Foundium Intelligence. From input to direction.

The data path in full. What is collected, when it is collected, what comes back out, and who is allowed to see any of it.

Six streams in. Five surfaces out.

Two streams ask something of you. Four read what you are already doing. Everything converges on one engine.

Weekly check-ins
Session behaviour
Milestones
Documents
Venture data
Opportunity database

The engine

Morning brief
Live chat
Opportunity feed
Risk alerts
Adviser signals

Intake.

Each stream carries a cadence and a mode. Active streams need something from you. Passive streams do not.

Stream 01

Weekly check-ins

Wellbeing, wins, blockers and focus, in your own words. The language you use is read for patterns over time.

Cadence: weekly · Mode: active · Input: free text

Stream 02

Session behaviour

Login timing, session length, day-of-week patterns and time on task. Nothing to fill in and nothing to remember.

Cadence: continuous · Mode: passive · Input: event log

Stream 03

Milestone tracking

What gets completed and what gets deferred. Which tasks you avoid is one of the highest signal inputs there is.

Cadence: on event · Mode: passive · Input: state change

Stream 04

Document upload

Term sheets, contracts, decks and financials, scanned on upload against known risk patterns in the clause library.

Cadence: on upload · Mode: active · Scan: clause library

Stream 05

Venture data

Stage, market, co-founders, funding history and team structure, updated as you add it rather than at set points.

Cadence: on update · Mode: active · Input: structured

Stream 06

Opportunity database

Grants, funds, accelerators and partnerships tracked with their calendars, eligibility criteria and stated thesis.

Cadence: continuous · Mode: passive · Source: external

Output.

Five surfaces carry the same output in different shapes. Which one it reaches depends on whether you asked for it and what priority it carries.

Output 01

Morning brief

Priority actions, risk flags and opportunity alerts, written against your venture rather than a template.

Trigger: scheduled · Delivery: 08:00 daily

Output 02

Live chat

Ask anything. It answers with your full venture history in view and remembers every prior conversation.

Trigger: on request · Delivery: real time

Output 03

Opportunity feed

Grants, investors, accelerators and co-founders ranked by fit and released when you are positioned to convert them.

Trigger: on match · Release: readiness gated

Output 04

Risk alerts

Flags from your documents surfaced across the dashboard, chat and brief at once. Critical alerts skip the queue.

Trigger: on detection · Priority: critical first

Output 05

Adviser and institution signals

Momentum trends, risk flags and matches sent only to the dashboards you have approved, at the level you set.

Trigger: on event · Access: consent controlled

Calibrationover time.

Confidence in the read, measured against the profile it eventually settles on. Day one is generic. Month four is reliable. There is no shortcut to four months of your own data.

Day 120%Calibration begins from what you entered at sign-up
Month 144%First patterns confirmed across a handful of check-ins
Month 472%Reliable operational read on your patterns and pace
Year 189%Predicts your working horizon eight to twelve weeks out
Year 297%A profile specific to you and rebuildable only over time

What it runs on.

Hosted in London, on UK infrastructure, with every sub-processor named.

01

Ingestion

  • Real-time event streaming from platform activity
  • Language analysis of check-in text
  • Document parsing for term sheets, contracts and decks
  • External feeds for grant calendars and opportunity sources
  • Webhooks for milestone and calendar events

02

Engine

  • Behavioural pattern model for rhythm and cadence
  • Context graph holding venture relationships
  • Trajectory forecasting from your own history
  • Risk clause library covering 500+ patterns
  • Multi-dimensional matching for opportunities

03

Models

  • Language model for chat and brief generation
  • Proprietary behavioural model, not language based
  • Embedding models for semantic similarity
  • Time-series models for forecasting
  • Classification models for document risk detection

04

Infrastructure

  • AWS London region, UK data residency
  • PostgreSQL for founder and venture data
  • Vector database for semantic and behavioural embeddings
  • Redis for session state and alert queuing
  • AES-256 at rest, TLS 1.3 in transit

It starts learning on day one.

Every stream is active from the first session. Nothing waits for a setup period to finish.