Tvara Research

Research for the next layer of business AI.

A dedicated space for the papers, frameworks and technical notes behind Tvara’s thinking — from context engineering and sales intelligence to AI-led business workflows.

Recent publication

The Context Layer

Source-Aware Compression for Stateless LLM Inference in Business Workflows

Published

20 June 2026

Topic

LLM Context Layer

Read the paper that defines Tvara’s view of the Context Layer.

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Research paper 20 June 2026

The Context Layer

Source-Aware Compression for Stateless LLM Inference in Business Workflows

Author

Shishir Govinda M

Co-Founder & CTO, Tvara

Abstract

Large Language Models are increasingly used as reasoning engines inside business workflows, but every model call is still stateless at the model level. Enterprise context is scattered across emails, calls, CRM records, documents, transactions, support tickets and internal notes. This paper introduces the Context Layer as the system layer between raw business data and stateless LLM inference. It retrieves, classifies, compresses, structures, ranks and assembles task-specific context for each model call. The core argument is that enterprise AI performance depends not only on model capability or context-window length, but on the density, structure, provenance, recency, persistence and relevance of the context supplied during inference. Instead of maximizing how much a model can read, business AI systems should optimize what the model needs to know, in what structure, from which source and under what operational constraints.

Context LayerContext EngineeringStateless InferenceContext DensitySource-Aware ContextEnterprise AI

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Research direction

The library will grow around systems thinking for enterprise AI.

Each publication should make Tvara feel less like another SaaS dashboard and more like a research-backed AI infrastructure company.

Context architecture

How business memory is retrieved, ranked, compressed and supplied to AI systems.

Enterprise workflows

How AI should reason across CRM, communication, sales and operational systems.

Source-aware AI

How provenance, recency, permissions and reliability shape enterprise AI outputs.

Coming next

A research page that can scale into a full publication archive.

Add new papers to the array at the top of the component and reuse the same paper card pattern for future releases.

Context cards for sales workflows

Reserved for upcoming Tvara Research publications, technical essays and white papers.

Lead intelligence and transaction-level reasoning

Reserved for upcoming Tvara Research publications, technical essays and white papers.

Evaluation frameworks for AI-led business actions

Reserved for upcoming Tvara Research publications, technical essays and white papers.