Evidence Signal
35+ signal types · 10-dimension deal health · 2,455+ tests · In Development
Project Deep Dive
Signal Intelligence Platform
Real-time deal health intelligence for Microsoft Dynamics 365 — detecting risks CRM data alone cannot show, built for the platform every major competitor ignored
Your CRM knows what happened. This platform knows what is happening — real-time deal health intelligence for Microsoft Dynamics 365.
Revenue intelligenceDeal health scoringMicrosoft Dynamics 365Compliance-first design
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.
CRM data tells you what happened last week, not what is happening right now. A deal marked 75% likely to close can be in serious trouble — champion stopped responding, key meetings cancelled, no proposal sent.
Every major competitor (Gong, Clari, Spotlight.ai) built for Salesforce first and retrofitted Dynamics 365 as an afterthought.
Signal Intelligence Platform is built natively for Microsoft Dynamics 365 — detecting deal health signals across CRM, email, calendar, and Teams to surface risks before they become losses.
The Build
What I built to solve it
A five-step intelligence pipeline: (1) Monitors Dynamics 365, Outlook, and Teams in the background — no new tools for reps. (2) Detects 35+ signal types using tiered processing. (3) Correlates signals across systems to surface patterns invisible in any single tool. (4) Scores deal health across 10 dimensions with configurable sales methodologies. (5) Delivers insights through Copilot, Teams, Dynamics, and API.
L1Surface AdaptersDeliver intelligence to any AI surface on demand
Serve
L2Query & SynthesisIntent classification, context loading, LLM synthesis
Read/Write
L3Entity Graph16-table PostgreSQL schema with row-level security
Score/Store
L4Scoring & Signal Processing5 pure-function scoring engines + tiered signal extraction
Ingest
L5Source AdaptersCRM, email, calendar, and call transcript ingestion
Click any node to explore details
Scoring engines
5 analysis models
Deal health (10 dimensions), deal priority (8 factors), task priority, confidence scoring, and entity matching — all deterministic and testable.
Signal types
35+ across 9 categories
Engagement drops, deal velocity, stakeholder coverage, qualification gaps, competitive signals, and more — detected from email, calendar, and CRM activity.
Data platform
16-table secure database
Deals, accounts, people, activities, signals, and scoring history — all with row-level security ensuring data isolation between customers.
Codebase
3,370+ lines · 21 modules
21 well-organized modules with strict type checking, automated quality gates, and comprehensive testing.
Value
What changed because it existed
This is the clearest evidence of practical value, system leverage, and execution quality.
Deal health scoring
10-dimension analysis
Scores deals across stakeholder engagement, qualification depth, competitive position, timeline risk, and 6 more dimensions — revealing the truth behind CRM probabilities.
Contact matching
Intelligent entity resolution
Automatically matches contacts across systems — handling name variations, title changes, and company moves so signals connect to the right people.
Smart alerting
4-stage notification management
Preference filters, quiet hours, deduplication, and daily caps prevent alert fatigue — sellers only see signals that matter.
Data honesty
Context quality assessment
The system knows when it has enough data to be confident vs. when data is thin — and tells the user, preventing false confidence.
Tech Stack
What it runs on
The stack matters here because it reflects design choices, constraints, and how the system was intended to scale or integrate.
Python 3.12 / FastAPIPostgreSQL with row-level security (Azure)Azure OpenAI (planned for LLM synthesis)Azure Container Apps (deployment target)Pydantic v2 strict validationPure-function scoring architecture (zero I/O in scoring layer)6 enforced architecture constraintsTiered signal processing (T1 heuristic → T2 lightweight LLM → T3 deep LLM)
Build Story
How the thinking unfolded
This is the reasoning path behind the output, not just the finished artifact.
01
Works wherever the seller works
Intelligence is extracted once and delivered to any tool — Microsoft Copilot, Teams, Dynamics, or custom dashboards. The platform is not another app to check; it brings insights to where sellers already are.
02
Scoring you can trust and test
All 5 analysis engines produce deterministic, testable results. The same inputs always produce the same scores — essential for enterprise trust. Evolved from proven SalesSidekick algorithms.
03
Compliance built from day one
SOC 2 audit logging, GDPR consent management, data erasure support, and row-level customer isolation designed into the database from the first line of code — not retrofitted.
Capability Signal
What this project demonstrates
Each project is a proof point. These are the capabilities it most clearly reveals.
Enterprise Data Architecture
16-table database with row-level security, customer isolation, and compliance infrastructure (audit logs, consent records, erasure support).
Intelligence Algorithm Design
5 analysis engines: 10-dimension deal health, multi-factor priority scoring, confidence assessment, and intelligent contact matching.
Quality Engineering
Comprehensive test coverage across all 21 modules. Strict type checking, automated linting, 6 quality gates, and zero-placeholder policy.
Product Architecture
Extracted and re-architected core algorithms from SalesSidekick into a standalone platform — designed to work with any CRM, delivered through any AI surface.
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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.