InsightLens
Your competitors are predicting churn before it shows in their CRM. They're optimizing inventory before the monthly close. They're giving every sales rep the next best action before the rep picks up the phone. You're still waiting for last quarter's dashboard to load. The gap between what your data could tell you and what your leadership actually acts on is where revenue leaks, margins erode, and market share shifts — quietly, then all at once.
AI-native analytics is widening the competitive gap. The window to close it is narrow.
Analytics leaders vs. laggards EBIT premium
Orgs with AI/ML operationalized in production
Time-to-answer reduction with AI analytics
Your data is everywhere. Your decisions are still based on last quarter.
Enterprise data and analytics leaders face a version of the same problem — and the bigger the organization, the hairier it gets: more data than ever, and less confidence in it. Your operational data lives in Salesforce. Your financial data is in an ERP. Your product telemetry is in cloud storage. Your customer behavior data is scattered across three marketing platforms and a data warehouse that hasn't been properly maintained in 18 months.
Your BI team produces reports. Your data engineers keep pipelines running. But no one has connected predictive intelligence to the decisions that actually drive revenue, margin, or risk exposure. The dashboards your executives see are descriptive — they explain what happened, not what's about to happen or what to do about it.
Meanwhile, AI-native competitors are using real-time demand signals to optimize inventory before you've finished your monthly close. They're predicting churn before it shows in your CRM. They're surfacing the next best action for every sales rep before the rep picks up the phone.
InsightLens closes the gap between your data infrastructure and your decision-making velocity — on Google Cloud, in weeks, not quarters.
The impact on your decision-making velocity
6 capabilities that drive measurable business value.
Each capability is delivered by our InsightLens AI Pod — specialized agents doing the work, a small human team governing quality and outcomes.
Scorecards that drive decisions — not dashboards that get ignored.
Most enterprise dashboards fail for the same reason: they were designed by data teams for data teams. Executives see 40 metrics when they need 5. They see what changed, not why it changed or what the implication is. InsightLens designs and deploys Looker scorecards built around decision workflows — starting from the decision, not the data.
For each executive function (CFO revenue operations, COO supply chain performance, CMO pipeline and attribution, CRO quota attainment), we define the 3–5 metrics that drive weekly decisions, build LookML models that surface those metrics with appropriate context, and deliver scorecards with drill-through paths that let leaders move from summary to root cause in two clicks.
Scorecard adoption is an explicit outcome metric. We track it, report on it, and iterate against it.
Ask your data a question. Get an answer in plain language.
The analyst bottleneck is real: every business question that can't be answered by a pre-built scorecard lands in a queue. Business leaders wait days for insights their teams spend hours producing. InsightLens deploys NLP-driven analytics interfaces powered by Gemini and Vertex AI — connected to your BigQuery warehouse — so business users can ask questions in natural language and receive structured, contextualized answers with the supporting data and visualization.
This is not a chatbot bolted onto a BI tool. It is a semantic layer built over your data model, with query generation that understands your business terminology, enforces row-level security, and returns results in the format most useful to the asker: a number, a chart, a ranked list, or a narrative summary with anomaly flags.
Stop explaining what happened. Start predicting what will.
Descriptive analytics tells you where revenue came from last quarter. Predictive analytics tells you where it's going next quarter — and which signals are moving. InsightLens develops Vertex AI models trained on your historical operational, financial, and behavioral data: demand forecasting models for supply chain optimization, churn propensity models for customer success intervention, revenue run-rate models for FP&A planning, and opportunity scoring models for sales prioritization.
Models are not static deliverables. Our pod deploys to Vertex AI endpoints with continuous drift monitoring, automated retraining triggers, and explainability outputs that let your business analysts understand the model's reasoning — not just its score. You don't inherit a black box.
From insight to action — with the next best move surfaced automatically.
Predictive models tell you what's likely to happen. Prescriptive intelligence tells you what to do about it — and quantifies the expected outcome of each option. InsightLens builds prescriptive decision layers on top of your predictive models: inventory reorder optimization that balances carry cost against stockout risk; pricing recommendation engines that model margin impact before a rep discounts; workforce scheduling optimization that accounts for demand variability and labor constraints.
These aren't static rule engines. They're reinforcement-learning-informed recommendation systems built on Vertex AI, with feedback loops that improve recommendations as outcomes are recorded. The result: your operational teams get a specific recommendation, not a dashboard to interpret.
