Store preferences, infer intent, and build living customer profiles that make every interaction feel personal — powered by real conversations, not cookies.
See how conversations enable capturing of preferences — unlike traditional CRM and without forms or surveys.
higher conversion with contextual recommendations
fewer repeat-information requests
to build a rich customer profile
intent-signal accuracy after 3 interactions
User interacts with an AI agent via chat, voice, or any channel. Raw signals are captured in real time.
Structured facts are written to the user's profile with full audit trail, consent tracking, and instant retrieval.
Downstream APIs query context to rank products, tailor offers, and route deliveries for each user.
NLU models identify preferences, constraints, and implicit signals — dietary needs, brand affinity, delivery windows.
Higher conversion, fewer returns, and customers who feel understood — measurable from day one.
Context APIs adapt to your industry. Same API, domain-specific intelligence.
Resolved 8 items from order history
Matched against last 4 weekly shops
Swapped peanut butter for sunflower seed butter
Peanut allergy flagged in dietary context
Prioritised organic options where available
Organic preference detected across 12 past orders
Applied Saturday AM delivery slot
Preferred window: before 10am
Split order across Waitrose and Ocado
Best price on 3 items at Ocado, rest at preferred merchant
{ "dietary": { "allergies": [ "peanuts" ], "preferences": [ "organic" ] }, "household_size": 4, "preferred_delivery": { "day": "saturday", "window": "08:00-10:00" }, "avg_basket": "GBP 85"}Three lines of code. No schema design, no ETL. Context flows directly from conversations as typed objects.
Preferences compound across sessions in real-time. Your agents remember what matters — without asking twice.
Every signal is versioned and traceable. Users own their data with full GDPR controls out of the box.
Every context field is visible and controllable. Users can view exactly what's stored, modify preferences, or delete their profile entirely — all through a simple, self-service interface.
Every context update is logged with timestamp, source, and confidence score. Compliance teams get a complete audit trail. No black boxes.
| Timestamp | Field | Action | Conf. |
|---|---|---|---|
| 2024-12-15 09:23:41 | dietary.allergies | added: "peanuts" | 0.98 |
| 2024-12-15 09:24:02 | dietary.intolerances | added: "lactose" | 0.96 |
| 2024-12-15 09:25:18 | preferred_delivery | set: saturday / before_10am | 0.95 |
| 2024-12-15 09:26:44 | preferred_merchants | added: "waitrose" | 0.92 |
| 2024-12-15 09:27:01 | shopping_segment | set: "cross_shopper" | 0.87 |
Clean, predictable interfaces in every language. Ship your first integration in minutes.
import Hyperfold from "hyperfold";
const hf = new Hyperfold({ apiKey: "hf_live_..." });
const ctx = await hf.context.get({ user_id: "usr_abc123",});Context APIs turn transient interactions into persistent intelligence. Start capturing preferences, dietary needs, and shopping signals today — your customers will feel the difference from the very first order.