Originally published on LinkedIn Pulse by Matthew Powers, CTO at Tatango.
Adapted for the Tatango blog.
At Tatango, AI isn’t a buzzword — it’s the backbone of how we help nonprofits connect with supporters at scale, across channels, and with empathy. Over the past year, AI has reshaped what and how we build – from engineering workflows to redefining what velocity means for product teams.
This post brings those threads together: how small, sticky AI features evolved into a full-scale agentic RAG system – a next-generation retrieval-augmented architecture with reasoning and autonomy – and how that system is quietly transforming what’s possible for nonprofits using Tatango.
Learn more about our AI-powered fundraising platform.
Starting Small: AI That Earned Its Place
Our first AI features weren’t glamorous. They began as quiet product enhancements built to remove friction, not add flash.
It started with AI Message Composer, helping users draft compliant, on-brand messages faster. Then came Auto-Generated Fallbacks, intelligently converting MMS to SMS when carriers didn’t support rich media.
From there, we built a behavioral scoring engine to measure each supporter’s engagement and giving patterns. That foundation evolved into Power Segments — AI-driven engagement tiers that classify every supporter as VIP, Highly Engaged, Passive, or At-Risk. We introduced AI Link Generation, automatically creating trackable short links and calls-to-action in real time. Finally, we launched Smart Replies, leveraging the same behavioral insights to help nonprofits manage one-on-one conversations at scale.
Each step built on the last — from AI that assisted with content, to AI that understood behavior, to AI that could engage donors directly. And what stood out most wasn’t how well these features worked — it was how quickly they were adopted.
When AI removes friction and gives people back time, adoption follows naturally.
Making the Inbox Smarter, Not Just Faster
As Smart Replies took shape, we realized intelligence shouldn’t stop at generation — it should extend to how information is surfaced and shared. The Tatango Inbox is where this vision comes to life.
The Inbox is more than a communication tool; it’s a relationship hub. Nonprofits thrive on personal connections with supporters — from thank-you messages to campaign follow-ups. Managing that level of personal engagement across tens of thousands of supporters is incredibly hard. That’s exactly what Inbox was designed to simplify.
- Filters categorize conversations by message type, sentiment, donor behavior, and Power Segment.
- Notifications summarize key changes — alerting teams when donor engagement spikes or sentiment shifts.
- Digest reports turn thousands of inbound replies into clear, actionable insights.
Together, these capabilities help nonprofits maintain authentic, one-to-one relationships at scale — the kind that drive trust, loyalty, and long-term giving. These tools don’t just automate; they amplify. Inbox ensures every supporter feels heard, while every organization gains leverage from AI.
Building Our First Production-Grade Agentic AI System
The insight that AI works best when it’s contextual and invisible led us to build our first production-grade RAG (Retrieval-Augmented Generation) system — the foundation behind Smart Replies.
How It Works
- Data Foundation: Every interaction lives in our data warehouse, where models score each supporter’s engagement.
- Dynamic Retrieval: Incoming replies trigger contextual queries that build prompts using engagement history and sentiment.
- Generation & Evaluation: Models draft replies tuned to each nonprofit’s tone and are benchmarked through custom evaluation frameworks.
- Observability: With Datadog’s LLM Observability, we track latency, cost, and drift just like any other service.
What started as a single feature became a RAG backbone powering multiple workflows — Smart Replies, donor sentiment classification, predictive segmentation, and more.
Why Agentic AI Matters
While we use retrieval-augmented methods, what we’ve built goes far beyond a traditional RAG implementation. These are agentic AI systems — capable of observing, reasoning, and taking action across multiple steps in real time.
Agentic AI gives us a unified architecture that retrieves truth from our own data before generating anything new. In fundraising, where accuracy, tone, and trust are everything, that grounding ensures every AI output is helpful, safe, and on-brand. It’s not just better technology — it’s a better donor experience.
The Power of Observability and Evaluation
Building reliable AI comes down to feedback loops. We treat our LLMs like microservices, tracking token usage, latency, and error rates to spot drift quickly. We A/B test prompt formats and retrieval weights against real human data. These loops make AI measurable, repeatable, and improvable — the same engineering mindset that’s powered every generation of machine learning.
Doing More with Less
Every nonprofit we serve is trying to stretch limited resources further. That’s why this matters: the same automation that saves an hour of staff time can translate into hundreds of extra donor conversations each week.
Inbox and Smart Replies now help fundraisers manage thousands of one-to-one conversations — authentically, efficiently, and at scale. AI isn’t replacing anyone. It’s amplifying the people already making impact.
From Experiments to Ecosystem
In less than a year, Tatango’s AI evolution has moved from isolated features to a connected ecosystem — and the same mindset has transformed our own software development lifecycle (SDLC).
Our AI-powered SDLC now underpins how we build, test, and ship innovations. From code generation and testing to observability and release automation, AI acts as a co-pilot across every workflow.
That culture of dogfooding before delivering has become part of Tatango’s DNA. The same systems that support our customers are the ones we rely on internally — creating a virtuous loop between innovation and execution.
The Road Ahead
In 2025 and beyond, our roadmap expands beyond SMS and MMS to include RCS and WhatsApp, bringing AI-powered personalization to even richer omnichannel experiences.
We’re also deepening our investment in AI observability and governance, ensuring transparency, auditability, and responsible scaling as these systems become core to how nonprofits engage their audiences.
That’s what “AI at scale” looks like for us: not just faster, but smarter, compliant, and deeply grounded in real data.
Closing Thoughts
AI started at Tatango as a curiosity — a way to automate the little things. Today, it’s a foundational layer that touches every part of our product, from how we write code to how nonprofits communicate with their supporters.
If AI saves people time, they’ll use it. If it makes them better at their jobs, they’ll love it. And if it earns their trust, it becomes invisible — just part of how things work.
That’s the goal. AI not as a novelty, but as a quiet system of leverage, helping nonprofits do more good, one smart reply at a time.
Want to Learn More?
Explore how Tatango’s AI capabilities are helping nonprofits raise more and connect more personally with their supporters. Connect with Tatango.
