Analytics Built From the Right Parts
Pipelines and financial metrics run on classic, tested code because a wrong join misleads every report. Forecasting and scoring use machine learning, and LLMs turn reviews, notes and tickets into structured signals. Decision dashboards ship faster on low-code. Each component gets the right engineering approach.
Analytics That Changed Real Decisions
For a 12-location café brand, our local market analysis scored 8,500 reviews, flagged two high-risk sites and picked one that beat its foot-traffic target by 34% in 90 days. For an apparel brand, returns analytics mined 15,000 return notes into a ranked fix list. Explore our case studies.
Revenue Data Protected
Revenue, customer and supplier data stay inside your private cloud perimeter. Nothing trains a public model, and SOC2/GDPR-aligned practices apply from the first prototype onward.
Your Models, No Lock-In
You own the pipelines, data model, trained models and notebooks. No proprietary analytics platform or per-seat license stands between your team and its own answers.
Works With Your BI
Results flow into Power BI, Tableau or your ERP when you already use them, so analysts keep their tools and gain models instead of another dashboard.
Honest About Uncertainty
Every result comes with its methodology and limits stated plainly, so leadership knows which numbers are measured, which are projected and which cannot be proven.
Data Analytics Consulting Services Cost & Packages
Analytics Sprint
$15,000+
One high-value question answered with a working model on your real data in about two weeks, such as a demand forecast, churn score or site ranking. Best when you need proof before funding a full analytics program.
What's included?
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Question Audit: decisions ranked by value with the data behind each one
Prototype Model: forecast, score or ranking built and validated on your data
Data Quality Check: gaps, duplicates and broken definitions found and documented
Findings Report: what the data shows, its limits and the path to production
Analytics Program
$38,000+
A production analytics layer covering your core questions, with modeled pipelines, validated models and results delivered into dashboards or your ERP. This is the tier most mid-market finance, operations and marketing teams choose.
What's included?
Everything in Analytics Sprint: hardened for daily use and deployed in your private cloud
Multi-Source Pipelines: ERP, CRM, storefront and external data unified into one model
Model Monitoring: accuracy and drift tracked with alerts when results degrade
Team Enablement: analysts trained to rerun, extend and explain the models
Advanced Analytics Platform
$70,000+
A multi-domain platform combining forecasting, scoring and LLM analysis of unstructured data, with insights that trigger actions in your systems of record. Built for companies replacing a stalled enterprise analytics rollout or a patchwork of consultants.
What's included?
Everything in Analytics Program: extended across finance, operations and customer domains
Unstructured Data Layer: reviews, notes, tickets and documents turned into structured signals
Closed-Loop Actions: insights routed to ERP, storefront or CRM with approval gates
Compliance & Governance: SOC2/GDPR-aligned controls, audit logs and documented methodology
Why Choose Building Agentic?
Most data analytics companies sell hours or dashboards. You could hire freelancers or an offshore team, or work with one accountable team that owns the answer.
Rules, Models, LLMs
Financial logic runs on tested code, forecasts on machine learning and free text on LLMs, each chosen for the question at hand.
Decisions, Not Decks
Every engagement starts from a decision and ends with a model your team can rerun, not a slide deck that ages in a folder.
Proof Before Budget
You see a working model answering one real question within 14 days, before you fund a larger analytics program.
You Own Everything
Pipelines, models and documentation stay yours, with no per-seat analytics license standing between your team and its own data.
How It Works
Analytics projects fail when they start with tools instead of questions. We run every engagement as four short, funded stages, each with a measurable result on your own data before the next one starts.
Step 1
Pick the Question
We rank the decisions where better data saves the most money, then confirm the data exists to answer the first one.
Step 2
Prototype the Model
A working forecast, score or ranking runs on your real data within about 14 days, validated against your own history.
Step 3
Deploy & Integrate
We harden pipelines, deploy inside your private cloud and deliver results into dashboards, alerts or your ERP where decisions happen.
Step 4
Measure & Expand
We compare outcomes to your baseline, document the methodology and limits, then apply the same engine to the next question.
Industries We Build Data Analytics For
The same analytics discipline works wherever decisions depend on messy, scattered data. These are the operations where we already run production systems and where analytics pays back fastest.
The Analysts Behind Your Answers
A founder leads every engagement, and our data lead has built analytics inside both a global retailer and a global insurer.
The founding team has built demand forecasting, pricing and recommendation systems at the scale of millions of users.
How We Turn Questions Into Models
Our data lead answers the questions executives ask before funding analytics: which models pay back first, how to trust a forecast and when not to build one at all.
Frequently Asked Questions About Data Analytics Consulting Services
