Applied data science for agent systems

The hard part of agents isn't the model.

It's the dependencies, the context and the people. NotedML builds the data layer underneath agent systems that have to hold up in production, not just in a demo.

See the products Book a working session
Planner / Orchestration Transcriptor / Ambient capture Dash / Shared surface Ranker / Personalisation
PEOPLE + ORGS CONSTRAINTS AGENTS COMMITMENTS
Method

Models are a commodity. The data work around them isn't.

Every agent project we've seen fail, failed on the same three things: nobody modelled the dependencies, nobody measured the output, and nobody could tell you why the system did what it did. We start there.

01 / Model

Map the dependency graph

Before an agent touches anything, we model what depends on what: people, systems, approvals, calendars, records. The graph is the product.

02 / Measure

Evaluate before you ship

Test sets, scored runs and regression gates on every prompt and tool change. If accuracy drops, the build fails, the same way code does.

03 / Monitor

Watch it in production

Traces, drift detection and human review queues wired in from day one. You can always answer why the system reached a given decision.

Products

Four modules. One data layer.

Each runs on its own. Together they share the same dependency graph, the same evaluation harness and the same audit trail.

Planner
Orchestration

Calendars that understand consequences.

Scheduling across organisations, not just across inboxes.

Most calendar tools treat a date as a free slot. Planner treats it as a node with upstream approvals and downstream commitments. Move a milestone and it tells you exactly which teams, vendors and people break, and what the nearest feasible window is.

  • Dependency-aware rescheduling across org boundaries
  • Constraint solving with human override at every step
  • Agents and people booked on the same timeline
  • Change impact shown before the change is committed
IMPACT VIEW MOVED DATE
Transcriptor
Ambient capture

The room, on the record.

Ambient AI for clinical rooms and client sessions.

Transcriptor listens, separates who said what, and turns the conversation into structured notes, coded records and follow-up actions. Nobody types during the session. The clinician or consultant reviews and signs off afterwards, with the audio and the reasoning attached to every line.

  • Speaker separation and diarisation in noisy rooms
  • Clinical note structuring against your own templates
  • Action and commitment extraction for consulting engagements
  • Audio and transcripts stay inside your tenancy
CAPTURE → STRUCTURE SUBJECTIVE OBJECTIVE PLAN ACTIONS SIGNED OFF BY CLINICIAN
Dash
Shared surface

Dashboards agents can read and write.

Built for a workforce that's part human, part agent.

A dashboard used to be a place people looked. Now half the readers are agents, and some of them need to write back. Every tile in Dash carries an owner, a lineage and an API, so an agent can query a number, post a revision, and leave a note your team can argue with.

  • Humans and agents editing the same board, with attribution
  • Lineage on every metric, back to the source table
  • Agent-callable read and write endpoints per tile
  • Comment threads that agents participate in, not just trigger
TILES + LINEAGE AGENT WRITE-BACK
Ranker
Personalisation

Recommendations that know why, not just what.

A hybrid LLM and knowledge graph engine for hyper-personalised ranking.

Ranker builds and caches a knowledge graph over your products or items — attributes, relationships, and the patterns in how people actually choose between them — then uses an LLM to reason over that graph per user, per context. The result reads less like a similarity score and more like a recommendation a good travel agent or shop assistant would make. It's running today in travel recommenders and ecommerce catalogues.

  • Knowledge graph built and cached per catalogue, not recomputed per request
  • Hybrid retrieval: graph traversal plus LLM reasoning over context
  • Personalisation from session, history and stated intent, not just past clicks
  • Deployed in travel recommenders and ecommerce ranking today
GRAPH → RANK ITEM GRAPH RANKED PER USER
Founder

Rob Cooper

Founder, NotedML

Rob has spent the last four years putting multi-agent systems into production for organisations that can't afford them to misbehave, across travel, retail, sport and resources. NotedML is what came out of that work. The patterns kept repeating, so he built the modules.

  • Led data science for a global travel group's agent systems, building the orchestration layer behind its customer-facing AI.
  • Built one of Australia's first production e-commerce agent systems for a national retailer, delivered with a major cloud partner and named a finalist in a national AI awards program.
  • Designed a multi-agent live-insights system for a national sporting body, turning match data into real-time commentary for fans.
  • Earlier AI architecture and data platform work spanning tourism, mining, education and banking.
Brisbane, Australia
Contact

Tell us what you're trying to coordinate. We'll tell you if it's a data problem or a model problem.

Email rob@notedml.com