APPLICATIONS & PRODUCTION DOMAINS
Where persistent context becomes infrastructure.
Long-running AI systems need to process massive context histories while remaining responsive and cost-effective. AVIKRAT explores how compact persistent state makes continuous long-context inference practical.
Assistants that remember the session.
Enterprise copilots can operate across persistent conversations, large documents and long-running sessions. As context accumulates, maintaining the full history can become increasingly expensive.
THE OPPORTUNITY Compact persistent state could provide another way to retain useful historical information while local decoding operates over a shorter recent window.
- Long conversations
- Large documents
- Persistent sessions
More context. Less persistent baggage.
Constrained devices have limited memory and compute budgets. A smaller persistent state could be friendlier to environments where carrying a continuously growing history is difficult.
THE OPPORTUNITY AVIKRAT is exploring whether compact persistent state can make continuous context more practical where resources are tight.
Long-running agents need memory that scales differently.
Agents can operate across many steps, tools and interactions. Long-running workflows naturally accumulate context, making state management increasingly important.
THE OPPORTUNITY AVIKRAT is exploring whether compact persistent state can keep long-running histories available alongside a short active window — so agents can continue without re-reading everything.
Continuous context without continuously growing state.
Real-time systems continuously receive new information. When memory remains bounded, continuous context may become more practical to maintain.
THE OPPORTUNITY AVIKRAT is exploring whether compact state can let real-time systems keep useful history without re-processing the full stream.
The Common Thread
Four environments.
One underlying problem.
ENTERPRISE
- What grows?
- Context
- What becomes expensive?
- STATE / COMPUTE PRESSURE
- What AVIKRAT explores?
- Compact Persistent State
EDGE
- What grows?
- Context
- What becomes expensive?
- MEMORY / COMPUTE PRESSURE
- What AVIKRAT explores?
- Compact Persistent State
AGENTS
- What grows?
- Context
- What becomes expensive?
- STATE / COMPUTE PRESSURE
- What AVIKRAT explores?
- Compact Persistent State
REAL-TIME
- What grows?
- Context
- What becomes expensive?
- MEMORY / STATE PRESSURE
- What AVIKRAT explores?
- Compact Persistent State
The Architectural Shift
From growing history to persistent state.
The same system environments share the same architectural question: can the history be carried as compact state instead of growing material?
Conceptual visualization — not measured data.
Who This Is For
Built for the teams solving this problem.
- Explore the architecture
AI Infrastructure Teams
Serving long-context models at scale.
- Explore the architecture
Enterprise AI Teams
Building persistent copilots and document workflows.
- Explore the architecture
Agent Builders
Maintaining long-running context across multi-step workflows.
- Talk to AVIKRAT
Edge / Systems Teams
Working within constrained memory and compute environments.
Pilot Opportunity
Explore where bounded context could matter.
We are interested in pilot opportunities involving long-context assistants, enterprise workflows and edge inference.
Build systems that keep context without carrying all of it.
AVIKRAT is pioneering an architectural shift in long-context inference built around compact persistent hidden state and local decoding.