Operating model
ARiDA keeps planning, execution, files, collaboration, memory, and governance in one system so complex work does not fragment across prompts, tabs, and exports.
How the work starts
Typical chat assistant
You keep decomposing the task prompt by prompt and manually steering every next step.
ARiDA
ARiDA turns one brief into a task graph, assigns the right lanes, and keeps the work moving after the first request.
How execution happens
Typical chat assistant
Research runs serially in one thread, so every dependency waits on you.
ARiDA
Background specialists run in waves, in parallel where possible, and unblock later work automatically.
What survives after the answer
Typical chat assistant
Important findings disappear into chat scrollback or disconnected exports.
ARiDA
Briefs, tables, findings files, archive artifacts, and project context remain durable after the run.
How teams collaborate
Typical chat assistant
Handoffs happen through copy-paste, emailed documents, or duplicated threads.
ARiDA
Shared chats, shared projects, borrowed files, and role-aware access keep teams on one evidence base.
What happens when no one is online
Typical chat assistant
The work stops when the user leaves the chat.
ARiDA
Workflow jobs can run now, later, or on a recurring cadence without a live user turn.
Governance and provenance
Typical chat assistant
Output quality depends on whatever generic response the model produces in the moment.
ARiDA
ARiDA is built for source-linked outputs, governed specialist paths, privacy-aware runtime operations, and durable files.
Execution flow
The strongest ARiDA runs begin with a good brief, then move into structured execution with inspectable files, recoverable context, and a clean path to handoff.
Example kickoff
Prepare a prospect-ready brief for an upcoming BD meeting. Use our internal materials, verify the latest public company signals, cross-check trial and IP movement where it matters, and leave a reusable markdown brief plus supporting files in the project.
State the brief, the evidence surface, the recency requirements, and the output that should remain after the run.
The main agent scopes the job, decides which lanes can run in parallel, and routes work to built-in specialists or dynamic teams.
Hot files stay visible, colder artifacts move to archive, and background outputs can be hydrated back into the workspace when they are needed again.
Outputs can stay in one shared chat, move through a project, or continue later through a workflow job on its own cadence.
Persistent platform capabilities
ARiDA is designed for durable project state: the graph, the files, the team, the schedule, and the context stay intact instead of vanishing when the last response scrolls off screen.
The main agent plans the graph, launches waves, and reconciles dependencies instead of waiting for you to micromanage prompt-to-prompt execution.
Use the fixed specialist roster when it fits, or let ARiDA create a custom role with task-specific instructions inside the same workflow.
Owners, editors, and viewers can work from one shared surface, borrow files across chats, and keep review tied to the same evidence base.
A workflow can run immediately, later, or on a recurring schedule through the same background execution system.
Hot files stay active, archived artifacts remain durable in storage, and cold outputs can be rehydrated into the workspace when they are needed again.
ARiDA can preserve identity, behavioral rules, domain knowledge, and lessons so the system can adapt without depending on one ever-growing transcript.
Use cases
The platform is strongest when the job spans evidence gathering, verification, synthesis, files, and internal handoff. That is exactly where chat-only tools tend to break down.
Join AACT, ChEMBL, and OpenTargets evidence to map a target space, key compounds, trial activity, and sponsor distribution in one structured landscape.
Typical output
Target-compound-trial matrix, sponsor map, evidence tables, and a decision-ready landscape brief.
Often involves
SQL Expert, Clinical Trials API, Python Sandbox, and Writer.
Map competitors, patent families, assignee concentration, and public company movement around a target, modality, or indication.
Typical output
Competitor map, IP crowding summary, whitespace notes, and reusable evidence files.
Often involves
Patent Research, Web Search, Clinical Trials API, and Writer.
Identify the most active investigators, institutions, and grant patterns in an emerging disease or modality area before outreach or strategy work starts.
Typical output
KOL shortlist, funding trend summary, institution ranking, and investigator network notes.
Often involves
Grant Research, Literature Research, Web Search, and Writer.
Bring together asset assumptions, evidence, regulatory context, and valuation logic before a portfolio or governance discussion.
Typical output
Scenario framing, valuation-ready inputs, risk notes, and an executive summary.
Often involves
Valuation, Regulatory, Literature or Web Search, and Writer.
Security and governance
ARiDA is designed for teams that require governed execution, provenance, privacy, and reviewable outputs.
ARiDA runs on a managed runtime with privacy-aware routing, enterprise-oriented controls, and an operating model that fits sensitive biopharma research.
The platform is designed to leave behind inspectable files, task state, evidence trails, and source-linked summaries rather than unsupported paragraphs.
Shared projects, role-aware access, durable file tiers, and GDPR-aligned operating language matter because serious work needs review, accountability, and controlled handoff.
Documentation
The docs are public and cover async teams, workflow jobs, shared projects, Memory V2, file handling, built-in specialists, dynamic roles, and the enterprise operating model in much more detail.
Access and evaluation
Start with the public documentation if you want technical depth. Request access if you want early use of the platform. If your team is already onboarded, log in and continue working.
