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Unlocking Enterprise GenAI for Lido Labs

Transforming Lido Labs:
Accelerating Governed & Multi-Tenant GenAI
with AWS and Arhasi.

Moving from fragmented experimentation to an enterprise-grade AI foundation in 30–60 days.

Presented by Arhasi in partnership with AWS

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The Challenge

The GenAI Adoption Challenge — Navigating the AI Landscape

The Shift

Today Ad-hoc Experimentation
Value 01
01 Value & Velocity

Identifying Value & Balancing Goals

Technology leaders face immense pressure to ship AI features fast, but often struggle to balance immediate, low-hanging wins against foundational long-term transformational changes.

  • Executive pressure to deliver visible AI wins in every sprint cycle
  • Competing mandates: feature velocity vs. durable platform investment
Resources 02
02 Resources

Resource & Budget Constraints

Accurately forecasting infrastructure, API usage, and resource requirements is tough when teams operate in silos.

  • Infrastructure, API spend, and headcount tracked in separate systems
  • No shared view of total cost of ownership across AI initiatives
Governance 03
03 Governance

The Fragmented Baseline

Unmonitored usage of standalone tools (GitHub Copilot, individual OpenAI subscriptions, custom LLMs) creates governance risks, data leakage threats, and duplicated effort across direct-to-consumer verticals.

  • Shadow AI tools proliferate without centralized visibility or guardrails
  • Each DTC brand reinvents the same integrations and prompts independently
Target Governed Enterprise GenAI
Bedrock Multi-Tenant Guardrails

The 4 Phases of GenAI Maturity

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Trustworthy and Responsible AI
1

Measurement & Mapping

Evaluate organizational readiness against foundational AI capabilities.

  • Baseline readiness across brands and teams
  • Map capability gaps and priority investments
2

Trustworthy AI Establishment

Enumerate operating models, data isolation standards, and governance processes before scaling.

  • Define ops models and accountability
  • Establish isolation and policy controls
3

Design & Execution

Execute strict enforcement of guardrails, tenant boundaries, and access controls through an AWS-native foundation.

  • Multi-tenant Bedrock architecture
  • Guardrails, IAM, and tenant isolation
4

Transformation Rollout

Roll out large-scale use cases, enable developers, and establish operational readiness across all business units.

  • Scale governed use cases per brand
  • Enable developers with repeatable pipeline
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The Partnership

Co-Engineering Partnership — Turning Developers into Superheroes

Lido Labs Product & Domain
Arhasi Cloud & Governance
01 Fusion

Core Product Mastery Meets Cloud Depth

Two complementary strengths fuse into one co-engineering model — each partner owns what they do best.

Lido Labs What to Build
  • Owns what to build — DTC domain mastery, psychographic intelligence, and high-velocity campaign execution.
Arhasi How to Build
  • Owns how to build — production-grade AWS GenAI, serverless scale, and pre-engineered governance from day one.
02 Multiply

Force Multiplier — Amplify, Don’t Replace

Force Multiplier

Accelerates your team — removing governance barriers and infrastructure heavy lifting without displacing developer autonomy.

Bypassing Learning Curves

Skips months of trial-and-error with ad-hoc tooling by deploying directly on Bedrock — production paths, not prototypes.

The Lethal Combination

The Lethal Combination for AI Success

Speed of Operationalization POC to production in weeks — pre-built governance stacks eliminate rework.
Weeks
Hallucination Management Deterministic validation and structured outputs catch errors before customers do.
Validated
Trust Infrastructure PII guardrails, lineage, and compliance embedded from Day 0 — no release halts.
Day 0
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Transforming Lido Labs

Accelerate Governed and Multi-Tenant Capability with AWS and TrustHouse.AI

Consolidating fragmented AI usage into a single, enterprise-grade architecture across all direct-to-consumer brands within 30–60 days.

Centralized Governance & Isolation
Unified RAG & Data Integrity
Accelerated Execution & Scaling

Unified Control Plane

Eliminates “wild west” tooling (Copilot, ad-hoc OpenAI, custom LLMs) in favor of a single secure AI gateway.

Seamless AWS Data Integration

Connects Bedrock Knowledge Bases directly to Lido’s $90K+ RDS footprint (MySQL, SQL Server, MariaDB) and S3.

Bypasses AWS AI Friction

Overcomes lack of internal AWS AI/ML experience—and prior Amazon Q friction—without custom ML pipelines.

Strict Multi-Tenant Boundaries

Enforces tenant isolation across distinct portfolio brands (American Service Pets, Concealed Coalition).

