Custom machine learning integrated into your existing technology.

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What We Do

Parallel Attention builds custom machine learning models and integrates them into the tools your team already uses: your CRM, BI dashboards, ERP. No new platform. No in-house ML team required.

A few signs you're a good fit:

01

You have at least thousands of historical data points tied to an outcome you care about.

transactions, service history, a consistently tracked sales funnel

02

Key outcomes are influenced by patterns in your data, not purely external factors.

something your business does or experiences, not macro forces outside your control

03

Your workflows lead you to repeat similar decisions hundreds, if not thousands, of times a month.

prioritizing, routing, flagging, segmenting

Not sure if you qualify? The Learn phase exists partly to answer that question before you commit to anything.

In Practice

A national service chain, 200+ locations

New locations are selected through a rigorous, data-informed process. One opens and consistently underperforms. Not dramatically, just enough to look like normal variance.

Problem

An unexplained performance gap

Traditional analytics confirm the location is underperforming. Regional managers have their suspicions, but there isn't enough empirical support to make any significant changes.

Application

Pattern detection and integration

A model identified a relationship between customer mix, timing, and product selection. The relationships spanned too many variable interactions for traditional analysis to isolate. ML-informed values pushed directly into the CRM, scheduling software, and inventory systems.

Outcome

Measurable improvement

No new platform to learn. No change to existing workflows. The right information reached the right people and systems at the right time, closing the performance gap.

This is what integrated ML looks like in practice.

Not a new system to learn. Not a report to interpret. The right information, shaped by your data, delivered where your team already makes decisions.

How We Help

Solutions for known and latent problems.

Problem

Unclear AI suitability

Knowing whether ML is the right tool for a given problem takes expertise most teams don't have in-house. Without it, it's hard to tell which use cases are worth pursuing and which aren't.

Solution

Validated before deployment

Your custom model is properly validated and tested against holdout data before it goes live. You see the measurable value before you commit. Your new model outputs then integrate directly into the tools you already know how to use best.

Problem

Insights trapped in dashboards

Some users can see the data, but many key signals are buried and rarely reach the people keeping your business running every day.

Solution

Integrated where you work

Model outputs push directly into the tools your team already uses. No new platform. No workflow change.

Problem

Baseline blindness

Normalized inefficiencies become invisible drag, baked into your baseline.

Solution

Custom predictive models

Augment your existing tools output by giving access to reliable predictive data. Make better decisions at every step of the funnel.

The Process

From kick-off to predictions in production.

01Learn
Every engagement starts with your business, not just your data. We map workflows, interview stakeholders, and identify where better predictions would inform a decision, and where they should drive one. Before we look at a single data point, we understand what your business actually needs to predict and why.
02Scope
No ambiguity. We deliver a scoping document that specifies the models, the data, the metrics, the integration plan, and the timeline. You sign off on what "done" looks like before we start.
03Build
We build predictive models specific to your business, not off-the-shelf algorithms with your data poured in. ML learns structure from your data, surfaces patterns traditional analysis misses, and generates predictions tuned to your specific performance metrics.
04QA
Models are validated against out-of-time holdout data so you can see real performance metrics before anything reaches production. APIs are tested at low volume and well above projected peak.
05Deploy
Predictions integrate into the tools your team already uses: CRM, BI dashboards, ERP, internal apps. No new platform to learn. No workflow disruption.
06Iterate & Optimize
Your business evolves. Your models should too. We retrain, tune, and extend models as new data comes in, technology develops, and conditions change. Continuous monitoring and methodical feedback loops keep the system improving alongside your business.
What You Gain

Built for your business. Integrated into how it runs.

A validated model

A working predictive model trained on your data, tested against out-of-time holdout sets, and documented with clear performance metrics so you know exactly what it does and how well.

Live integration into your stack

Predictions written directly into your CRM, BI dashboards, ERP, or internal applications through APIs and connectors built and maintained by us. No new platforms for your team to learn.

Continuous monitoring and retraining

Automated performance tracking plus scheduled retraining cycles. You get model health reports on a regular cadence, with alerts when metrics drift beyond agreed thresholds.

Documentation and handoff

Every model ships with a scoping document, validation report, integration spec, and runbook. Your team knows what the model does, where it lives, and how to work with it.

FAQs

Things you're probably wondering.

Traditional analytics starts with a hypothesis and tests it against data. ML works in the other direction. It learns structure from your data, finding patterns and relationships that human analysis often misses. The result is models that can make predictions at a scale and accuracy that manual forecasting can't match.
It depends on what you're trying to predict. During scoping, we assess your existing data sources, quality, and gaps. Many companies have more usable data than they think. It's often a matter of organizing it for the right purpose, not collecting more of it.
Growth-oriented and mid-market businesses: companies with enough data to train meaningful models but not enough budget or headcount to justify a full in-house ML team. We complement existing data science and analytics teams, not replace them.
See the What We Do section above for the specific criteria we use to evaluate this. The short version: if you have historical data tied to an outcome you care about and a repeatable decision you want to improve, you're probably a stronger candidate than you think. The Learn phase exists partly to confirm this before you commit to anything.
It depends on the problem, but a useful rule of thumb is a few thousand historical examples of the outcome you're trying to predict, not total rows, but instances where the thing you care about actually happened. Quality matters more than volume. The Learn phase includes a data assessment, so you'll know early whether what you have is sufficient before any significant commitment.
ML is most valuable when you have enough historical data to train on, the problem involves complex patterns that are hard to capture with rules or formulas, and the prediction directly drives a business decision. Many organizations start with straightforward models like time-series forecasting before transitioning to more sophisticated solutions. We help you find the right starting point.
We validate models against out-of-time holdout data early in the engagement, so you can see performance metrics before anything touches production. The timeline from scoping to integrated model outputs depends on data readiness, but we design engagements to show value fast.
Engagements start with a scoping phase to understand your business and data. Pricing depends on the complexity of the models and the depth of integration required. We're upfront about costs and structure engagements so you can see measurable value before scaling.
No. The entire point is that key signals show up where your team already works: your CRM, BI dashboards, ERP, or internal applications. We integrate into your stack, not the other way around.
Models degrade over time as data and conditions change. We continuously monitor performance, retrain on fresh data, and update models so they stay accurate. This isn't a build-and-forget engagement. It's an ongoing partnership.
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Parallel Attention
ParallelAttention
Custom ML · Integrated Predictions · Ongoing Partnership