After migration or a fresh Attio launch, implementation determines whether the workspace sticks: the schema that matches the business, the automations that remove daily friction, and the reporting framework partners actually use.
What's included
These usually ship together. The schema decides what the workspace can model, the automations decide what the team does not have to do manually, and the reporting framework decides what the partners actually look at on Monday morning. Splitting them across separate engagements creates rework. We scope them as one.
Capability 01
We translate how the business actually runs into an Attio data model: objects, attributes, relationships, stages, and lifecycle. Build from scratch when the workspace is new, or validate and refactor when the team has started and the shape is not holding up.
Capability 02
Stage triggers, task creation, Slack notifications, enrichment routing, and cross-tool data syncing. We implement the initial workflow set during configuration, then optimize once the team is live and we can see which workflows the team actually relies on versus which ones we should retire.
Capability 03
Attio's native dashboards cover the operating views partners want weekly: pipeline by stage, owner workload, stale deals, recent meetings, next-step gaps. We design the framework and the saved filters. When the firm needs deeper analytics, the work continues in data engineering.
When it fits
Implementation usually takes one of three shapes. Each changes scope, timeline, and production risk.
Shape A
Most common. The migration produces a fresh schema decision, day-one automations, and a reporting framework matched to the new model. Scope is folded into the migration engagement; you do not need to buy this separately.
Shape B
The team is starting on Attio with no source CRM to migrate. We design the schema from the business itself, configure the workflow set the team needs on day one, and stand up the reporting views partners will look at weekly.
Shape C
The workspace exists but the shape is not holding up. Schema drift, broken workflows, missing reports, lists that became proxies for objects. We audit the current state, propose the redesign, and ship it in phases without disrupting the live team.
How it runs
Implementation engagements run shorter than migrations because there is no source data to validate. The shape of the work is the same: scope first, build in your sandbox, ship to production, document so the team owns it.
We start with a free 30-minute call and map the business onto the object families Attio needs: Companies, People, Deals, and any custom objects the firm requires. We sketch the schema, identify the integrations needed at launch, and capture the reporting questions partners ask every week.
We configure the Attio workspace around the agreed schema, including the object model, attributes, relationships, stages, lifecycle, and permissions. The team reviews the work in the sandbox before any change touches production.
We build the day-one automations and connect the integrations the team needs at launch, such as email and calendar sync, Slack notifications, and deal-stage workflows. Deeper or bespoke integrations belong in our integrations service.
We create the saved filters, dashboards, and operating views partners look at every week. Training happens hands-on in the live workspace rather than through slides, and we hand over documentation so the team owns the system from day one.
Related services
Most engagements blend implementation with one or more of the services below.
When data needs to move
Move off Affinity, HubSpot, Pipedrive, Google Sheets, Salesforce, Copper, and others. Imports keyed on legacy IDs, scoped kickoff, dedup at the end.
CRM migrations service
When the stack needs to connect
Stripe, NetSuite, Calendly, Slack, custom APIs, warehouses. Attio SDK by default for reliability; Zapier / Make / n8n when you want a setup the team can manage without code.
Custom integrations service
When reporting needs depth
When Attio's native dashboards are not enough. Pipe Attio into a warehouse, model with dbt, build real dashboards in Metabase / Hex / Looker. Pipelines and orchestration alongside.
Data engineering services
The questions that come up most often when teams scope a setup engagement, separate from the migration mechanics.
Schema build plus day-one automations and reporting framework typically lands in 2-3 weeks when the business is well-understood and the team is decisive on the data model. Refactor engagements (existing workspace with drift) tend to take longer because we have to migrate the live workspace into the redesigned shape without breaking the team. Discovery call is where we land the actual estimate.
Yes. When the team is starting on Attio fresh with no source CRM to move data from, the implementation is the entire engagement. We design the schema from the business itself rather than from a translation of an old data model, which is often cleaner.
This page covers the in-Attio reporting framework: saved filters, dashboards, the operating views partners use weekly. When you need historical trend analysis, cross-system joins, or board-grade reporting, the work belongs to data engineering: pipe Attio into a warehouse, model with dbt, build dashboards in Metabase, Hex, or Looker. See /services/data-engineering/.
Both. Simple workflows (stage triggers, task creation, Slack notifications) get configured in Attio's native automation builder. More complex flows (parsing forwarded intro emails, building stage logic from multiple signals, syncing across tools with conflict rules) usually need the SDK and middleware. We pick the right tool per workflow rather than forcing everything through one path.
Yes. Refactor engagements start with a structured review of the current workspace. We look at the current schema, where it is drifting, which workflows duplicate each other, which lists have become proxies for objects, and where permission scope is leaking. From that review we propose the redesign and ship it in phases without disrupting the live team.
Yes. Documentation is part of the deliverable, not an afterthought. The schema gets documented with the rationale (why we chose this object family, when to add a new attribute versus a new object). Automations get documented with the trigger and the desired effect. Reporting framework gets documented with the saved filter logic so the team can extend it. Dependency on us is not a deliverable.
Book a 30-minute discovery call. We'll walk through your data model, the workflows the team actually runs, and what an implementation engagement would look like for your workspace.