Hybrid Data Ingestion

Simultaneously queries unstructured technical markdown docs/SOPs and structured relational databases.

Standardized Claude Operations

Consolidates all business unit workloads on Anthropic Claude hosted inside Lido’s private AWS environment.

Brand-Specific Guardrails

Custom context controls and safety rules for each distinct consumer brand and customer-facing chatbot.

Provenance & Context Verification

TrustHouse.AI profiles data sources to eliminate hallucinations and ensure full decision auditability.

Governed MCP Connectors

Safely expands third-party Model Context Protocol integrations with real-time risk monitoring.

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Use Cases

Production Workloads — Internal RAG & Customer Chat

From Brief
Use Case 1

Enterprise Knowledge Engine

Internal RAG
Problem & Scope

Lido’s internal risk assessment generated hundreds of documents — but they sit disconnected from live operational data across a decade-old portfolio of DTC brands, custom software platforms, and AWS-hosted production systems.

Complex Data Integration

Connect Bedrock Knowledge Bases to Lido’s $90K+ Amazon RDS footprint (MySQL, SQL Server, MariaDB) and S3 — unifying GitLab CI/CD-deployed services, technical markdown, and SOPs in one hybrid query layer.

Isolation & Context Retrieval

Enforce metadata filtering and multi-tenant boundaries across American Service Pets, Concealed Coalition, and Nocturnal Enterprises — so teams only retrieve data relevant to their business unit.

Live Operational Bridge

Bridge static risk reports and compliance docs with live CRM, call-center, and subscription data — giving employees one natural-language interface to institutional knowledge and current system state.

From Brief
Use Case 2

Multi-Brand Customer-Facing Chat

Claude on Bedrock
Problem & Scope

Lido’s brands serve thousands nationwide — from HIPAA-compliant ESA/PSA telehealth at American Service Pets to CCW training at Concealed Coalition — but phone and email support (Mon–Fri, 8AM–7PM CST) can’t scale without governed, brand-aware AI.

Brand Portfolio Personalization

Deploy tailored chat across ASP (housing & travel ESA letters), Concealed Coalition (state-specific firearms training), and Nocturnal brands including Home Bidz and Targeted Careers — each with distinct tone, FAQs, and compliance guardrails.

Claude Standardization

Replace fragmented Copilot, ad-hoc OpenAI, and custom LLM tooling with Anthropic Claude on private Bedrock — commercial-model UX with all customer PII retained inside Lido’s AWS environment.

Agentic Integration

Use Agents for Bedrock to handle subscription changes, training enrollment, and secure portal navigation — escalating to human agents when needed, with TrustHouse.AI provenance and governed MCP connectors to CRM systems.

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Use Cases

Secondary Workloads — Future Roadmap

Proposed
Use Case 3

Campaign & Ad Ops Automation

Reporting & Content Generation
Problem & Scope

Lido’s omni-channel ecosystem runs paid media across Meta, TikTok, and Google for a multi-brand DTC portfolio — but performance reporting, creative iteration, and ad ops remain largely manual.

Automated Performance Reporting

Consolidate CPA, ROAS, CPM, and LTV from ad platforms into unified dashboards — replacing manual weekly reports with Bedrock-generated executive summaries.

AI Content Generation

Generate ad copy, landing page variants, and creative briefs aligned to brand guidelines across American Service Pets, Concealed Coalition, and partner properties.

Campaign Orchestration

Use Agents for Bedrock to automate bid adjustments, budget pacing, and cross-platform launches — with human-in-the-loop approval before spend changes go live.

Proposed
Use Case 4

Predictive Lead Scoring

Audience & Programmatic Bidding
Problem & Scope

Lido already applies data science to optimize acquisition across real estate, career, concealed carry, and support-pet brands — extending ML to lead scoring and programmatic bidding is a natural next phase.

Audience Segmentation

Train models on first-party CRM and web analytics to cluster high-intent prospects by vertical, geography, and lifetime value across Lido’s brand portfolio.

Programmatic Bid Optimization

Deploy SageMaker models to adjust Meta and Google bids in real time against CPA and ROAS targets — supporting media-buy portfolios managing $100K+ monthly spend per brand.

Attribution & Model Governance

Integrate with Google Tag Manager and Microsoft BI attribution pipelines; version models and monitor drift across multi-brand conversion funnels.

Proposed
Use Case 5

Rogue Rabbit Content Automation

Web · SEO · Email · Social
Problem & Scope

Rogue Rabbit Media — Lido’s in-house creative agency — delivers full-service marketing for portfolio brands and external clients, but web, SEO, email, and social production workflows don’t yet scale with AI.

