# Basecamps | Outcome-Priced AI Engagements | Climb Source: https://climb.ai/basecamps # Migrate, modernize, and ship AI in weeks. Basecamps are guided Databricks engagements with defined scope, timeline, and outcomes. Backed by pre-built solutions. Tailored to your needs. Shipped to production in weeks. [Book a Discovery Call](/contact) ## Why we built engagements this way Climb brings senior data and AI teams, proven services leadership, and a delivery model built for the agentic era. Led by operators who have scaled some of the largest pure-play services firms in North America, we are now focused entirely on helping enterprises turn Databricks into production AI advantage. - ### Defined scope, defined outcomes The work starts at kickoff, not after a discovery phase. Each Basecamp arrives pre-scoped, with architecture, deliverables, and outcomes already mapped. - ### Senior teams ship in weeks Six-role senior teams. Every Basecamp is delivered by engineers who have shipped this kind of work at enterprise scale before. - ### Reusable IP compounds Each Basecamp gets faster as the Climb AI Runtime strengthens. Every new engagement gets the benefit of every prior project. ## Basecamps Four engagements, each scoped to a specific modernization outcome. Defined timeline, fixed price, and a KPI agreed before kickoff. Start where the pain is sharpest. - Basecamp 01 From Tableau or Power BI to AI/BI Genie 7–15 weeks ### Replace per-seat BI with AI-native analytics. Per-seat BI is a recurring tax on insight, and the AI bolt-ons do not know your data. We migrate your dashboard estate to AI/BI Genie on Databricks. Plain-English analytics governed by Unity Catalog. Always-live data. Per-seat licenses retired workload by workload, not all at once. - $0 Per-user BI licensing - 60% Lower analytics platform cost at scale - 100% Live data. Zero extracts to maintain. - 10× Faster time to insight on ad-hoc queries - Basecamp 02 From Hadoop, HDFS, and Hive to Databricks Lakehouse 13–27 weeks ### Modernize the platform without the multi-quarter chaos. Hadoop stalls the AI initiatives on your roadmap. No native ML, expensive specialists, escalating support contracts. The migration runs domain by domain with parallel-run validation. Hive to Delta Lake, MapReduce to Spark, Oozie to Databricks Workflows. AI-ready infrastructure on the other side. No big-bang cutover. - 10× Faster than Hive - 50% Lower total cost of ownership - 100% Spark compatibility - 99.9% Platform reliability - Basecamp 03 From ADF or Fabric to Databricks Lakeflow 10–22 weeks ### Unify pipelines and cut orchestration costs. If you run Databricks alongside ADF or Fabric, you are paying for two platforms to do the work of one. We move the ADF estate to Lakeflow Connect, Designer, and Jobs. One platform for ingestion, transformation, and orchestration. Lineage and governance unified inside Unity Catalog. Cutover is staged by domain and validated in parallel before anything is retired. - 83% ETL cost reduction (Lakeflow customers) - 25× Faster pipeline development - $0 Orchestration fees - 99.9% Orchestration reliability - Basecamp 04 Lakebase Activation 9–18 weeks ### Run AI at sub-second latency on your governed data. Operational apps and analytical data live on opposite sides of fragile CDC pipelines. We deploy Lakebase inside your Lakehouse. Postgres-compatible OLTP with zero-config Synced Tables. CDC pipelines retired. Operational and analytical data unified under Unity Catalog. AI inference at sub-400ms. - 500ms Database provisioning - <400ms End-to-end AI inference - 40% Fraud reduction (Lakebase customers) - 100% Postgres compatible Platform figures as reported by Databricks and its customers. ## Same operating rhythm. Every Basecamp. Aligned to a P&L outcome before kickoff. Owned by one senior team. Handed off cleanly to yours at the end. Whether the Basecamp is a BI migration or a Lakebase activation, the cadence is consistent and predictable. - Phase 01  ·  Assess ### Outcome alignment Strategy session with executive stakeholders. KPI defined and signed off before code is written. Ships: Readiness Report - Phase 02  ·  Design ### Architecture & governance Target architecture mapped. Unity Catalog model. Migration sequence prioritized. Ships: Architecture spec - Phase 03  ·  Build ### Migrate or build to production Domain-by-domain execution. Parallel-run validation. Reusable AI Runtime accelerating delivery. Ships: Production system - Phase 04  ·  Optimize ### Climb Summit handoff Runbook transferred. KPI measured against baseline. Internal team self-sufficient. Ships: Measured KPI delta ## More Basecamps in active development The Basecamp library compounds. Each engagement we ship hardens the next one. Here is what is queued up next. - Coming soon ### Agentic OS Agent runtime, governance, and observability on top of Databricks. Ship enterprise-grade agents without rebuilding the platform layer. - Coming soon ### AI Transformation Org-wide AI adoption blueprint. Use-case prioritization, governance scaffolding, and the human enablement that makes the platform stick. - Coming soon ### Next-Gen Analytics & BI AI-native analytics layered over the Lakehouse. Self-service for executives, agents for analysts, dashboards retired one workload at a time. ## Common questions - 001 How does Climb price engagements? We define the scope, timeline, success metric, and commercial model before kickoff. Most Basecamps are fixed-scope and fixed-price, with pricing aligned to the business outcome being delivered. No open-ended time-and-materials sprawl. - 002 How fast can you actually start? Discovery calls can typically happen within 48 hours. From there, we run a focused diagnostic to confirm scope, KPI, data readiness, and delivery path. Most Basecamps can move from discovery to kickoff within a few weeks. - 003 Why Databricks? Because enterprise AI depends on governed, high-quality, accessible data. Databricks brings data engineering, governance, analytics, machine learning, and AI application patterns into one platform. We go deep on Databricks because production AI rewards platform depth over generalist coverage. - 004 Do you work on our existing stack? Yes. We are Databricks-native but pragmatic. We work with the rest of your stack as needed and do not require a green-field rebuild to ship value. - 005 Who owns the IP once we're done? You do. Everything we ship lives in your environment with your team named as owner. Our IP is the delivery runtime and playbook, not your deliverables. - 006 What if our scope changes mid-Basecamp? Scope is fixed before kickoff. Genuine net-new work is handled as a separate Basecamp or a change order tied to a defined KPI delta. Never an open meter. - 007 How do you measure outcomes? Every engagement ships against a measurable KPI defined before code is written. We establish the baseline, deploy the production system, and verify the delta against the agreed outcome. 0 AI ## Done piloting?Let's go to production. Thirty minutes with a senior architect. Bring the use case, migration, or AI initiative that is stuck. Leave with the Basecamp that fits, the likely path to production, and a straight answer on what it will take to ship. [Book a Discovery Call](/contact) [Browse Basecamps](/basecamps)