Give AI agents a live understanding of how your business is operating
From the last millisecond to years of history. Streambased is the operational context layer that lets agents explain what changed, why it changed, and what should happen next.
The eyes and situational awareness for every AI agent, across your entire operational history.
Knowledge
RAG · docs
What should happen?
Operational Context
Streambased
What is actually happening?
Reasoning
Streambased
What changed and why?
Actions
MCP · agent tools
Fix it.
Knowledge is the memory. Actions are the hands. Streambased is the eyes, the source of truth for what a live business is actually doing.
Agents have knowledge and hands. They're missing eyes.
Today's AI support platforms provide two capabilities. Neither understands the current operational state of a customer's system.
Today: Knowledge
AI can answer from the docs
RAG over large documentation repositories — Glean, Guru, Confluence AI, internal wikis. Great at “how does feature X work?” and “what are the expected behaviours?”
Today: Actions
AI can execute operational tasks
Agent frameworks and MCP servers restart services, roll back deployments, scale infrastructure, open Jira tickets, and update configuration.
What's missing
An understanding of operational reality
Before an agent can decide what action to take, it has to know what is actually happening right now, and how that differs from what normally happens. This capability is largely absent from today's AI stack.
Streambased provides operational reasoning over live systems, spanning from real-time events back to the beginning of retained business history.
Questions an agent can't answer today:
- ?What is happening right now?
- ?Is this normal?
- ?What changed?
- ?When did it begin?
- ?Has this happened before?
- ?Is this isolated or widespread?
- ?Which customers are affected?
- ?What is the likely root cause?
Neither observability nor BI sees the whole picture
Streambased combines both worlds into a single continuous timeline that agents can reason across.
Observability reasons over
BI reasons over
One continuous operational timeline
Real-time and historical, operational and business, in a single model an AI agent can reason over.
One continuous timeline
From the last millisecond to years of history
An agent doesn't think in terms of "the stream" and "the warehouse." It asks a question and reasons across time. Drag the handle from the live edge back through years of archive — the same query keeps working, and Streambased picks HOT, UNIFIED, or COLD automatically.
- HOT reads the live stream in milliseconds
- UNIFIED spans stream and archive in one result
- COLD reaches years into compacted history
mode = HOT
Reading the live stream
now
p50 4 ms
Auto-playing·drag, tap a marker, or use ← → to take control.
Continuous operational awareness, six ways
Instead of asking many systems independently, an agent reasons across the complete operational history of a business.
Continuous state querying
Retrieve the current operational state across every data source at once. “What is happening with customer ACME right now?”
Temporal comparison
Compare live behaviour against historical baselines. Last minute vs 30-day average, today vs same day last week, this deployment vs the last one.
Operational reasoning
Move beyond querying into explanation. The service tells an agent what changed, why, and how confident it is, not just what the raw numbers were.
Cross-domain correlation
Reason across logs, Kafka messages, customer activity, business metrics, revenue impact, and support tickets in a single line of thought.
Historical investigation
Search years of operational history. Has this happened before? Which incidents looked similar? Which deployment introduced it?
Business context
Unlike observability tools, Streambased understands customers, merchants, orders, payments, subscriptions, devices, and regions as first-class entities.
Agents reason about your business, not your schemas
Streambased exposes a semantic operational model. Pick an entity to see the attributes it carries and the questions it unlocks.
Attributes
Questions an agent can ask
- →What is ACME's revenue doing versus last quarter?
- →Which plan tier is churning fastest in the EU?
- →Show this customer's orders in the last 24 hours.
Example workflow
"Our checkout is failing."
The agent automatically investigates across every source at once, correlating live behaviour with years of history, then returns an explanation, not a wall of raw query results.
→The issue began three minutes after deployment 1843.
→Payment failures increased only for UK merchants using Gateway B.
→Overall traffic remains normal — this is isolated, not systemic.
→This matches an incident from April, resolved by rolling back a configuration change.
Recommended action
Rollback payment configuration.
Temporal intelligence
"Is this normal?" — answered against 30 days of history
A raw number means nothing on its own. Streambased compares live behaviour against its own historical baseline, so an agent knows when something has genuinely broken, when it started, and what happened immediately beforehand.
- Last minute vs 30-day average
- Today vs the same day last week
- This deployment vs the previous one
Checkout latency
last 60 min vs 30-day baseline
326 ms
+172% vs baseline
Not just "latency is 340 ms" — the agent knows it's 172% above the 30-day norm and broke three minutes after deployment 1843.
Not "what is happening?" but "what changed, and why?"
The temporal intelligence and operational reasoning that separate Streambased from observability and BI.
Continuous timeline
One operational view from milliseconds to years. No seam between the live stream and the archive.
Real-time + historical
Most systems optimise for one or the other. Streambased combines both in a single model.
Business + operational data
Observability understands infrastructure. Streambased understands customers, orders, revenue, and transactions too.
Temporal intelligence
Not just “what is happening?” but what changed, when, what happened just before, and whether it has happened before.
Operational reasoning
The product explains observations rather than returning raw query results.
AI-native interface
An MCP-compatible operational context service agents use naturally. The MCP interface is the delivery mechanism, not the product itself.
Where Streambased fits
The operational context layer that sits between knowledge retrieval and operational actions.
| RAG platforms | Observability | AI BI platforms | Streambased | |
|---|---|---|---|---|
| Documentation | — | Limited | ||
| Live operational state | Limited | — | ||
| Historical business data | — | Limited | ||
| Streaming events | — | Limited | — | |
| Cross-domain reasoning | — | Limited | Limited | |
| Temporal comparisons | Limited | Limited | Limited | |
| Business + operational context | — | Limited | Limited | |
| AI operational reasoning | Limited | Limited | — |
From investigation copilot to autonomous operations
Streambased starts by cutting investigation time from hours to minutes, and grows into the operational intelligence layer for increasingly autonomous enterprises.
Phase 1
Customer Success
Customer Operations, Support, Escalation Engineers and Solution Architects. Reduce investigation time from hours to minutes.
Phase 2
Platform & SRE
Deployment analysis, root cause investigation, service health, and cross-system correlation for SRE, DevOps and Platform teams.
Phase 3
Business Operations
Reason over business KPIs. Why did revenue fall? Which merchants are affected? Which regions changed? Which products are impacted?
Phase 4
Autonomous Operations
Once trust is established: observe, reason, recommend, get human approval, execute, and verify the outcome.
Long-term vision
"The operational context layer for enterprise AI."
As knowledge platforms become the memory of AI and agent frameworks become its hands, Streambased becomes the eyes and situational awareness, giving every AI agent a continuously updated understanding of how an organisation is operating in real time and across its entire history.
Give your AI agents operational awareness
See how Streambased gives agents a continuously updated understanding of your business, from the last millisecond to years of history.