Data your organization can trust — and audit.
AI models trained on bad data produce confident wrong answers. Scorecards built on inconsistent definitions mislead executives. InsightLens treats data governance not as a compliance checkbox but as the foundation that makes everything else work.
Our pod implements Google Cloud's Data Catalog for automated metadata management and lineage tracking, BigQuery column-level access controls and row-level security policies for sensitive data, and dbt-enforced data contracts between pipeline layers. Model inputs are documented, versioned, and auditable. Every metric in every scorecard traces back to a defined, tested, approved data source.
For organizations in regulated industries — financial services, healthcare, manufacturing with export controls — this audit trail is not optional. For every enterprise, it is the difference between analytics that gets used and analytics that gets questioned.
Reliable data in. Trustworthy decisions out.
Every analytics initiative fails at the same place: the data pipeline. Broken ingestion, inconsistent schemas, undocumented transformations, and silent failures that corrupt downstream models before anyone notices. InsightLens starts by engineering the foundation — Cloud Composer-orchestrated pipelines that ingest from your ERP, CRM, product telemetry, and third-party data sources into a structured BigQuery lakehouse.
Our pod builds dbt transformation models with embedded data quality tests at every layer — not bolted on afterward. Lineage documentation is generated automatically. SLA monitoring catches failures before they reach a scorecard. AI agents run continuous regression validation after every pipeline change, so your data team stops firefighting and starts building.
GCP Analytics Accelerator Kit
Pre-built Composer DAG templates, BigQuery schema registry, and dbt model scaffolding. Eliminates 8–12 weeks of foundational pipeline build.
Your GCP investment. Our intelligence layer. Decisions that drive revenue.
We don't build a proprietary analytics platform on top of your cloud. We add a differentiated intelligence and agentic layer to your existing GCP investment — turning data infrastructure into decision infrastructure.
Executives get answers in Looker sessions that replace days of manual data assembly
Churn propensity, demand forecasting, and revenue run-rate models that see around corners
Business users ask questions in plain language and get structured, contextualized answers
What we add on top of your existing platform investments — the IP, agents, and accelerators that create differentiated outcomes.
Predictive and prescriptive models deployed with drift monitoring, automated retraining, and explainability — the intelligence that turns data into foresight
Natural language analytics over your data model — leadership asks questions in plain language, gets structured answers with row-level security
Pipeline Agent builds and monitors Composer DAGs. ML Agent trains and deploys Vertex AI models. Quality Agent enforces data contracts and SLA. Analytics Agent builds Looker dashboards and tracks adoption. All operating continuously — not waiting for a sprint.
Automated schema drift detection, lineage tracking, data quality enforcement, and governance policy compliance — on every pipeline run, not quarterly audits
Pre-built Composer DAGs, BigQuery schemas, dbt models, and Looker blocks — 8–12 weeks of foundational build eliminated. Every deployment makes the next one faster.
Your analytical data infrastructure — extended with AI-driven intelligence, not replaced
Your operational data sources — bidirectionally connected into the analytics platform
Your current reporting tools — augmented with predictive intelligence and self-service analytics
Your pipeline and governance infrastructure — automated and continuously monitored
Outcomes from the field — not from a slide deck.
Replacing spreadsheet-driven planning with a real-time GCP analytics platform.
A B2B technology company with $700M+ in annual revenue was running its revenue operations, FP&A, and customer success functions on a mix of Excel models, Salesforce reports, and disconnected BI tools. There was no single source of truth. Executives received different revenue numbers from different teams. The CFO's monthly close required 3 days of manual reconciliation. Churn was identified reactively — after it appeared in the CRM, not before.
16-week deployment | BigQuery + Vertex AI + Looker + Salesforce integration | 4-phase delivery
Predictive demand intelligence for a global manufacturer managing supply chain volatility.
A global manufacturer operating across 12 production facilities and 60+ markets was forecasting demand using a combination of historical sales data and sales team gut feel. Stockout events were costing margin. Excess inventory was tying up working capital. The operations team had no visibility into which demand signals — external market data, distributor ordering patterns, macroeconomic indicators — were the most predictive of their actual demand cycles.
20-week deployment | BigQuery + Dataflow + Vertex AI + Looker | 3-phase delivery
Ready to see InsightLens in action?
We'll walk you through a InsightLens demo — how our AI Pod delivers, what the economics look like, and how it applies to your specific use case. 30 minutes. No commitment.