Service Offering
AI Data Foundation
We help SaaS companies build a centralized data layer, ready for AI, in weeks, not quarters. Stop waiting for multi-year consolidation projects and start building the foundation you need to scale AI.
What You Can Build
AI Use Cases Across Your Business
A centralized data foundation unlocks AI applications across every function. Here's what becomes possible.
Account Health Dashboard
Unified view of usage, engagement, support, and NPS data per account to surface risk and opportunity at a glance.
Renewal Risk Prioritization
Score and rank accounts by renewal risk using health scores, usage trends, sentiment, and days to renewal.
CS Copilot: Call Prep
Auto-generated account briefings before every call, drawing from the full 360: transcripts, NPS verbatims, open risks.
Churn Early Warning (90-Day)
Predictive model combining feature decay, call sentiment, ticket burden, and email engagement to flag at-risk accounts.
Automated Renewal Outreach
Agent that monitors renewal risk scores and account context, then drafts and sends personalized renewal outreach.
Escalation Early Warning
Triggers alerts when P1 tickets, renewal proximity, and ARR thresholds converge on the same account.
Each layer adds signal that improves every other CS use case. A health score without engagement data misses relationship warmth. A churn model without call sentiment misses the clearest early warning signal.
The Problem We Solve
Most SaaS companies that have grown through acquisition are sitting on fragmented customer data scattered across legacy CRMs, CS platforms, product tools, billing systems, and spreadsheets. There's no shared customer master. No reliable source of truth. No foundation for AI.
Waiting for a multi-year CRM consolidation is the wrong answer. Bolting on another CS platform creates yet another silo. The AI wave is here now. Companies that don't build a centralized data foundation in the next 12–18 months will miss it entirely.
How It Works
Four Phases to AI Readiness
Phase One
Days 1–30
Discovery & Data Mapping
- → Inventory all source systems: CRMs, CS platforms, billing, product telemetry, support
- → Identify canonical customer & account records across systems
- → Map fields to a standardized SaaS customer data model
- → Document permissions, data ownership, and access requirements
- → Identify initial business use cases
Outcome
Full source inventory + target schema
Phase Two
Days 31–60
Data Foundation Build
- → Stand up centralized data warehouse or vector store environment
- → Implement ELT pipelines from source systems using modern open-source tooling
- → Execute deduplication, field standardization, and record matching
- → Apply permissions, compartmentalization, and data governance rules
Outcome
Clean, unified data layer in production
Phase Three
Days 61–90
AI Readiness Layer
- → Configure RAG architecture on top of the data foundation
- → Connect structured and unstructured data into a unified queryable store
- → Enable connection to your preferred LLM (OpenAI, Copilot, Claude)
- → Deliver initial AI use cases: health queries, renewal signals, expansion flags
Outcome
Working AI applications on live data
Phase Four
Days 91–120
CRM Integration Roadmap
- → Use the clean data model to define target CRM state
- → Provide a record-by-record migration roadmap from validated data
- → Reduce CRM consolidation risk by solving data quality first
- → Support phased migration as legacy CRMs are rationalized
Outcome
Derisked, phased CRM consolidation plan
"Don't wait for Salesforce to be ready. Build the foundation now and let it inform the migration, not the other way around."
What You Get
Deliverables
Source System Inventory
Complete map of all data sources, record types, field definitions, and ownership across your organization.
Standard Customer Data Model
A rationalized account, contact, product, and activity schema tailored to your business and AI use cases.
Centralized Data Warehouse
A production-ready data environment with cleansed, deduplicated, and permissioned records.
ELT Pipelines
Automated connectors from source systems into the central layer, built on modern open-source tooling.
AI Query Interface
A working RAG-based query layer connected to your preferred LLM: OpenAI, Microsoft Copilot, or Claude.
CRM Rationalization Roadmap
A phased plan for downstream CRM consolidation, informed by the clean data layer you've already built.
Why Balboa
What We Bring to the Table
We've done this before
Our team includes data warehouse architects and ETL practitioners with decades of experience. The same problems, now solvable in a fraction of the time.
We move fast
Baseline use cases and working pipelines in weeks, not months. No 12-month engagement before you see results.
We think like operators
Our leadership has held CCO, SVP CS, and CRO roles in SaaS. We know what revenue and success teams actually need from this data.
AI-native from day one
We build the data layer with AI consumption in mind. RAG architecture and LLM connectivity are built into every engagement.
Pendo-integrated
For companies using or evaluating Pendo, we layer in product telemetry data directly, powering renewal prediction and expansion identification.
Who This Is For
Built for SaaS Leaders Who Can't Wait
VP or C-level CS, Revenue, and Data leaders who know the data is broken and need to act before the window closes.
Companies that have grown through M&A managing multiple legacy CRMs, inconsistent records, and no unified customer view.
Organizations planning a CRM consolidation who want to derisk the process by solving data quality first.
Teams that tried the traditional approach (big SI, multi-year project) and watched it stall before delivering value.
Engagement Model
Choose Your Starting Point
Advisory Sprint
Architecture review, source system assessment, and a prioritized roadmap. The fastest path to a clear plan of action.
2–3 weeks
Foundation Build
Hands-on implementation of the data layer, ELT pipelines, and initial AI use cases. From fragmented to functional.
6–12 weeks
Ongoing Partnership
Continued development, expansion of AI applications, and CRM rationalization support as your environment evolves.
Ongoing
Ready to build your AI Data Foundation?
Let's talk about where you are and what it would take to get moving in weeks.