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Fictional client sample

Technology Recommendations

Reference stack, vendor shortlist, and build-vs-buy rationale for implementation readiness.

Meridian Financial Services • February 2026 • Confidential

01 Stack Blueprint

Recommended Technology Stack

The target stack is designed to maximize reuse of existing investments while adding focused AI capabilities.

Foundation

Azure OpenAI Service

Leverages existing Microsoft agreement, compliance controls, and regional data options.

Orchestration

LangChain + Semantic Kernel

Flexible orchestration patterns with strong ecosystem support and Microsoft interoperability.

Vector Database

Pinecone

Managed scale, production reliability, and low operational overhead for retrieval workloads.

Observability

LangSmith + Azure Monitor

LLM traceability paired with existing enterprise monitoring practices.

Data Platform

Snowflake (existing)

No migration requirement and strong integration path to AI services.

ML Platform

Dataiku or H2O.ai

Governed low-code environment for business-aligned model development.

02 Build vs Buy

Sourcing Decision Matrix

CategoryBuildBuyRecommendation
LLM FoundationHigh cost, 18+ months, very high riskModerate cost, weeks to launch, low risk
BuyAzure OpenAI
ML PlatformHigh cost, 12+ months, high riskModerate cost, 1-2 months, low risk
BuyDataiku / H2O.ai
Use Case AppsModerate cost, 3-6 months, medium riskHigh recurring cost, variable fit
BuildCustom applications
Data PipelineLow cost, 1-2 months, low riskModerate cost, 1 month, low risk
HybridExtend existing ETL
Governance ToolingHigh cost, 6+ months, high riskModerate cost, 2-3 months, low risk
BuyAlation / Collibra

03 Vendor Shortlist

Top Vendors by Capability Layer

ML / AI Platform

Dataiku

9/10

Strong governance UX and business adoption path

$150K-$250K/year

H2O.ai

8/10

AutoML and open-source-friendly ecosystem

$100K-$180K/year

AWS SageMaker

6/10

Deep AWS-native stack for cloud-first teams

Pay-as-you-go

Data Catalog and Governance

Alation

9/10

Best discovery experience and Snowflake alignment

$80K-$120K/year

Collibra

8/10

Enterprise-grade policy and control tooling

$100K-$150K/year

Azure Purview

7/10

Native Azure integration and lower overhead

$30K-$60K/year

LLM Observability

LangSmith

10/10

Deep tracing purpose-built for LangChain flows

$2K-$5K/month

Arize AI

8/10

Model monitoring with strong drift analytics

$3K-$8K/month

Datadog

7/10

Unified stack leveraging existing telemetry

Incremental add-on

04 Integration Model

Reference Integration Flow

Existing Systems

Core BankingCRM (Salesforce)Wealth Platform

↓

Central Data Layer

Snowflake (existing)

↓

New AI Platform Layer

Dataiku/H2O.aiAzure OpenAIPineconeLangSmith

↓

Use Case Applications

Loan Pre-ScreeningFAQ Assistant

Full Recommendations Continue

This sample covers the first stack and vendor sections. The complete deliverable includes procurement assets, implementation sequencing, and detailed cost models.

  • RFP templates and weighted vendor scorecards
  • Three-year total cost of ownership modeling
  • Dependency-based implementation sequencing
  • Skill-gap analysis and hiring/upskilling plan
  • Security and compliance control mapping
  • POC playbooks for final vendor validation