Engagement Models

No transformation theater.
Just outcomes.

Sprint-based engagements with fixed deliverables and measurable outcomes. We work in focused 2–16 week cycles — not 18-month programs that produce slide decks.

Data-First
We fix the foundation before touching AI tooling. Every time.
No Shelfware
Every engagement ships to production or we consider it incomplete.
Tech-Agnostic
We are not partnered with any vendor. We pick what fits your context.
Fixed Fees
Assessments and PoCs are fixed-fee. No billing surprises.
Engagement 01
◎

AI Readiness Assessment

Know exactly where you stand before you spend another dollar on AI.

A structured diagnostic across 23 dimensions of your data infrastructure, governance posture, team capability, and AI feasibility. We interview stakeholders, audit your stack, and stress-test your data foundations against real AI use cases. Outputs a board-ready report with a prioritized roadmap and ROI estimates.

2 WeeksFixed Fee
Best for

CDOs, VPs of Data, CTOs evaluating AI strategy

Deliverables
  • →AI Readiness Score (23 dimensions)
  • →Data Infrastructure Gap Report
  • →AI Use Case Feasibility Matrix
  • →Prioritized 90-day Roadmap
  • →Exec-ready Presentation Deck
Start This Engagement →
// Engagement Process
Day 1–3
Stakeholder Interviews
CDO, Data Eng leads, Business owners — understand the AI vision vs ground truth
Day 4–7
Technical Audit
Stack review, data quality sampling, governance posture, pipeline reliability
Day 8–10
Scoring & Analysis
23-dimension scoring, gap analysis, AI use case feasibility ranking
Day 11–14
Roadmap & Readout
Prioritized roadmap, effort vs impact matrix, exec presentation
Engagement 02
⬡

Modern Data Architecture

Design and build the data foundation that makes enterprise AI actually work.

From lakehouse design to data mesh implementation — we architect, prototype, and deliver production-grade data infrastructure optimized for AI workloads from day one. Technology-agnostic: Snowflake, Databricks, BigQuery, Delta Lake, Apache Iceberg — we pick what fits, not what we're partnered with.

8–16 WeeksSprint-based
Best for

Data Engineering teams, Platform leads, Architecture committees

Deliverables
  • →Architecture Decision Records (ADRs)
  • →Production Lakehouse / Data Platform
  • →Semantic Layer (dbt)
  • →Data Product Catalog
  • →Runbooks & Tech Transfer
Start This Engagement →
// Engagement Process
Sprint 1
Current State & Target Architecture
Document as-is, design to-be, validate with engineering team
Sprint 2–3
Core Infrastructure Build
Lakehouse foundation, ingestion pipelines, medallion layers
Sprint 4–5
Semantic Layer & Data Products
dbt models, metrics layer, first data products with SLAs
Sprint 6+
AI-Readiness Layer
Feature store, embedding pipelines, RAG corpus preparation
Engagement 03
◈

GenAI Accelerators

From governed data to working AI product in 4 weeks. No hallucinations, no excuses.

We build production-ready GenAI prototypes on your governed data — not on synthetic demos. RAG pipelines, enterprise copilots, AI agents, document intelligence, NL-to-SQL — each PoC is designed for handoff, not just a demo. Every accelerator includes full tech transfer so your team can own it post-engagement.

4–8 WeeksFixed Scope PoC
Best for

Product teams, Innovation leads, CEOs who want to see AI work

Deliverables
  • →Working GenAI Prototype (production-grade)
  • →Evaluation Framework & Benchmarks
  • →Architecture Documentation
  • →Full Source Code + Deployment Guide
  • →Team Enablement Session
Start This Engagement →
// Engagement Process
Week 1
Use Case Lock & Data Assessment
Pick the highest-ROI use case, validate data readiness, define success metrics
Week 2
Architecture & Pipeline Build
RAG pipeline / agent framework / fine-tuning setup on your infrastructure
Week 3
Prototype + Evaluation
Working prototype, hallucination rate benchmarking, user testing
Week 4
Production Hardening + Handoff
Guardrails, observability, cost optimization, full tech transfer
Engagement 04
◻

Data Governance for the AI Era

Governance that enables AI, not just checks compliance boxes.

Most governance programs are built to satisfy auditors, not power AI. We design governance frameworks that create trust in data — implementing data contracts, lineage tracking, access controls, and metadata management that make your AI outputs auditable and your data teams autonomous.

6–10 WeeksModular Sprints
Best for

Chief Data Officers, Compliance, Risk, and Data Platform teams

Deliverables
  • →Data Governance Policy Framework
  • →Implemented Data Contracts
  • →Lineage Graph (column-level)
  • →Data Catalog + Business Glossary
  • →Compliance Readiness Report
Start This Engagement →
// Engagement Process
Sprint 1
Governance Audit & Policy Design
Current state assessment, policy framework, RACI, data ownership model
Sprint 2
Data Contracts Implementation
Schema contracts, SLA definitions, quality gates in pipelines
Sprint 3
Lineage & Metadata Layer
End-to-end lineage, data catalog, business glossary
Sprint 4
Access Control & Compliance
RBAC/ABAC implementation, audit trails, regulatory alignment
Not Sure Where to Start?

Start with the Assessment.
Everything else follows.

90% of our architecture and GenAI engagements begin with a Readiness Assessment. It aligns stakeholders, surfaces real blockers, and makes the roadmap obvious.

Book a Free 30-min Discovery Call →