Context Engine

Start with the person, not the request. Someone reaches out; Unpod knows who they are and what happened before; the agent picks up from there; and the outcome is remembered for next time. The Context Engine is that memory. Context is the one input a competitor cannot buy, scrape or copy - and you keep it. It comes in five tiers, from hot and fast at the top to persistent and compounding at the bottom:

Live turnThis call, right nowProduction

Session memoryActive context across the conversationProduction

Customer contextEvery touch with this personPer call: productionCross-channel: in build

Organisation knowledgeProducts, policies, FAQsProduction

Outcome historyEvery result, keptAs data: productionAs memory: in build

↑ Hot · fastPersistent · compounding ↓

Use it today

Tier	How you use it today
Live turn, session memory	Automatic: the runtime carries turn history through the conversation. A bring-your-own brain keeps its own history in its adapter.
Customer context	Pass what you know as call data (below), or as Space People columns. Turn on Enable Memory (Agent Studio → Advanced) and the agent recalls past calls with the same number - per agent, per number, voice only. Enable Memory applies to Agent Studio agents; SDK agents dialed with `client.calls.create` cannot turn on past-call memory yet (in build).
Organisation knowledge	Upload documents to a knowledge base and attach it to the agent; the agent retrieves from it mid-call.
Outcome history	Every completed call produces structured post-call data, by webhook or API. SDK agents read analytics blocks with `client.analytics.for_call(call_id)`.

call_with_context.py

import asyncio
from unpod import AsyncClient

async def main() -> None:
    async with AsyncClient() as client:
        call = await client.calls.create(
            agent_id="my-support",
            to_number="+9188473xxxxx",
            data={"name": "Priya", "plan": "Gold", "last_order": "ORD-1042"},
        )
        print(call.call_id, call.status)

asyncio.run(main())

In build

Cross-channel identity, and one timeline per person across call, WhatsApp, SMS and email.
Facts with provenance, a time-to-live and sensitivity levels, scoped per organisation, with organisation-level erase.
Outcome history read back as memory by the next interaction, on any channel.

On the spine

Context underlies the spine from entity through outcome. The Context Engine reads entity records, conversation history and structured outcomes, and the next interaction reads it before the agent speaks. Each interaction’s outcome feeds the next one.

What stays hidden

The embedding and vector implementation. Which models embed what, how the index is built and how similarity is scored are internals we tune and swap freely. You work with call data, knowledge, memory and outcomes - never with vectors.

Status

Production on voice: live turn, session memory, per-call customer context with memory of past calls on Agent Studio agents, knowledge-base retrieval, and post-call outcomes. In build: cross-channel context, the facts layer with provenance, and outcome history as memory.

Go deeper

Knowledge Base

Organisation knowledge, attached to an agent.

Post-call data

The structured outcome of every call.

People & identity

The record context attaches to.

Own your model

Where outcome history goes next.

Overview

Talk

Remember

Act

Use it today

In build

On the spine

What stays hidden

Status

Go deeper

Knowledge Base

Post-call data

People & identity

Own your model

​Use it today

​In build

​On the spine

​What stays hidden

​Status

​Go deeper

Knowledge Base

Post-call data

People & identity

Own your model

Use it today

In build

On the spine

What stays hidden

Status

Go deeper