You don’t need AI everywhere. You need it where it moves your numbers.
70% of AI project success depends on picking the right problem, aligning the right people, and deploying where the business case is clear. Every engagement starts with an honest conversation about where AI will change your operations — and where it will not. We have walked away from projects where AI was not the right answer. It is not always comfortable, but it is how we earn long-term trust.
Section 02 / 06The Orbis Suite — Five Production Systems. Built From Scratch. Tested Under Real Load.
We build products alongside our consulting work — five AI systems developed from the ground up because the specific problems our clients faced needed purpose-built solutions. Every product has been iterated under real operational conditions across multiple deployments. Having our own products means we understand what it takes to maintain, improve, and support AI systems long after launch day.
Orbis Analytics
An AI agent that interrogates your ERP, warehouse, and operational data to surface the decisions your dashboards bury. Ask a question in plain language, get an answer grounded in your actual numbers. Deployed across live operations. Your team stops building reports and starts acting on answers.
Orbis Call
AI voice agent that handles outbound sales calls, procurement follow-ups, and support inquiries at scale. Runs 24/7. Conversations are natural, context-aware, and logged for your team to review. It handles the call volume so your people can focus on the conversations that need a human touch.
Orbis Security
Computer vision surveillance that processes live camera feeds and flags threats as they develop. Real-time detection with classified severity levels, deployed across 40+ facilities. Continuous monitoring that does not lose focus at hour fourteen of a shift.
Orbis Vision
Fuses live camera feeds with operational sensor data to give you a real-time picture of your facility — plus the leading indicators of failure before the alarm goes off. Built for operations teams who want to act on conditions early, with enough lead time to plan rather than scramble.
Orbis Print QC
Computer vision defect detection deployed directly on the production line. Catches color deviations, dimensional tolerances, and print failures at sustained throughput — before the product ships. Measures every item consistently across a full shift. Reduces waste. Reduces returns.
Five deployments. Measured outcomes. Every number verified against a pre-deployment baseline.
Every project listed here runs in production. Every metric was measured against a pre-deployment baseline. We define what "success" looks like before a single line of code is written, and we hold ourselves to that definition.
2M Additional Clicks in 14 Days — A Recommendation Engine That Learned What Readers Actually Want
Every user was seeing the same content regardless of reading history, clicks, or behavior. No personalization. No signal capture. We built a recommendation engine using collaborative filtering, content-based filtering, and user clustering. It processed 1.6 million content items and learned individual reading patterns. Two weeks after going live: 2 million additional clicks. The system is still running, still learning, still improving.
Additional clicks in 14 days
Content items processed
Four Phases. Full Visibility. You Approve the Architecture Before We Write a Line of Code.
80% of AI projects stall before they reach production. In our experience, the reason is almost never the technology — it is use-case selection, preparation, and accountability. Our process is built to address all three. You know exactly what we are building, why, where it will run, and how we will measure whether it worked. Before we start.
Section 03 / 06Responsible AI Is an Engineering Decision.
Every AI system we build has a human in the loop at every critical decision point. We have seen firsthand what happens when that safeguard is missing — and it shaped how we build everything. AI that reaches an employee, a customer, or the public without human review is one bad output away from real harm.
Enterprise AI built responsibly survives its first audit, its first edge case, and its first public-facing moment. The reason is practical, not philosophical. Building responsibly is how you build something that lasts.
Transparency
Every system comes with documentation your team can read and understand. How the model works. What data it uses. What it does not know. Full visibility into every decision the system makes.
Accountability
Clear ownership of outcomes assigned before deployment. A named person on both sides who owns the result. Accountability is specific, not distributed.
Fairness
Bias auditing of training data and model outputs — continuously, not just at launch. Drift happens and edge cases surface under real load. We monitor for both.
Privacy
CCPA, GDPR, and applicable data regulation compliance built into the architecture from day one. Privacy is a design requirement, not a retrofit.
Inclusiveness
AI that is accessible across diverse user groups. If your system serves a varied population, it needs to work for all of them, not just the majority.
Diversity
Representative datasets that train our models. We audit what goes into the training pipeline, not just what comes out. Because the quality of AI decisions starts with the quality of the data.