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Simplify Money Intelligent Advisory System

Cutting Generative-AI Operating Costs 40 % and Boosting Click-Through Rates 22 % with Steady Rabbit’s Core-Flex
Micro-GCC Delivery Model

Client
Simplify Money
Mission

Democratise high-quality financial advice using generative AI

Pre-Engagement State

Python Flask API, OpenAI completions, MongoDB

Simplify Money

Simplify Money
Case Study

Executive Summary

Simplify Money, a Silicon-Valley FinTech focused on mass-affluent millennials, offers personalised financial guidance through an AI chat and portfolio-insights engine. Early traction was promising—DAU grew 180 % in three months—but infrastructure bills spiked and response latency crept past two seconds. Investors set aggressive OKRs: halve model spend, improve recommendation click-through by double digits, and get SOC 2 readiness—all inside six months.

Steady Rabbit deployed a Core-Flex Micro-GCC squad and, in 11 sprints, delivered:

  • A LangChain + LangGraph serverless backend on AWS Lambda that cuts GPT token spend 40 %
  • Agentic workflows that raised personalised-advice CTR from 4.6 % → 5.6 % (+22 %)
  • An RAG (Retrieve-and-Generate) pipeline sourcing from 60 k docs with p95 latency 660 ms (-2.1 s → 660 ms)
  • Real-time cost-governor logic to auto-downgrade temperature & context length under load
  • SOC 2 controls and evidence automation—audit passed with zero major findings
  • Predictable delivery: 96 % sprint adherence, zero P0 incidents in first 90 days post-launch

The cost savings alone extended Simplify Money’s runway by seven months and helped close a US $13 M Series A at a 35 % valuation premium.

Client Profile & Business Context

  • Client
    Simplify Money

    San Francisco–based FinTech

  • Founded

    2022

  • Mission

    Democratise high-quality financial advice using generative AI

  • Pre-Engagement State

    Python Flask API, OpenAI completions, MongoDB

  • Pain Points

    GPT-4 costs ballooning, latency > 2 s, no SOC 2 program

Simplify’s early MVP resonated with users—portfolio Q&A, goal plans, and daily “Money Morning” insights. Yet every 10 k new users added ≈ US $18 k/month in GPT costs, threatening unit economics. CTO needed a partner that could optimise AI spend without degrading recommendation quality and build compliance foundations in parallel.

Problem Statement / Key Challenges

Escalating LLM Costs

Challenge

GPT-4 tokens ~$0.06/1k; Monthly bill > US $85 k

Stakes

Burn rate unsustainable; runway < 9 m

High Latency

Challenge

p95 response 2.1 s

Stakes

Drop-off in chat engagement; NPS slipping

Cold-Start Knowledge

Challenge

Generic responses when context missing

Stakes

CTR on advice cards stagnant 4.6 %

Compliance Debt

Challenge

No SOC 2 controls

Stakes

Enterprise channel partners on hold

Aggressive Timeline

Challenge

6-month Series A deadline

Stakes

Delay = down-round funding

Our Approach

Micro-GCC Squad Blueprint

Layer
Roles
Mandate
Core (6)
Squad Lead/PM, 2 Serverless-Python Engineers, Data Engineer, DevOps/SRE, QA Automation
Re-architect backend, optimise LLM usage, enforce compliance
Flex (2)
AI Optimisation Scientist, SOC 2 Security Architect (CISSP)
High-risk spikes: LangGraph agent design, control mapping
Buffer (1)
Shadow Python Engineer
PTO/attrition insurance—funded by Steady Rabbit

Shift-Left Governance

  • 7 Plan-Left gates on every Jira story (Persona, Acceptance, Risk, Arch Sketch, Est., SteadCAST capacity, Test Note)
  • SteadCAST dashboards surface Risk-High WIP %, velocity drift daily
  • 30-min weekly steering with CTO + Head of Product—no surprises

Discovery Sprint 0 (Weeks 1–2)

  • Chat Journey Mapping – prompt shapes, cost hotspots, user churn points
  • Architecture North Star – RAG pipeline (DynamoDB + S3 vector store) → LangGraph agents → cost governor layer → Lambda front door
  • North-Star KPIs – token cost / active user –40 %, p95 latency ≤ 800 ms, advice CTR +15 %, SOC 2 readiness by Week 20

Outcome: Backlog sized at 105 SP/sprint; launch fixed for Week 22.

