Manizh Consulting · Boutique Data & AI

Senior architecture, lean teams, outcomes — not utilization.

A boutique consulting practice for Snowflake, modern BI, and production AI. We run small senior teams against scoped engagements — and we stay for the run. Fixed bid when you want certainty. T&M when you need flexibility. Same dollar ceiling either way.

56%
Cost vs. Tier-1 SI
< 8%
Annual attrition
4.0+
Snowflake skills · 0-5
100%
Senior · no benched bodies
What we do

Four capabilities. One bench.

Every Manizh engagement is delivered by senior architects who own the work end-to-end. We do not subcontract. We do not bench-warm. We pick four things we are deeply good at — and we say no to the rest.

Snowflake engineering

From greenfield platforms to mature-estate optimization. We own the architecture, security model, FinOps posture, and the migration runbook.

  • Pipeline migration (Fivetran, Stitch, dbt, Coalesce)
  • Semantic layer & medallion architecture
  • RBAC, masking, network policies
  • Cost optimization & warehouse rightsizing

Sigma & modern BI

Sigma Computing migrations, dashboard programs, and semantic-layer governance. Tableau and Power BI when the use case warrants.

  • Sigma migrations & reusable template kits
  • Self-service governance & RLS design
  • Executive dashboards & KPI scorecards
  • Sigma + dbt semantic alignment

AI engineering

Production-grade — not demos. RAG, agentic systems, and LLM evaluation, built on the data layer we already understand inside out.

  • Retrieval-augmented generation (RAG)
  • Agentic workflows & tool orchestration
  • LLM evaluation harnesses & guardrails
  • Production observability for AI systems

Advisory & architecture review

Independent reviews of your existing platform, vendor proposals, or roadmap. We give honest answers — including when the answer is "stay where you are."

  • Architecture review & second opinions
  • Vendor / SI proposal evaluation
  • Snowflake cost & performance audit
  • Two-quarter data-platform roadmap
Industry focus

Built for retail. Engineered for apparel.

The deepest part of our domain bench is retail — specifically apparel, wholesale, and multi-channel retail. We have shipped Snowflake estates that move sales, inventory, customer, vendor, and product data for retailers ranging from mid-market specialty chains to enterprise-scale diversified operators with multi-billion-dollar annual revenue.

CHANNEL

Omnichannel data

DTC, retail stores, marketplaces, and wholesale unified into a single Snowflake estate with channel-agnostic identity resolution.

CATEGORY

Apparel attributes

SKU, color, size, season, style-color-size grain modeling. SCD-2 attribute history. Size-curve and option-count analytics.

WHOLESALE

B2B vendor flows

EDI 850 / 856 / 810 ingestion. Vendor scorecards. Partner portals. Drop-ship reconciliation. PO-to-cash visibility.

MERCHANDISING

Inventory & demand

Store-day-SKU inventory grain. OTB & markdown analytics. Allocation, replenishment, and demand-forecast feature pipelines.

Platform evaluations · anonymized

Two retail evaluations we have assisted on.

CLIENT HIGHLIGHT · 01

Specialty Apparel Retailer

Mid-market North American men's apparel · multi-channel

200+
Retail stores
~$500M
Annual revenue
DTC · Retail · Wholesale
Channels
SAP + Snowflake
Stack
HOW MANIZH ASSISTED

Delivered proof-of-concept pipelines as part of the client's Snowflake platform evaluation. POCs covered sales, inventory, customer, and SAP integration patterns — exercising the architecture options the client was considering.

CLIENT HIGHLIGHT · 02

Enterprise Diversified Retailer

Multi-vertical · global footprint · highly regulated

$8B+
Annual sales
4,000+
Global locations
Multi-category
Apparel · electronics · services
PIM + ERP + WMS
Source systems
HOW MANIZH ASSISTED

Delivered proof-of-concept pipelines as part of the client's enterprise Snowflake evaluation. POCs covered B2B vendor ingestion, apparel category analytics, and PIM integration patterns — exercising the patterns the client was assessing for forward-state architecture.

Capability spotlight

Snowflake ↔ enterprise PIM & ERP.

Production-grade integration patterns from enterprise PIM and ERP systems into Snowflake — built for retailers whose product master, finance ledger, and analytics data all need to reconcile in one place. We handle the slowly-changing dimension complexity, the reconciliation, and the master-data conflicts so the analytics layer just works.

