Stateful AI at fortune-500 scale.Secured end-to-end.
Context pairs a three-tier memory stack with enterprise-grade controls, delivering frontier-model reasoning over tens of millions of tokens without compromising data sovereignty.
A deep-dive for CISOs, enterprise architects, and staff engineers. All claims link to primary evidence in our Trust Center.
Architecture at a Glance.
Practical ceiling today: ≈ 50M tokens—a cost-latency sweet spot. Scale is unbounded as hardware pricing falls.
Hot
Capacity
≤ 64k tokens
Purpose
Real-time chat & task context.
Key tech
Mix of frontier models
Persistent
Capacity
~ 50 M tokens
Purpose
Stable, large-scale reasoning.
Key tech
Triplet-creation model builds a graph of thoughts—logical relations & intermediate inferences healed continuously.
Cold
Capacity
Unlimited
Purpose
Long-term archive.
Key tech
Traditional RAG over encrypted object store.
Stateful Compute Paradigm.
Inspired by the shift from raw
inference → test-time compute.
01
Structured Thoughts
We store structured “thoughts,” not raw vectors. Each thought is a reusable node in a knowledge graph.
02
Query-Time Reasoning.
At query time, a frontier LLM re-enters this graph, traversing relationships exactly as a pseudo-world model would—eliminating redundant computation and cognitive drift.
03
Linear Scaling
Swarm agents crawl, reflect, and rank results with linear cost scaling, so accuracy persists even at extreme context sizes.
Why it matters?
Context Engine solves critical limitations of traditional AI systems.
Context Size
Robust reasoning at 50M+ tokens with no quality cliff.
Legacy bottleneck
Short context windows (8k-32k)
Performance
Linear crawl; P95 latency < 2s on 10TB store.
Legacy bottleneck
Latency spikes with corpus size
Semantic Understanding
Graph-of-thoughts captures causality, chronology, and semantic drift.
Legacy bottleneck
Vector-only RAG misses nuance
Performance at enterprise scale.
Built for search, recommender systems, and autonomous agents. Our graph-of-thoughts approach delivers unmatched accuracy.
100%
80%
60%
40%
20%
0%
Context
90.5%
+24% improvement
Context
66.5%
HELMET 128k Benchmark
Performance comparison on long-context reasoning tasks (higher is better).
LOFT 1M Benchmark
Performance on million-token reasoning tasks (higher is better).
GraphRAG Precision
Factual precision on 2TB enterprise corpus (relative comparison).
How Context differ from Microsoft, Google & Notion.
We care about deeper precision, zero tool-switching, and faster adoption.
| Context (AI-native) | Legal AI suites | |
|---|---|---|
| Scale | Graph-of-thoughts → stable at 50 M+ practical tokens. | Reliant on 128 k/1 M window limits. |
| Architecture | Built ground-up around a memory engine & autonomous agents. | 40-year-old file formats with chatbot overlay. |
| Customization | Forward-deployed engineers codify your exact workflows & templates. | Limited to prompt templates. |
| Workflow | Single workspace; agents orchestrate end-to-end deliverables. | User must hop between Docs, Sheets, Slides. |
Security & Compliance.
We care about deeper precision, zero tool-switching, and faster adoption.
Compliance
SOC-2 Type II (Apr 2025); ISO 27001 in flight.
Encryption
AES-256 at rest, TLS 1.3 in transit, envelope-encrypted secrets.
Residency
US, EU, APAC selectable; single-tenant VPC or on-prem.
Access
SAML/SCIM inheritance; field-level ACL mirrored in memory graph.
Audit
Immutable agent-action logs with tamper-proof hash chain.
Privacy
Zero data used for training unless you opt-in under DPA.
Integration & Extensibility
Pre-built connectors.
GraphQL API & web-hooks
for custom data lakes
and integrations.
Autopilot SDK.
SDK lets Autopilot invoke internal micro-services
under your IAM.
API Access.
GraphQL API & web-hooks
for custom data lakes
and integrations.
Trust Center and Next Steps.
Trust center
AES-256 at rest, TLS 1.3 in transit, envelope-encrypted secrets.
Patch SLA
Zero-day patch SLA: < 24h critical, < 72h high.
Red-team Program
Invitation-based with cash bounties.
Comprehensive Question and Answers.
Does Context identify key clauses in contracts?
Yes, AI clause detection flags obligations, renewal dates, and risky terms for quick review.
Can Context track regulatory changes that affect our policies?
Absolutely. It monitors trusted sources and alerts you when new rules align with your stored documents.
Is the data inside Context auditable?
Absolutely. Yes, every edit and view is logged, giving you a full chain of custody for compliance audits.
How secure is privileged or client-confidential information?
All data is encrypted, access is role-based, and Context supports on-prem or dedicated cloud deployments for extra control.
Does Context identify key clauses in contracts?
Yes, AI clause detection flags obligations, renewal dates, and risky terms for quick review.
Can Context track regulatory changes that affect our policies?
Absolutely. It monitors trusted sources and alerts you when new rules align with your stored documents.
Is the data inside Context auditable?
Absolutely. Yes, every edit and view is logged, giving you a full chain of custody for compliance audits.
How secure is privileged or client-confidential information?
All data is encrypted, access is role-based, and Context supports on-prem or dedicated cloud deployments for extra control.
Control every obligation at scale.
Centralize contracts, policies, and audit trails under robust permissions to reduce risk and response time.