Solution Delivered

Serverless LangChain + LangGraph Core

  • AWS Lambda (Python 3.11) runs RAG+agentic workflow; warm pool via SnapStart
  • Step Functions orchestrate multi-step ReAct agents—planning, tool selection, answer synthesis
  • p95 end-to-end latency 660 ms (was 2.1 s)

Cost Governor

  • Middleware inspects remaining context & forecast token; auto-downgrades model (GPT-4 → GPT-3.5) or truncates coT when cost > $0.028/response
  • 40 % month-over-month token cost reduction

Retrieval Pipeline

  • User docs + public finance corpus embedded via bge-base-en in SageMaker GPU Spot; vectors stored in pgvector on Aurora
  • Reranker (cross-encoder) boosts citation accuracy to 94 %

Personalised Recommendation Engine

  • Feature store (FeatureBase) feeds risk-profile, goals, cash-flow into LangGraph planner
  • Advice cards click-through 4.6 % → 5.6 % (+22 %)

Observability & FinOps

  • Lambda-Powertools, OpenTelemetry, cost allocation tags; real-time Grafana board
  • Alerts when daily token spend > US $2 k; auto-suspend heavy users

Compliance & Evidence Automation

  • AWS ControlTower baseline, GuardDuty, IAM Analyzer; audit artefacts auto-archive to immutable S3
  • SOC 2 auditor: zero high findings; final report issued three weeks before Series A roadshow

Execution Journey

Sprint
Deliverables
KPI Shift
Predictability
Sprints 0
Discovery, backlog, threat model
Baseline cost $0.045/msg
100 % gates
Sprints 1
Lambda baseline, SnapStart PoC
Latency 2.1 s → 1.3 s
Risk WIP 17 %
Sprints 2
Vector store, bge embeddings
Latency 1.3 s → 880 ms
Buffer unused
Sprints 3
Cost governor v1, model swap
Token cost –21 %
Flex AI 16 h
Sprints 4
LangGraph agents, Step Functions
CTR 4.6 % → 5.1 %
No slip
Sprints 5
Reranker, citation links
Citation accuracy 77 % → 94 %
Hot-fix 0
Sprints 6
Feature store, personalised prompts
CTR 5.1 % → 5.6 %
Budget +4 %
Sprints 7
SOC 2 controls, audit scripts
Coverage 65 % → 94 %
Flex Security 24 h
Sprints 8
FinOps dashboard, auto alerts
Daily spend –35 %
--
Sprints 9
Blue/green Lambda, load test 5×
p95 880 ms → 660 ms
--
Sprints 10
Auditor walk-through, GA launch
Cost –40 %, latency 660 ms
Delivered 2 days early

Buffer engineer filled in when a serverless dev had appendicitis (Sprint 6)—velocity dip 0 SP.

Business Outcomes & Impact

LLM token spend –40 %, extending runway 7 months

p95 latency 2.1 s → 660 ms (3.1× faster)

Advice CTR 4.6 % → 5.6 % (+22 %) boosting upsell revenue projection by US $1.3 M/year

Citation accuracy 94 %; user trust & share rate +19 %

SOC 2 Type I report issued 3 weeks early; unlocked enterprise reseller deal (US $2.8 M ARR)

Support tickets –32 % (fewer generic answers & timeouts)

Series A US $13 M closed at 35 % higher valuation citing cost discipline & compliance readiness

Predictability premium (~8 % rate uplift) paid back in one sprint by preventing a projected three
-week slip valued at US $0.9 M in lost ARR.

Why Steady Rabbit?

Core-Flex Micro-GCC

AI optimisation & security SMEs within 48 h; Buffer bench erased PTO risk

SteadCAST Predictability

96 % sprint adherence across 11 sprints

Shift-Left Governance

Seven Plan-Left gates cut re-work 38 % with < 2 h overhead/sprint

Gen-AI & FinOps Depth

Edge prompt-engineering, LangGraph agents, real-time cost governor

Outcome-Linked Engagement

KPIs (cost, latency, CTR, audit pass) tie to squad incentives—no vanity metrics

Transparent Partnership

Weekly demos, Slack warroom, open burn charts—zero surprises

Client Testimonial

Steady Rabbit

CTO & Co-Founder

Simplify Money

Token spend down 40 %, answers twice as fast, SOC 2 done early—Steady Rabbit nailed every metric. Their Core-Flex model turns uncertainty into clockwork