We built a unified data layer across 6 fragmented tools, then deployed 3 production AI agents (lead scoring, email drafting, and meeting prep) directly into the client's CRM product in 8 weeks.
These case studies are representative examples. Client names, figures, and specific details may have been modified or anonymized until signed releases are available.
The client had a solid CRM product and a growing customer base, but the market was shifting fast. Larger competitors were shipping AI features every quarter (smart lead scoring, auto-drafted emails, pre-call briefings) and the client's product team knew they were falling behind. The problem wasn't ideas. They had a backlog full of AI feature requests. The problem was data. Customer data lived in 6 different tools with no unified pipeline connecting them. Every AI prototype the team built worked on demo data and broke on real data.
We started with the data. The Data practice stood up a BigQuery warehouse, configured Fivetran connectors for all 6 source systems, and built a dbt transformation layer that created clean, unified customer profiles. While the data infrastructure was being built, the AI practice designed 3 agent architectures in parallel: a lead scoring model, an email drafting agent, and a meeting prep agent. Build embedded all three agents into the client's existing Next.js frontend and Supabase backend. The agents went from design to production in 8 weeks, with the data layer shipping in week 4 and the agents shipping incrementally in weeks 5 through 8.
The client shipped 3 AI features that their competitors had taken 6+ months to build. Users adopted them immediately; manual data entry dropped 60% because the agents pre-filled fields that reps used to type by hand. Engagement with AI-powered features was 2x higher than the product average, and the unified data layer opened the door for a dozen more features on the roadmap.
“We'd been trying to build AI features for a year and kept hitting the same wall: our data was a mess. DGTL fixed the foundation in 4 weeks and had 3 agents live in our product by week 8. Our users noticed immediately.”
VP of Product, B2B Sales Tech
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