stay in your perimeter. You own them.
Codex
Flows to the model provider
Cowork
Flows to the model provider
Vertical tools
Locked inside the vendor

Models, frameworks, and vendors change. The platform you run, and the data your team generates inside it, stays with you.

A platform for every industry.

Prebuilt agents and use cases for the work your industry runs.

Connects to the tools you already use

Connect a source once. Agents read, work, and write back under the access rules you set.

800+ connectors across data warehouses, documents, CRMs, ticketing, and internal systems.

Browse all connectors →

Data and warehouses

SnowflakeDatabricksBigQueryPostgresLooker

Documents and knowledge

BoxNotionConfluenceGoogle Drive

CRM and support

SalesforceHubSpotZendeskServiceNow

Engineering and communication

JiraGitHubSlackMS Teams

and hundreds more, across 18 categories

Some of the agents built on these connections

Due diligenceReads the full data room and extracts what mattersBoxSnowflakeNotionChip designTracks RTL-to-GDSII progress across blocks and flags critical-path slipsGitHubJiraSlackWorkflow automationAutomates handoffs across teams and toolsServiceNowSlackAsana
See all 69 agents →

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