Building the “Full-Length Film” of Revenue: Inside Aviso’s Time-Series Foundation
Sep 8, 2025
You wouldn't drive a car forward by only looking in the rearview mirror, right? Yet, many sales teams run their entire strategy by looking at last quarter's results. They're stuck reacting to the past instead of proactively shaping the future. This reactive approach to sales is risky, outdated, and leaves money on the table.
What if you had a GPS for your revenue? A system that not only tells you where you are but also predicts traffic jams, suggests faster routes, and ensures you reach your destination—your sales target—on time. That’s precisely what Aviso’s AI-powered Time-Series database does for your business.
What's a Time-Series Database, Anyway?
A Time-Series database is simply a system designed to track changes in data over time. Think of it like a fitness tracker for your business. Your Fitbit records your heart rate every second to show you trends, patterns, and progress. Similarly, Aviso’s Time-Series platform captures every single change in your revenue operations—every deal update, every forecast modification, every customer interaction—and timestamps it.
Unlike a traditional database that just stores the current snapshot (like a single photo), a Time-Series database stores the entire movie. It knows not just that a deal's value is $100k today, but that it was $50k last week and $25k the week before. This continuous stream of historical data is the secret ingredient to unlocking incredibly accurate predictions.
Why CRM Snapshots Fall Short
Traditional CRM systems treat deals and interactions as simple “point-in-time” records. Update a field—like close date or deal amount—and the old value disappears. This data model strips away the temporal evolution of a deal:
Was the close date moved multiple times?
Did the deal’s size shrink after a critical buyer meeting?
How do rep-level behavioral histories affect the likelihood of close?
Without temporal context, AI tools can’t reason about momentum, causality, or trend—the essentials of intelligent forecasting and prescriptive insight.
Most vendors simply layer LLMs and GenAI on top of that static data. The problem? Without time-aware context, LLMs are just guessing. They lack a built-in memory of how things change, so they can’t reason about trends, causality, or momentum.
A Time-Series Database Engineered for GTM Insights
Aviso’s Time-Series database is the backbone of its predictive intelligence. Unlike CRM snapshots, this system stores and indexes every change in deals, rep behavior, and interactions over time—all with timestamps. It’s more like a full-length film than a still image.
Features & Architecture Highlights:
Event-based ingestion via a hybrid ELT architecture
Aviso extracts data from a wide range of sources—not just CRMs, but also email, calendars, activity logs, and conversational platforms. Using both direct connectors and intermediaries, Aviso’s ELT layer captures rich, time-stamped events from any system with an API.Normalized, multi-dimensional time-series signal store
Each event gets mapped across multiple dimensions—opportunity, rep, team, deal stage, etc.—not limited to snapshots. Overwriting is avoided: every signal is preserved as part of a continuous historical record.Feature engineering on temporal dynamics
From raw timestamped events, Aviso derives more informative features—like "time since last next-step update"—rather than relying solely on static CRM fields. Derived features allow more accurate modeling of deal health and risk."Unborn" Deal Prediction
Thanks to historical patterns, Aviso can predict deals that haven’t been created yet but are likely to emerge within the quarter—spotting pipeline opportunities before they exist officially.
Machine Learning Over Time-Series: Forecasting, Nudges & Accuracy
With a rich Time-Series data backbone, Aviso builds high-powered ML systems that deliver actionable insights.
AutoML over Time-Series events
We use an AutoML pipeline: feature extraction, selection, hyperparameter tuning, and model selection—operating automatically and continuously on evolving data.Backtesting & Forecast Validation
Models are routinely backtested against historical periods to validate accuracy before being deployed in production. This process drives forecast accuracy beyond 98%.Role-specific, real-time insights
Forecasts and predictions are tailored for different stakeholders: reps get deal-level nudges, managers get pipeline health summaries, and execs get enterprise-wide forecasts.
From Signals to Prescriptions: LQMs + LLMs + Time Awareness
For Aviso, the Time-Series database isn’t the end—it’s the context. Building on that foundation, two complementary model layers deliver intelligence:
A. Large Quantitative Models (LQMs)
LQMs are Aviso’s act as a cohesive reasoning engine, fusing outputs from task-specific AI/ML models and enriching them with context from our Ontology layer. So instead of insights operating in isolation, LQMs connect the dots. For instance, they recognize when a high-commit deal with no recent meetings, weak engagement, and repeated stage slippage signals systemic risk. The LQM quantifies the forecast impact and recommends exactly what to do next — whether that’s escalating internally or re-engaging the buyer — so your actions are always grounded in connected, data-driven reasoning.
B. Large Language Models (LLMs)
LLMs layer on interpretability and narrative:
They understand natural-language questions (“Which slipped deals are gaining momentum?”), surface explanations, and translate numeric patterns into actionable language
They integrate conversational intelligence with quantitative reasoning grounded in temporal context, not just static data
C. Time-awareness = Context + Momentum
The combination of LQMs with LLMs enables reasoning not just over "what is" but also "why” and “what’s next.” Assistants understand rate-of-change, causality, and evolving patterns.
Forecasts become dynamic, risk-insightful, and anticipatory
Deal health can trigger real-time alerts—for acceleration or slipping—before it becomes evident in standard dashboards
Coaching becomes personalized and behavior-driven—e.g., if a rep's deals stall consistently at negotiation, the system surfaces that pattern with suggestions
Why Aviso's Time-Series Approach is a Game-Changer for Revenue Teams
By building our platform on this Time-Series foundation, Aviso provides a "single source of truth" that goes beyond simple reporting.
Unprecedented Accuracy: By analyzing historical patterns and every subtle change, our AI can forecast with a level of accuracy that’s simply impossible with spreadsheets or traditional CRM reporting. We see the trends before they become obvious.
Real-Time Course Correction: Get early warnings about deals at risk or reps falling behind. Our platform acts as an early-warning system, allowing managers to intervene and coach proactively, not after the quarter has already ended.
Eliminates Guesswork: Stop relying on "gut feelings" and subjective opinions in forecast calls. Aviso provides data-backed insights into which deals will close, which are at risk, and what your true revenue pipeline looks like.
Deeper Deal Intelligence: See the entire journey of a deal. Who changed the close date? When did the deal value increase? This complete historical context helps you understand deal health and identify winning (or losing) patterns across your entire team.
Beyond Forecasting: Writing the Next Chapter
Time-Series is more than just a data structure for Aviso—it’s the narrative backbone of revenue intelligence. By capturing every event across time, layering in quantitative and language models, and reinforcing insights with memory and feedback, Aviso transforms static CRM snapshots into a living, evolving film of the business.
Where most tools tell you what’s happening right now, Aviso shows you how you got here, where you’re headed, and what actions will change the outcome. This fusion of time-series data, LQMs, and LLMs doesn’t just forecast—it explains, prescribes, and adapts.
The result? A self-improving system that not only predicts with over 98% accuracy but also guides sales teams and leaders with the context they need to act decisively. In a world where every quarter counts, Aviso’s time-series foundation ensures that GTM organizations don’t just see the numbers—they understand the story behind them, and how to write the next chapter.