Web Design & SEO

Auto-generate conversion-optimized page copy, meta tags, and blog drafts aligned to Rogue Rabbit’s SEO standards — driving qualified traffic through authoritative keywords and PPC support.

Email Journey Automation

Build A/B-tested nurture sequences from CRM segments — matching Rogue Rabbit’s data-driven email approach to cultivate loyalty and move leads through the funnel.

Social Content at Scale

Produce platform-specific posts for TikTok, Instagram, Facebook, and YouTube — with brand voice controls tailored to each client niche and pop-culture relevance.

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Use Cases

Prioritization Matrix

0/5 prioritized
Impact ↑
Complexity →
1

Enterprise Knowledge Engine

Unify internal docs and live AWS data into a single natural-language query system for employees.

From Brief
8/9
3/9

2

Multi-Brand Customer-Facing Chat

Deploy governed, brand-tailored Claude chat across Lido’s multi-brand customer support footprint.

From Brief
9/9
6/9

3

Campaign & Ad Ops Automation

Automate campaign reporting, ad copy generation, and cross-platform ad ops workflows.

Proposed
6/9
5/9

4

Predictive Lead Scoring

Apply ML-driven lead scoring and programmatic bid optimization across DTC acquisition funnels.

Proposed
7/9
8/9

5

Rogue Rabbit Content Automation

Scale Rogue Rabbit’s web, SEO, email, and social content production with AI automation.

Proposed
5/9
4/9

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Partnership Value

The Arhasi Advantage & TrustHouse.AI

Pull pre-engineered Bedrock blueprints into Lido’s AWS environment in days — while TrustHouse governs every model decision behind the scenes.

Arhasi Partner Advantage

Pre-Engineered Library
Infrastructure-as-Code Blueprints

Ready-to-deploy Terraform and CloudFormation templates for Bedrock Knowledge Bases, Agents, Guardrails, and multi-tenant RAG — compatible with Lido’s GitLab CI/CD pipeline and Amazon RDS footprint.

Rapid Blueprint Deployment

Skip months of foundational cloud engineering — deploy pre-tested hybrid SQL + vector RAG and Claude agent architectures in days, not quarters, using patterns validated on AWS Bedrock.

AWS Funding Support

Potential qualification for AWS MAP and partner program funding — now expanded to AI new builds — to offset Bedrock implementation and co-engineering costs.

arhasi.ai — Blueprint Library

TrustHouse.AI

Managed Trust Layer & Governance POC
Fully Funded POC
TrustHouse.AI

Enterprise AI trust infrastructure — extends the AWS environment Lido has already vetted, without replacing Bedrock.

Fully Funded POC & Access

Includes 12 months of complimentary TrustHouse.AI on AWS alongside a fully funded proof of concept — moving AI from pilot purgatory to production in weeks with governance built in.

Complete Decision Provenance

Trace every model prompt, RAG retrieval context, and agent decision to its source — append-only audit trails complementing Bedrock invocation logging and agent trace events.

Zero Data Leakage Assurance

Automated data profiling and AI Risk Engine guardrails enforce brand segregation across ASP, Concealed Coalition, and Nocturnal — blocking hallucinations before outputs reach end-users.

trusthouse.ai
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Execution

Proposed Execution Roadmap — 6-Week Blueprint

6 Weeks

From fragmented experimentation to a production-ready, fully governed AWS AI platform — in six weeks.

Wk 1
Phase 01

Discovery & Selection

Review technical architecture and select target AWS blueprints from the Arhasi solution library.

Wk 2–3
Phase 02

TrustHouse POC

Activate 12-month TrustHouse.AI access, deploy the Bedrock control plane, and set up multi-tenant data isolation.

Wk 4–5
Phase 03

Use Case Acceleration

Build out the internal Knowledge Base (RAG) and customer-facing Claude chat prototypes.

Wk 6+
Phase 04

Production Handover

Hand over fully governed, automated AWS pipelines to Lido developers to scale independently.

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Next Steps in the AI Transformation Journey

Let’s Build Lido’s GenAI Foundation Together

Actionable Next Steps

  1. Finalize NDA & SOW

    Execute the Non-Disclosure Agreement and Statement of Work to formalize the partnership.

  2. Align on Scope & Team

    Confirm scope, roles, and team involvement for the 6-week execution roadmap.

  3. Kick Off AI Roadmapping (AIR)

    Launch the AI Roadmapping deep dive session with AWS and Arhasi technical leads.