Shepherd · Solutions
Case Studies

A few projects we're proud of.

DealDocket is our own product. The other write-ups are representative builds: composites of the engagement patterns we run, with illustrative figures, so no client name, system, or number ever appears here. For a working example you can click through, use the interactive demo suite.

Live demo suite: drill-through, row-level security, and a glossary on synthetic data
Our productAI Solutions

DealDocket.ai, our proprietary AI real estate investment platform

Shepherd Solutions (proprietary product) · Real estate / private investment · Built and maintained in-house

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DealDocket.ai is our own AI-driven real estate investment platform. It automatically sources deals, runs the underwriting, and stress-tests scenarios, replacing weeks of manual work per deal.

Challenge

Real estate investors hunt deals manually, build one-off Excel underwriting models, and re-run scenarios from scratch every time financing or assumptions shift. Good deals slip through and capital sits idle. We built DealDocket to fix it, and to prove what's possible when AI is at the core of a product.

Approach

  • Deal sourcing pipeline that ingests MLS, off-market, wholesaler, and public-record feeds, then de-dupes and scores against the investor's buy box
  • AI-assisted underwriting engine: cap rate, cash-on-cash, IRR, BRRRR, and value-add models with sensitivity analysis baked in
  • Scenario engine for financing, rate, vacancy, capex, exit, and market shocks, fully reproducible with stored assumptions
  • Natural-language assistant on top of the deal database, ask 'compare these three deals at 7% rates with 5% vacancy' in plain English
  • React + TypeScript UI with map view, deal pipeline, and one-click LP-ready memos generated from the model output
ReactTypeScriptNode.jsPythonPostgresOpenAI / ClaudepgvectorMapbox

Outcomes

Days → minutes
to underwrite a new deal end-to-end
10x+
deals evaluated per analyst per week
1 click
LP-ready memos from any scenario
RepresentativeAI Solutions

AI-driven SaaS website with self-serve billing baked in

Representative B2B SaaS build · SaaS · 6-10 weeks

Designed and shipped an AI product website where prospects can chat with an AI assistant, sign up, try the product, and start paying, all without ever leaving the site or talking to sales.

Challenge

The team needed to launch an AI product fast, but every off-the-shelf option required stitching together AI, billing, auth, and onboarding from scratch. Months of plumbing before a single customer could pay.

Approach

  • Marketing → product → billing flow with AI woven through every step
  • Embedded AI assistant that answers product questions and qualifies leads on the marketing site
  • Stripe Checkout and customer portal for self-serve subscriptions, plan upgrades, and invoices, no manual quoting
  • AI-powered onboarding that personalizes the first-run experience based on the customer's stated use case
  • Auth (email magic links + SSO option), tier-based access control, and usage metering tied to billing
  • Production deploy on Vercel with cost and quality monitoring from day one
ReactNext.jsTypeScriptStripeOpenAI / ClaudePostgresVercel

Outcomes

Weeks
from kickoff to a site that can take payment
Self-serve
sign-up, trial, and subscription with no sales touch
Client-owned
code, prompts, and billing logic in their repo
RepresentativePower BI

Unified Power BI platform for a field-services operator

Representative Operations-tier build · Field services · 8-12 weeks

Replaces a pile of departmental spreadsheets and siloed reports with one governed semantic model and a suite of executive, dispatcher, and technician pages.

Challenge

Operations leaders were piecing together weekly numbers from disparate spreadsheets. Definitions of 'revenue' and 'completed job' differed by team, making leadership meetings about reconciling, not deciding.

Approach

  • Workshops with ops, finance, and field leadership to agree on metric definitions
  • Star-schema model in Power BI with dataflows from the dispatch system, ERP, and CRM
  • Row-level security so franchise managers only see their territory
  • Executive, dispatcher, and technician dashboards plus a paginated report for invoicing
Power BIPower QueryDAXSQL ServerAzure Data Factory

Outcomes

One model
ops, finance, and field agree on the numbers
By territory
row-level security for franchise managers
Hours, not days
to prepare the weekly ops review
RepresentativePower BI

CFO reporting pack that replaces the month-end Excel build

Representative Finance (Core-tier) build · Multi-entity professional services · 6-10 weeks

A governed finance model and CFO pack covering P&L, cash, and AR aging, with entity- and account-level drill-through replacing a hand-built monthly Excel pack.

Challenge

Finance was rebuilding the same Excel-based pack every month from three GL exports. Errors slipped in, the CFO couldn't trust the variance commentary, and the team was burning out at month-end.

Approach

  • Standardized chart of accounts mapping across entities in dataflows Gen2
  • Star-schema model with calculation groups for time intelligence and currency
  • Executive P&L, cash, and AR dashboards with drill-through to entity and account
  • Deployment pipeline (dev → test → prod) with a documented release checklist
  • Recorded handoff and a measure-authoring guide for the FP&A team
Power BIMicrosoft FabricDataflows Gen2DAXCalculation Groups

Outcomes

Days faster
month-end pack, built from the model not by hand
Traceable
every variance drills to the GL lines behind it
Glossary
one definition of margin, EBITDA, and DSO across entities
RepresentativePower BI

Performance rescue: a sales model rebuilt to a tuned star schema

Representative Performance Rescue engagement · Multi-location retail · 2-6 weeks

The flat-table model that times out and fails refresh, rebuilt as a star schema with incremental refresh and rewritten measures, so the team can add to it again.

Challenge

A single wide table fed by a dozen queries. Refreshes fail, visuals take tens of seconds, and nobody wants to touch a measure for fear of breaking it.

Approach

  • Vertipaq analyzer audit and a performance baseline against business-critical visuals
  • Refactored to a star-schema with dimension tables and incremental refresh
  • Rewrote 40+ DAX measures using variables and calculation groups
  • Added RLS for store managers and district leaders
  • Power BI Projects (.pbip) in Git with a PR review checklist
Power BIDAX StudioTabular EditorGit / .pbipSQL Server

Outcomes

Sub-second
target for business-critical visuals
Smaller model
Vertipaq-tuned columns and incremental refresh
Baselined
refresh and load times recorded at handoff
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