Predictive ICP & lead scoring
A model trained on your actual win data — not a vendor's generic template. Accounts get a fit score and an intent score, and reps see why, not just what.
- Closed-won training
- Explainable
- Monthly retrain
Pillar 01 · Service 03 AI Automation
Dashboards
that answer to revenue.
Predictive scoring, attribution, and forecasting built on a warehouse your team can actually trust. Not a Salesforce dashboard. Not a consultancy slide. A working analytics stack your leadership runs the business from.
- Typical engagement 8–16 weeks
- Warehouse-native Yes
- Exec-ready reporting Week 4
01 What we build
The analytics layer
your GTM team operates against.
From raw event data to the exec dashboard that decides next quarter's hiring plan.
Multi-touch attribution
Data-driven attribution wired to your warehouse and normalized across paid, organic, content, and events. CFO-defensible, marketer-useful.
- Shapley / Markov
- Warehouse-native
- Channel-level
Pipeline & revenue forecasting
Forecasts that reflect real historical velocity by segment, not a flat conversion rate applied to every stage. Leadership stops guessing.
- Segment-aware
- Scenario modeling
- Weekly cadence
Warehouse & dbt
The boring-but-essential foundation. Snowflake / BigQuery / Redshift, dbt models you can read, tested pipelines, and a data contract between engineering and analytics.
- dbt models
- Data contracts
- CI-tested
Reverse-ETL & CDP
Warehouse-to-tools syncing so your enriched data lives where your reps actually work. Hightouch / Census, plus whatever CDP you've committed to.
- Hightouch / Census
- Identity resolution
- Consent-aware
Exec & ops dashboards
One source of truth for the weekly review. Metabase or Looker dashboards a CFO can read without a training session, a board member can skim without confusion.
- Looker / Metabase
- Board-ready
- Export to deck
02 Stack
Modern data stack, no religion.
We use what your team already has where possible. If you need net-new, we lean modern data stack — but we've shipped on Google Sheets when that was the right answer.
Snowflake / BigQueryWarehouse
dbtTransformations
Fivetran / AirbyteIngestion
Hightouch / CensusReverse-ETL
Looker / MetabaseBI
Segment / RudderstackEvent tracking
Python / scikit-learnModels
Great ExpectationsData quality
03 How we ship
Data first.
Dashboards last.
Inventory
What sources, what tables, what's broken, what's duplicated. A truthful map of your current stack before we touch anything.
Model
Canonical entities, dbt transformations, tested pipelines. The boring foundation that everything else stands on.
Predict
Scoring models, attribution, forecasting. Validated against historical outcomes before they touch production.
Operate
Reverse-ETL to tools, exec dashboards live, team trained. Handover includes the ops cadence that keeps it working.
KacificSatellite Internet · SG
X-PHY IncCybersecurity · US
FlexxonNAND Solutions · SG
Gauze CareMedical Products · US
NDAIndustrial SaaS · EU
NDAHealthtech · US
NDAClimate & Energy · APAC
NDAB2B Fintech · UK
Let's build it
What do you
not know yet?
Tell us the question your board keeps asking that nobody can answer cleanly. We'll send back a read on how to fix the data behind it.