Aequo helps individuals, research groups, and organisations turn their own AI agents into capable professional collaborators—equipped for the work they actually need to do.
A prompt alone cannot carry a complex professional task. Capability emerges when knowledge, method, execution, orchestration, and quality control are designed together.
Curated technical references, terminology, domain context, assumptions, and structured knowledge resources that help an Agent understand the subject it is working on.
Possible assets: knowledge bases · reference libraries · context packsExplicit procedures, decision rules, task decomposition, scientific protocols, and engineering practices that guide how work should be done.
Possible assets: Skills · method guides · task specificationsConnect agents to the software, scripts, APIs, datasets, and execution environments required to carry out defined operations.
Possible assets: tool wrappers · APIs · scripts · software integrationsOrganise specialised tasks, dependencies, multiple agents, checkpoints, and hand-offs into workflows that can be inspected and resumed.
Possible assets: workflow templates · agent roles · orchestration rulesDefine evidence requirements, automated checks, audit records, permission boundaries, and human review points appropriate to the task.
Possible assets: test suites · QC gates · provenance rules · review protocolsWe do not require users to adopt one closed Aequo platform or buy an entire research lifecycle. The integration target and scope are agreed around the user's existing environment and practical needs.
Equip an existing Agent for a focused task—such as literature screening, computational chemistry setup, manuscript quality checks, or production-line diagnosis.
Where technically compatible, connect the capability to the Agent framework, scientific software, data sources, and operating environment already used by the team.
Design reusable and documented assets so users can inspect, maintain, and extend their workflows rather than depending on a black-box service.
Set appropriate approval gates for scientific interpretation, access permissions, operational actions, and other consequential decisions.
Each engagement is scoped independently. The sequence below describes a typical capability-development process, not a mandatory package.
Understand the user's problem, current Agent environment, inputs, outputs, constraints, and success criteria.
Translate domain knowledge and professional methods into structured instructions, tools, and workflows.
Connect the capability to the target environment and test it against representative tasks and known constraints.
Provide documentation, reusable assets, known limitations, and a route for user-led iteration.
Tell us about the task and the environment where your Agent works.