PIM
SAP MDG · Stibo · Informatica · Salsify · inriver
ERP
SAP S/4HANA · SAP CAR · Oracle · NetSuite · Microsoft D365
REFERENCE FLOW
SAP MDG · product master, hierarchy, attributes
Snowpipe / CDC · streams, dynamic tables
Snowflake · gold layer, SCD-2 product dim
Featured Product · NEW

Manizh PMIS — AI-native project management.

Ask questions in plain English. Get answers in seconds. Manizh PMIS transforms capital programs with AI-powered portfolio intelligence — no SQL, no dashboards to maintain, no reports to chase.

  • Natural-language portfolio & project queries
  • Real-time analytics on schedule, cost, risk
  • AI-generated insights and exception alerts
  • Built for capital-program owners and PMOs
M.
Manizh PMIS
LIVE
YOU
"Which capital projects are tracking over 10% above budget this quarter?"
MANIZH PMIS · 1.4s
Four projects: Northgate Refit (+14%), Beacon Tower Phase 2 (+12%), Tribute Plaza (+18%), Westwood Logistics (+11%). Total variance $4.2M. Top driver: subcontractor change orders. Would you like the detailed breakdown?
94
PROJECTS
$2.1B
PORTFOLIO
12
AT RISK
Capability spotlight

Captive build-out, with consulting rigor.

We help enterprises stand up their own offshore captive — also called a Global Capability Center — the right way. Not staff augmentation. Not body-shop arbitrage. A genuine second office of yours, run with the discipline of a consulting practice.

We have done this ourselves: Manizh's Coimbatore practice is built on the same model we set up for clients. The methodology, governance, and quality bar travel with the playbook.

Senior-first hiring
Architects first, then leads, then developers. Quality bar set before scale.
IP & methodology stays with you
Captive operates under your governance, your IP perimeter, your runbooks — not a third-party SI's.
Operating model from day one
Governance, escalation paths, performance reviews, knowledge transfer — all designed before the first hire.
Captive economics, consulting quality
Long-term cost structure of a captive (30–50% below SI offshore) with the rigor of a senior consulting practice.
Discuss a captive build →
DELIVERY MODEL · FOUR PHASES
01
4–6 WKS
Plan
Site selection · legal entity · governance design · hiring plan · operating model.
02
3–6 MO
Build
Hire architects · stand up tooling · transfer knowledge from existing teams · accelerator baseline.
03
6–12 MO
Operate
Ramp to autonomous operation · embed quality bars · Manizh transitions to advisory.
04
YEAR 2+
Scale
Expand bench · deepen capabilities · open additional sites · captive runs on its own rails.
END STATE
A captive that runs like a senior consulting practice — not a body shop. Your IP. Your governance. Your bench.
More from Manizh

The full Manizh practice.

Beyond the data and AI specialty, the Manizh practice carries six additional service lines for clients who need them. The same senior team, the same outcome-led economics.

01

Data Management

Master data, governance, lineage, quality frameworks.

02

Cloud & DevOps

AWS, Azure, GCP. CI/CD, IaC, observability, FinOps.

03

UI & UX Design

Design systems, prototyping, user research, accessibility.

04

Software Development

Full-stack applications, microservices, API design.

05

Quality & Testing

Test automation, performance, security, accessibility audits.

06

Web Development

Marketing sites, e-commerce, headless CMS, CMS migrations.

How we work

Discovery first. Always.

Every engagement starts with a structured discovery. We audit your environment, talk to your stakeholders, and write you a discovery report. If we are the right team for the work, we propose. If we are not — we tell you that too, and recommend who is.

01

Discovery & KT

Audit the estate, ingest tribal knowledge, map stakeholders, write the discovery report.

audit + report
02

Design & estimate

Target architecture, modeling standards, governance blueprint, pointed sprint backlog.

1 wk
03

Build sprints

Three two-week sprints: foundation, pipelines, then dashboards & polish. Sprint demos at the end of each.

6 wks
04

QA & UAT

Reconciliation against legacy, integration tests, UAT support, security review, performance pass.

2-3 wks
05

Launch & run

Cutover, hypercare, handoff to retainer. The same humans who built it operate it.

Ongoing
Tier-1 SIs sell methodology. Body shops sell hours. Manizh sells an outcome.
— How Manizh sees the market
Commercial model

Pick your structure. We are agnostic.

The build sits at the same dollar number whether you want certainty or flexibility. The retainer afterwards is optional.

