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4L DATA INTELLIGENCE
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INTERACTIVE SCOPING ENGINE

Estimate Your Engineering Investment

Step 1 of 4

1. What is the primary focus of your project?

Select the primary architectural scope for your team.

Data Engineering & Pipelines Automated batch and streaming ingestion pipelines, dbt modeling, and warehouse synchronization.
Cloud Lakehouse & Platform Greenfield or migration to Snowflake, Databricks, BigQuery, or AWS with enterprise governance.
AI & Machine Learning Custom predictive models, LLM agents, semantic vector databases, and MLOps pipelines.
BI & Executive Dashboards Central semantic metric layer, Power BI / Tableau reporting, and sub-second dashboards.

2. What is your expected data volume and ingestion velocity?

Helps determine cluster concurrency, CDC requirements, and partition design.

Early-Stage (< 500GB / day) Standard daily batch runs, Postgres/MySQL replicas, and third-party SaaS API syncs.
Mid-Market (500GB – 10TB / day) Frequent hourly batches, high concurrency analytical queries, and multi-tenant reporting.
Enterprise (10TB – 100TB / day) Continuous streaming (Kafka/Kinesis), Change Data Capture, and sub-minute freshness SLAs.
Petabyte Scale (100TB+ / day) Massive IoT, financial tick feeds, or adtech telemetry requiring multi-region data mesh.

3. What is your required delivery velocity?

Determines pod staffing size and parallel engineering tracks.

Standard Velocity (3–5 Months) Systematic architecture, phased sprint handoffs, comprehensive automated testing, and team training.
Accelerated Sprint (4–8 Weeks) Urgent migration, pre-audit SOC2 readiness, or unblocking an impending product launch.
Dedicated Pod Retainer (Ongoing) Continuous delivery pod acting as your senior data engineering and platform team.
Architecture Audit (2 Weeks) Targeted FinOps cost review, security assessment, and technical roadmap documentation.
ESTIMATED ENGAGEMENT INVESTMENT
,000 – ,000

Based on senior-only execution pod (Principal Data Architect, Senior Platform Engineer, and FinOps Specialist).

What’s Included in This Scope:

  • Complete Terraform / Pulumi Infrastructure-as-Code scripts owned 100% by your team.
  • Production-grade CI/CD pipelines with automated schema validation and data quality testing.
  • Comprehensive architectural runbooks, security documentation, and team handoff sessions.
  • 30 days of post-deployment production monitoring and performance tuning SLA.

The 4L Transparent Engineering Estimation Framework

At 4L Data Intelligence, we reject opaque billing and arbitrary markups. We staff exclusively with senior practitioners—principal data architects, distributed systems engineers, and applied AI specialists. We do not maintain bloated junior pyramids or charge enterprise clients for on-the-job training.

Whether you require a fixed-price architectural sprint to modernise a lakehouse or a dedicated engineering pod embedded directly into your GitHub repository, our commercial models align our incentives with your speed to production and ongoing cloud cost efficiency.

Key Drivers of Enterprise Data Project Investment

1. Ingestion Velocity & Streaming Freshness

Standard overnight batch ELT using dbt requires less infrastructure complexity than real-time Change Data Capture (CDC) via Kafka or Flink. High-frequency streaming demands custom watermarking, stateful deduplication, and automated micro-file compaction.

2. Source Schema Heterogeneity

Connecting modern REST APIs or PostgreSQL databases is straightforward. Extracting data from legacy on-premises SAP, Oracle WMS, or bespoke mainframes requires specialized networking, VPC tunneling, and schema normalization pipelines.

3. Regulatory & Compliance Safeguards

Projects subject to HIPAA, SOC2 Type II, GDPR, or financial audit mandates require column-level encryption, automated PHI/PII masking, differential privacy filters, and immutable audit ledgers, expanding verification rigor.

4. Semantic Layer & Downstream Activation

Delivering data to a cold S3 bucket is only half the equation. Standardizing 100+ executive metrics with automated regression testing, sub-second Looker dashboards, and reverse-ETL push to Salesforce determines tangible business value.

Engagement Models Comparison

Model Ideal Scenario Pricing Structure Key Deliverable
Fixed-Scope Architecture Sprint Greenfield platform build, lakehouse migration, or emergency FinOps audit. Fixed milestone pricing ( - ) Turnkey production platform, Terraform code, CI/CD, and runbooks.
Dedicated Engineering Pod Ongoing product roadmap, scaling data teams, or continuous AI systems engineering. Monthly sprint retainer ( - / mo) Senior team embedded in your daily standups, Jira, and Slack.
FinOps & Architecture Review Runaway Snowflake/BigQuery bills or preparing for enterprise due diligence. Fixed fee or performance gain-share Comprehensive 40+ page audit report and optimization pull requests.

Frequently Asked Questions

Who owns the intellectual property and code?

You own 100% of the code, Terraform templates, SQL transformation logic, machine learning weights, and documentation generated during the engagement. Everything is committed directly to your private GitHub or GitLab repository from day one.

How quickly can 4L mobilize an engineering pod?

Because we maintain an active bench of senior architects in San Francisco and Miami, we typically execute mutual NDAs, complete access provisioning, and kick off active architecture sprints within 5 to 7 business days.

Do you provide post-launch support and SLAs?

Yes. All fixed-scope deliverables include 30 days of complimentary production hypercare. Following rollout, clients can retain ongoing platform monitoring and pipeline support via our flexible retainer tiers.

Need a Precise, Fixed-Fee Architectural Proposal?

Schedule a 45-minute technical discovery session with our Lead Architect. We evaluate your existing data stack and return a detailed milestone roadmap within 3 business days.

Schedule Discovery Session