10x the Delivery of Critical Assets

The most common first engagement for a marketing team is an integration and reporting project, connecting two or three data sources and building a custom dashboard on top. These typically run six to ten weeks depending on the complexity of the data sources and the number of views required. A full internal campaign management platform or CDP replacement is a Production Application build at $60K to $200K over 3 to 5 months. For typical budgets and timelines, see our project cost estimator at codieshub.com/project-cost-estimation. We scope every project with a discovery sprint first so you have a firm estimate before committing to a build.

Yes, this is the explicit premise of most of our marketing engagements. We integrate with Salesforce, HubSpot, Marketo, Pardot, and several mid-market MAPs via their native APIs or webhooks. We also connect to ad platforms (Google, Meta, LinkedIn, TikTok) and data warehouses (BigQuery, Snowflake, Redshift). The goal is always to make your existing stack more valuable by closing the integration gaps, not to add another standalone system.

Data privacy compliance is scoped explicitly at the start of every engagement that touches PII. We design data flows to minimize PII exposure: pseudonymizing identifiers at the point of ingestion, implementing field-level encryption where required, and ensuring that consent signals from your consent management platform propagate correctly to downstream systems. For EU data subjects, we can architect data residency within AWS EU or GCP EU regions. We are not a legal or compliance firm, but we build the technical controls that your compliance team specifies.

Custom attribution can range from a simple multi-touch model (linear, time-decay, position-based) implemented in SQL and surfaced on a dashboard, to a data-driven model trained on your historical conversion data using logistic regression or gradient boosting. The minimum data requirement for a statistical model is typically 1,000 to 3,000 tracked conversion events with full touchpoint history. For simpler rule-based models the data requirement is much lower. We usually start with a rule-based model in the first sprint and layer in statistical modeling once we have validated the data pipeline.

We build marketing software in two-week sprints with a rolling backlog. At the start of each sprint we confirm the top priorities with the marketing lead, and changes between sprints are accommodated as long as the total scope does not grow. If a direction change would require extending the timeline, we flag it immediately with a revised estimate before proceeding. We do not have a change-order-heavy process. Our preference is to maintain a transparent backlog and deprioritize lower-value items when new priorities emerge.