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Context engineering
infrastructure for enterprise AI.

Your AI agents are making decisions about consumers they cannot see. They run on settled data: transactions, CRM entries, purchase logs. But consumers are not settled. Right now, they are researching, comparing, switching, and deciding. None of that signal reaches your AI. The infrastructure to carry consumer behavior into enterprise intelligence does not exist yet.

Trusted by
Two worlds. No connection between them.
What your AI sees
Last transaction: 14 days ago
CRM status: active subscriber
Segment: mid-tier, urban, 35-44
Last support ticket: billing inquiry
What the consumer is doing
Comparing competitor pricing right now
Researching home loans in a new city
Switched grocery delivery provider this week
Reading reviews for your competitor's product
The Category
Every platform shift creates a missing
infrastructure layer. This one is called context engineering.

Turning consumer behavior into enterprise intelligence requires a new infrastructure layer. The analyst community is calling it context engineering. We are building it.

Gartner, July 2025
"Context engineering is in, and prompt engineering is out."
AI leaders must prioritize context over prompts. Build context-aware architectures, integrate dynamic data, and reimagine human-AI interfaces. This is critical for the relevance, adaptability, and lasting impact of AI.
Source: gartner.com
The Failure Pattern
Agents have access to records. They have zero access to consumer context.
Enterprises deploy agents on transaction history and CRM data. The agents respond to what happened. They cannot anticipate what is happening. The missing variable is live behavioral context from the consumer.
Gartner, September 2025
"Context engineering is becoming critical for the reliability and cost efficiency of AI agents."
Software engineering leaders should make context engineering a core discipline focused on strategic context management, token efficiency, and continuous context validation.
Source: gartner.com
Platform

The context engineering stack.

Four layers that turn live behavioral signals into structured, confidence-scored context your enterprise AI agents pull on demand.

01
Originate
Capture consumer behavior as it happens
Continuous, user-authorized behavioral capture. Not surveys. Not cookies. Not inferred from transaction logs. Real signals: what people research, compare, consume, and decide, captured at the moment of action.
Ambient behavioral capture across the user's digital life
Multi-domain: consumption, research, decisions, finance, attention
Authorized at the source by the user, not retroactively by the enterprise
Behavioral capture User-authorized
9:12 am
Compared SUV safety ratings
Research
9:34 am
Filtered school districts by commute
Decision
10:15 am
Priced term life insurance quotes
Financial
11:02 am
Browsed 4-bedroom home listings
Research
1:30 pm
Switched to organic meal delivery
Consumption
2:48 pm
Reviewed 529 plan contribution limits
Financial
23 signals captured today
02
Resolve
Transform raw signals into structured context
Behavioral signals resolved into typed namespaces with confidence scores. Entity-resolved to individuals. Not a unified profile stitched from cookie fragments. A living context graph that updates continuously as the person's behavior evolves.
Typed namespace resolution: life trajectory, consumption, decisions, finance, attention
Confidence scoring on every signal so enterprise agents know what to trust
Temporal intelligence: trajectories, not snapshots. What is changing, not just what is.
188 raw signals
browsing compare purchase search subscribe scroll revisit cancel review share save return
Resolved
Life stage
.82
Spending
.95
Financial
.68
Decisions
.90
Attention
.74
03
Activate
Deliver consumer context to enterprise AI on demand
Your enterprise AI agents query. DataHive returns structured behavioral context in real time. One API call. No pipelines. No segments. No funnels. The context your agents could never access is now one query away.
Single API endpoint returns live behavioral context for any individual
Agent-native response format for any LLM or framework
Query-time assembly from the live context graph, not pre-computed segments
Agent query
CPG Brand Agent
This user switched two product categories in three weeks. What is driving the change and what are they filtering by?
Context returned
142ms
Spending
.95
Decisions
.90
Attention
.74
Agent action
Generated shortlist filtered by ingredient sensitivity, matched to current consumption patterns.
04
Govern
Consent at the infrastructure layer
The old governance model is enterprise-imposed: the company decides what data to use. DataHive inverts this. The user decides what context is shared, with which enterprise, and under what conditions. This is what makes the pipeline legitimate, not just powerful.
User consent dashboard: full visibility into every enterprise connection and query
Signal-level authorization: per-signal, per-enterprise, per-query control
Full audit trail built for regulated industries from day one
Consent ledger
User-controlled
✓
CPG Brand Agent
3 min ago
Accessed spending, decisions
Allowed by user
✓
Wealth Advisor Agent
12 min ago
Accessed life stage, financial
Allowed by user
✕
Insurance Agent
18 min ago
Requested life stage, financial
Blocked by user
✓
Auto OEM Agent
34 min ago
Accessed attention, spending
Allowed by user
47 queries today · full audit trail
Use Cases

One consumer.
Five enterprise agents. Five outcomes.

The same behavioral context, queried by five different enterprise AI agents. Each one extracts different value from the same signal.

Life
Spend
Fin.
Dec.
Attn.
Outcome
CPG
Brand switch detected 3 weeks early
Before it ever reaches the register
Insurance
Life transition surfaces coverage gap
Agent acts before the call ever comes in
Finance
Relocation signal triggers 529 timing
Advisor agent acts weeks before the outreach
Retail
Personalization resets in days, not months
At the speed of behavior, not purchase history
AdTech
Intent signals replace cookie proxies
First-party, consent-verified at the source
From the Blog

Thinking out loud.

Where we share what we are learning about context engineering, enterprise AI, and the infrastructure between them.

Context Engineering
Why your AI agents are flying blind
Enterprise AI runs on settled data. But consumers are not settled. The gap between what your agents see and what is actually happening is where value leaks.
Read more →
Infrastructure
Context is infrastructure, not a feature
The analyst community is converging on context engineering as a discipline. We think it needs to go further: context needs its own infrastructure layer.
Read more →
Enterprise AI
One consumer, five agents, five outcomes
The same behavioral signal, queried by five different enterprise AI agents. Each one extracts different value. That is what a context layer makes possible.
Read more →
View all posts →
Founding partners · Limited spots

Your AI agents don't need more data.
They need to see the consumer.

We are building the infrastructure that carries consumer behavior into enterprise intelligence. Founding partners shape how it gets built. If your AI agents need consumer context they cannot get anywhere else, this is your seat at the table.