Tab autocomplete for agentic work.

Prescience learns the intuition behind your customers' decisions from the events they already generate — so your agents stop asking and start acting.

Construction agent
An example of a customer's agent — a construction coordination agent — before and after Prescience.
Before · 11 turns 26 min
Tue 7:14 AMJobsite 4120 — rain call
With Prescience 1 turn · 54 sec
Tue 7:14 AMSame event — Prescience predicts
How it works

Four layers. One prediction loop.

Every action your users take becomes training signal for your agents. No skill files, no workflow engineering, no FDE rollouts per customer — just a prediction layer underneath the agent you already built.

Layer 04 · Predict & Actoutput

Your agent knows what to do.

Predicted next action
Pull Jennifer from float pool (ICU, <40 hrs)
Bump 2 non-critical rounds to tomorrow
Notify charge nurse & patients
0.94
Layer 03 · Learnmodel

Causality, not retrieval.

Layer 02 · Unifystream

Every source, one stream.

07:14fieldsite_closed
07:15sub_sms×5 replies
07:22deliveryHarris ETA 10:00
07:30human→ reschedule Thu
07:34human→ draft RFI #47
09:00ownerprogress mtg set
11:05weatherradar clear 14h
Layer 01 · Ingestsources

Your customers' data — already flowing.

SlackProcoreWeatherSub SMSRFI logPhotosScheduleEmailDeliverySafetyTicketsProduct DB
The Prescience stack

Scroll to walk through each layer.

01 / 04 keep scrolling
The platform

Built for agent-for-X companies who want their agents to feel like coworkers.

Four capabilities you don't want to build yourself — because every agent-for-X company would be building them in parallel.

01 · Stream SDK

Connect any source.

Slack, email, logs, your product DB, MCP for custom. One connector per source, zero schema work.

slack
email
logs
stripe
db
mcp
sms
gh
→ unified event stream
02 · Continuous-time transformer

Built for events, not tokens.

Time-aware, multimodal, irregularly sampled. The architecture your agent needed all along.

e₁ e₂ eₙ a₁ aₖ
03 · Per-customer weights

A model for every tenant.

One SaaS for you. One bespoke model per customer — isolated, improving, un-exportable.

PRS tenant A tenant B tenant C Mₐ M_b M_c
04 · Prediction endpoint

One call. Confidence returned.

Drop it in front of your agent loop. Get the next action, confidence score, and causal trace back.

pull Jennifer0.94
bump rounds0.91
notify charge0.87
POST /v1/predict · 38ms
For teams building agent-for-X

Your agents, with instincts.

Prescience is in early access with design partners across healthcare, construction, insurance, and customer support. Want to see how much better your agent could be with a prediction engine underneath?

Book a demo