Evidence Signal
Built four times, about 5,000 tests, 50-user beta GO.
Project Deep Dive
SalesSidekick Legacy
SalesSidekick Legacy was the original full-stack enterprise sales SaaS monolith: 78 AI modules, 4,760+ tests, 295 automated pipelines, and roughly 100K lines of production code.
The first monster build, finished and deliberately thrown away.
Origin storyEnterprise SaaSOwn the middleLegacy
Flagship Case Study
What SalesSidekick Legacy Taught
SalesSidekick Legacy was the original full-stack sales SaaS monolith: a huge build that turned seller pain into product architecture and became the learning base for the current SalesSidekick.
SalesSidekick Legacy is the clearest example of how I think about AI products under real pressure: start with operational pain, encode domain expertise, build deeply enough to learn what is actually hard, then narrow the product into a cleaner current direction.
Where the pain lived
Enterprise sellers needed account context, CRM memory, call history, qualification logic, research, recommendations, and follow-through in one place.
What had to be true
The system had to reason iteratively, assemble context in parallel, carry seller memory, and show enough work that a rep could trust the output in a real deal.
Why it matters
It shows the learning curve behind the current work: generative coding, GTM workflow development, governance, testing, and the discipline to reduce a massive monolith into the current SalesSidekick.
Positioning Lens
SalesSidekick Legacy
Its value now is not that it is the current center of gravity. Its value is the lesson: Latif built the hard thing, learned where the scope was too broad, and extracted the sharper operating-system direction.
Signature product decisions
- Built the original product as a full sales SaaS platform instead of a thin assistant wrapper.
- Kept the valuable operating logic while moving the current direction into SalesSidekick.
- Encoded sales methodology directly into the system so the product reflects judgment, not generic summarization.
- Protected the platform with enterprise-style test coverage and human-in-the-loop checkpoints on high-stakes workflows.
The Problem
What problem was worth solving?
I build things to solve a specific pain, not to decorate a portfolio. This is the pressure that made this project necessary.
Before the current SalesSidekick product, Latif tried to build the whole seller operating system as one full SaaS product: CRM memory, account history, call context, sales methodology, research, recommendations, approvals, and follow-through in one platform.
That build was too broad to remain the current product direction, but it became the biggest practical learning curve in generative coding, agent workflows, enterprise GTM systems, test discipline, and product scope control.
The Build
What I built to solve it
SalesSidekick Legacy combined a 78-module AI reasoning system, parallel context assembly from 12 data sources, human approval gates, CRM and research workflows, a full React application, Azure infrastructure, and 295 automated pipelines. The current SalesSidekick product keeps the useful operating logic without making the original monolith the public product story.
Current status
SalesSidekick Legacy
SalesSidekick Legacy is the original sales SaaS platform and learning base. The current SalesSidekick brand carries the revenue-team product forward.
AI reasoning engine
78 modules
A dedicated AI system for understanding sales context, classifying what the rep needs, and assembling intelligence from multiple sources to produce recommendations.
Reliability
4,760+ tests
Comprehensive automated testing across backend and frontend — built to enterprise standards with near 1:1 ratio of test code to production code.
Automation
295 pipelines
Automated workflows handling data ingestion, CRM synchronization, and background processing — keeping the intelligence layer current without manual intervention.
Human approval gates
Built-in for high-stakes actions
The AI pauses and requests approval before executing strategic workflows — ensuring human judgment on consequential decisions. Data access enforced with row-level security per tenant.
Value
What changed because it existed
This is the clearest evidence of practical value, system leverage, and execution quality.
Product evolution
Current SalesSidekick successor
The build clarified what should become a narrower branded product: governed skills, owned memory, manager rollups, receipts, and correction loops.
Codebase
~100K lines production + ~90K lines tests
Enterprise-grade engineering with nearly as much test code as production code — the kind of reliability investment enterprise customers require.
User experience
142 source files / 79 components
A full React application that shows the AI's reasoning in real time — sellers can see what the system is doing and why, building trust in the recommendations.
Sales skills deployed
3 live, 10 in development
Strategic Account Analysis, Post-Call Intelligence, and Competitive Positioning in production. Each skill produces graded output with human review before high-stakes actions.
Microsoft integration
4 enterprise surfaces
Designed for Copilot, Teams, Dynamics 365, and Outlook — delivering intelligence wherever the seller already works, with Microsoft marketplace distribution planned.
Technical Layer
How the system is built
This is the implementation surface behind the work: the architecture choices, operating layers, integrations, and controls that make the project more than an idea.
Anthropic Claude
Claude for Work Team / Enterprise
LangGraph (78-module AI engine)
FastAPI + React
Azure Container Apps
Azure Durable Functions
Azure OpenAI / GPT portability
Application Insights
n8n (295 automated pipelines)
PostgreSQL + pgvector
Build Story
How the thinking unfolded
This is the reasoning path behind the output, not just the finished artifact.
01
The legacy build that taught the system
SalesSidekick was not a thin AI wrapper or a prompt template. It was the first attempt to build the entire enterprise seller operating system as one full SaaS monolith, with context, workflow memory, methodology, approvals, and recommendations built into the product.
02
Enterprise-grade reliability
~100K lines of production code backed by 4,760+ tests. The frontend shows 1,632 tests passing. This is disciplined engineering built to the standards enterprise buyers expect.
03
What became current SalesSidekick
The monolith proved the value of seller context and governed workflow, but it also showed that the sharper product is the current SalesSidekick: a revenue-team product with governed skills, owned memory, manager visibility, and correction loops.
04
Built from real seller pain
The product came from direct experience: too much admin, fragmented context, and tools built for management reporting instead of seller execution. Deal qualification (MEDDPICC), competitive analysis, and health scoring are built into the core logic — not bolted on as overlays.
Capability Signal
What this project demonstrates
Each project is a proof point. These are the capabilities it most clearly reveals.
AI Platform Architecture
Designed a full sales SaaS monolith and 78-module AI reasoning engine that assembles sales context, works through multi-step analysis, and produces recommendations with human approval gates on high-stakes actions.
Sales Intelligence Integration
Connected ~30 tools spanning research, CRM, communication, and analytics into a single intelligence layer. The AI chooses which tools to use based on what the rep needs.
Real-Time Context Assembly
12 data sources (CRM profiles, territory data, deal history, market intelligence, conversation memory, and more) assembled in parallel in under 20 seconds — with graceful handling if any source is slow.
Enterprise Architecture
Single API entry point serves web, Copilot, Teams, and Slack. Sales skills run as independent cloud functions. Tenant isolation, performance monitoring, and configuration controls built into the production stack.
Sales Methodology Automation
MEDDPICC deal qualification, competitive analysis, and deal health scoring (0-100) are encoded directly into the AI skills. Outputs distinguish verified facts, estimates, and hypotheses.
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Want to go deeper?
Ask the Latif AI Guide about the architecture, the commercial logic, or what this project says about how I approach hard problems.