SOW · OPTION A
Fixed Bid
Firm price
Quoted per engagement
  • Scope locked in signed Statement-of-Build
  • Phase deliverables tied to exit criteria
  • Change-order process for in-flight shifts
  • Manizh carries effort risk
SOW · OPTION B
T&M / NTE
Same ceiling
Pay only for hours used
  • Not-to-exceed cap matches Option A
  • Weekly burn-rate transparency
  • Sprint backlog agreed at sprint start
  • Team scales monthly with 10-day notice
POST-LAUNCH
Retainer
Run & enhance
Monthly · same team
  • Build team continues post-launch
  • Director coverage complimentary
  • 6-month minimum · 60-day exit clause
  • Task-level attribution & weekly demos

Need a one-off architecture review or vendor evaluation? Talk to us about a scoped advisory instead.

Resources · whitepapers

Field notes from the retail data trenches.

Three working papers from the Manizh practice on how to build production retail data infrastructure in Snowflake. Written for data engineers and architects who actually have to ship the work.

WHITEPAPER · 01
Building Retail Data Pipelines in Snowflake
A reference architecture for specialty apparel

Covers source-system inventory (POS, OMS, WMS, e-comm), medallion architecture for retail data domains (sales, inventory, customer, product), dbt patterns for SKU-level analytics, performance tuning for store-day-SKU grain, and FinOps for retail-scale Snowflake estates.

KEY TOPICS
  • · Medallion (bronze / silver / gold) for retail
  • · SKU-level dbt patterns & SCD-2
  • · FinOps for retail-scale Snowflake
28 pages · 12 min read Request access →
WHITEPAPER · 02
Connecting B2B Vendors to Snowflake
Wholesale, EDI & partner data pipelines

How to ingest wholesale partner data — B2B portals, EDI 850 / 856 / 810, vendor file drops, drop-ship feeds. Reconciliation across vendor systems, enrichment for downstream merchandising and finance, and operationalizing partner scorecards.

KEY TOPICS
  • · EDI 850 / 856 / 810 ingestion patterns
  • · Drop-ship vs. owned-inventory reconciliation
  • · Vendor scorecards & PO-to-cash visibility
22 pages · 9 min read Request access →
WHITEPAPER · 03
Snowflake + SAP for Apparel Analytics
Integrating PIM & ERP into your data estate

Patterns for connecting SAP MDG, SAP S/4HANA, and SAP CAR to Snowflake. PIM/master-data alignment, slowly-changing dimension handling for apparel attributes (SKU, color, size, season), and reconciliation against ERP finance.

KEY TOPICS
  • · SAP MDG & S/4HANA → Snowflake CDC
  • · SCD-2 for apparel SKU dimensions
  • · Finance reconciliation across systems
34 pages · 15 min read Request access →

All three whitepapers are gated — request access via the contact form and we send the PDFs directly. We do not put you on a marketing list, and we do not sell or share your information.

M.

The Manizh Practice

Boutique data & AI consulting
HQAshland, Massachusetts · USA
DELIVERYCoimbatore · India
FOUNDED2018
TEAMSmall. Senior. Stable.
About Manizh

A small studio with a long memory.

Manizh draws on Oriental wisdom — the name carries notions of grace, consideration, and deliberate craft, and that is what we are trying to be as a consulting practice. We are headquartered in Massachusetts; our delivery practice is in Coimbatore, India. The team is small and senior. The same humans who scope your engagement are the ones writing the code.

We started Manizh because the market kept offering clients a choice between "Tier-1 SI methodology theatre" and "rotating-bench staff augmentation." Neither answer is the answer.

Our thesis: a small team of senior architects compound faster than a bench of rotating consultants. Every engagement Manizh runs is structured to prove that thesis.

Schedule a 30-minute conversation →

Bring us your hardest data problem.

We will diagnose the real issue, propose a path, and tell you honestly whether Manizh is the right team. If we are not — we will point you at who is.

Book a call with Us →
Get in touch

Let's start with a conversation.

Tell us briefly about the work and we will reach out within one business day. No sales-development reps. No automated nurture sequences.

NEW BUSINESS

sales@manizh.com

PHONE

+1 (508) 656-0080 · US Office
+91 73388 71369 · India

HQ

162 Algonquin Trl
Ashland, MA 01721 · USA

DELIVERY PRACTICE

Coimbatore, Tamil Nadu · India