Data Analytics & AI Company System
Data teams building models and dashboards for enterprises.
The data project from exploring and preparing the client's data to building, evaluating, deploying, and monitoring the model.
No credit card required — your project is set up with the full system in under a minute
This is the system board as you'll receive it: workflow columns and starter cards showing the first steps
How does work flow in this system?
Every project starts as a card in “Exploration”, where the data scientists understand the client's sources, their quality, and what can actually be built on them — no promises before this stage. Then “Data Preparation” to clean and unify the sources and build the training sets — usually the longest and least glamorous stage. Then “Model” to build the model or dashboard and trial it, then “Evaluation” with documented accuracy metrics the client agrees to before deployment, then “Deployment” to the production environment, and finally continuous “Monitoring” for model drift and accuracy decay as the data changes.
The Forum documents model decisions: why did we choose this algorithm? Which data did we train on? What confidence limits were adopted? — questions the client will ask six months later, and the answers must be documented. Dues manages project payments by stage and the dues of specialized freelancers brought in at peak times. Chat is for daily coordination, and Announcements is for what concerns all projects: new data-privacy policies or an upgrade to the shared work environment.
The data lead owns the client relationship and their expectations and agrees the “Evaluation” metrics with them before deployment; data scientists execute exploration, preparation, and model building and document their decisions in the Forum; and application engineers run “Deployment” and “Monitoring” and build the interfaces and alerts. Handover between stages happens on the card: whoever takes over finds the data, decisions, and measurements attached — there is no verbal handover in data science.
Who does what?
The operational roles in this system and each role's responsibility in daily work — assign them to your team as-is or adapt them to your reality.
Data Lead
Owns the client relationship and their expectations: approves “Evaluation” metrics before deployment, settles model decisions documented in the Forum, and reviews the quality of whatever ships in the company's name.
Data Scientists
Execute “Exploration”, “Data Preparation”, and “Model”: assess source quality, build the models, and document assumptions and confidence limits on the card.
Application Engineers
Run “Deployment” and “Monitoring”: connect models to the client's systems, build drift alerts, and address performance decay before the user notices it.
Projects Coordinator
Tracks stage deadlines and their payments in Dues, coordinates acceptance meetings with the client, and moves cards between columns when their conditions are met.
What's prepared for you from day one?
System units
- Tasks — A kanban board with workflow columns and execution cards
- Chat — The team's fast daily coordination channel
- Forum — Documented discussions in organized sections — decisions and knowledge that never get lost
- Announcements — The official voice of management — circulars and alerts that reach everyone
- Dues — Internal money with strict privacy — dues, advances, and expenses
Forum sections (4)
- Model DecisionsDocumenting algorithm choices, training data, and adopted confidence limits for every deployed model — the reference for any later client question.
- Data QualitySource problems discovered in projects and how they were treated — lessons that save weeks on upcoming projects.
- Data Privacy & GovernancePolicies for handling sensitive client data: permissions, what may be moved, and what is forbidden.
- Tools & TechniquesEvaluating new AI libraries and tools and the team's experience with them before adopting them in client projects.
«Working Rules for This System» — Pinned in the forum
1) No promise to the client before “Exploration”: data quality decides what can be built. 2) Every model is documented in the “Model Decisions” section: the algorithm, the training data, and the confidence limits. 3) No deployment before the client approves the “Evaluation” metrics in writing on the card. 4) Client data never leaves the approved work environments — copying to personal devices is forbidden. 5) Every deployed model has a “Monitoring” card with defined drift metrics and active alerts. 6) Project and freelancer payments are posted in Dues against their agreed stages.
Welcome announcement: «Welcome to the Data System»
From today, every project passes as a card from “Exploration” to “Monitoring”, model decisions are documented in the Forum with their reasons and limits, and nothing deploys before the client agrees the “Evaluation” metrics in writing. First step: register ongoing projects and document the deployed models' decisions. Data-privacy policies are published here and bind everyone.
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