We Deploy AI ThatShips.

We take AI from strategy to production — that is all we do. 5.5 years across 5 industries, every system running under real operational load. The person who scopes your project is the same person who deploys it.

Custom AI SolutionsComputer VisionNLPAI Agents
See What We Shipped30-Min Strategy Call
0+Years building AI systems running in live operations today
0+Facilities where our systems are deployed and operational right now
0M+Additional clicks generated for one client, 14 days after deployment
What We Build

Eight Capabilities. Each One Built for Your Specific Operation.

Enterprise AI Agents

Autonomous agents that handle complete business workflows: procurement matching across vendor catalogs, invoice reconciliation with three-way matching, candidate screening from thousands of applications, automated report generation from live operational data. Each agent integrates with your existing tools -- ERP, CRM, HRIS -- makes decisions within rules your team defines, and escalates when confidence drops below threshold. Typical first deployment: 4-6 weeks from workflow mapping to production.

Computer Vision Systems

Visual AI that sees, classifies, and acts across any environment -- not limited to manufacturing. Security surveillance with real-time threat detection, retail shelf monitoring, agricultural crop analysis from satellite imagery, medical image classification, and warehouse inventory tracking. Each system is trained on your specific visual taxonomy and deployed where the decisions happen. First models typically reach production accuracy within 3-4 weeks of labeled data collection.

NLP & Document Intelligence

Systems that read, classify, and extract structured data from your documents at scale -- contracts, invoices, support tickets, regulatory filings, medical records. Sentiment analysis for customer feedback pipelines, automated routing for support queues, clause extraction for legal review, and entity recognition across multilingual corpora. Your team stops reading through stacks and starts acting on structured insights. Most document pipelines reach production within 3-5 weeks depending on document complexity.

Recommendation & Personalization Engines

AI that learns individual user behavior and delivers relevant content, products, or next-best actions in real time. Collaborative filtering, content-based matching, and behavioral clustering tailored to your domain -- media publishers, e-commerce catalogs, SaaS onboarding flows, internal knowledge bases, or learning platforms. Each engine improves continuously as it processes more interactions. Where personalization makes sense, the lift is measurable within the first two weeks. Where it does not, we will tell you during scoping.

Predictive Analytics & Maintenance

Models that combine sensor telemetry, operational data, and historical failure patterns to predict equipment degradation, forecast demand spikes, and flag supply chain risks before they become emergencies. Use cases include turbine health scoring, fleet maintenance scheduling, grid load balancing, and warehouse demand forecasting. Typical first model reaches production within 6-8 weeks, with accuracy improving as it ingests more operational cycles.

AI Voice Agents

Voice AI that handles outbound sales calls, appointment confirmations, procurement follow-ups, and tier-1 support inquiries at scale. Natural conversations, full context awareness, 24/7 availability. Every call logged, transcribed, and sentiment-scored for your team. Works best for high-volume, structured conversations where consistency matters -- the agent handles those so your people focus on complex negotiations and relationship-building. First voice agent typically live within 3-4 weeks.

Production Line Quality Control

Purpose-built for manufacturing environments where inspection happens at line speed. Catches color deviations, dimensional tolerances, surface defects, and print failures integrated directly into your production flow. Use cases include label verification, fill-level inspection, assembly completeness checks, and cosmetic grading. Your quality team gets data on every unit produced, enabling continuous process improvement. Typical line integration: 4-6 weeks from defect taxonomy definition to live inspection.

AI Strategy & Readiness Assessment

A focused working session that answers the question every enterprise asks first: where should we start? We score your use cases on two axes -- business criticality and deployment complexity. The sweet spot is high criticality, low complexity -- the problems that matter most and can be solved fastest. You leave with a ranked list of opportunities, realistic timelines, estimated ROI, and a clear first step. Where AI is the wrong tool for a problem, we will tell you that directly. The person who runs the assessment is the same person who would build the system.

Start here
02 / 06

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 / 06
  • 01

    Automation

    Your best people are spending 20-30% of their week on repetitive, high-volume work that requires zero judgment. That is a strategy problem worth solving.

    We map those workflows, identify the ones where automation delivers measurable time back, and deploy pipelines that handle production volume from day one. Your team goes back to the work that requires their expertise. The volume work runs reliably without them. This is the "crawl" phase — the safest starting point with the fastest payoff.

  • 02

    Enhancement

    You have systems that work and teams that know their domain. We add the predictive layer on top.

    Decision-support models, analytics, and recommendation engines that integrate with your existing infrastructure. Your teams keep the tools they trust. They get measurable lift from AI layered into what already works. This is the "walk" phase — you have proven AI works in your organization, and now you are expanding what it can do.

  • 03

    Transformation

    You have optimized everything you can optimize. Now you need to build something that did not exist before.

    When you have exhausted efficiency gains and your market demands something fundamentally new, this is where we go together. We design the architecture, validate the approach against your real operational data, build to production spec, and stay through first measurable results. Full-stack, end-to-end. This is the "run" phase, and it works best when you have already proven AI at smaller scale first. We will be honest about whether you are ready for this step.

Industries We Serve

Five industries. Production systems running today. Here is where our work lives.

Computer vision for quality control, predictive maintenance for energy infrastructure, recommendation engines for media publishers, real-time threat detection for security operations, and satellite vision pipelines for agriculture. Every system deployed and running in live operations.

0+Facilities Deployed
0+Years in Production
99.7%defect detection

Manufacturing

Computer vision systems for quality control, defect detection, and predictive maintenance — deployed across 40+ production facilities.

63%less downtime

Energy

Predictive maintenance models combining sensor telemetry, weather patterns, and historical failure data. Six-figure downtime events prevented.

2M+clicks generated

Media

Recommendation engines and personalization systems that drive measurable engagement. One deployment: 2M additional clicks in 14 days.

<3salert latency

Security

Real-time computer vision that detects threats as they develop. Multi-camera fusion, classified severity levels, sub-3-second alert latency.

18%yield improvement

Agriculture

Satellite and drone vision pipelines processing multispectral imagery. Early disease detection, yield prediction, and irrigation anomaly flagging.

Products We Built and Run Ourselves

The 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.

01

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.

02

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.

03

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.

04

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.

05

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.

Deployment Results01 / 05

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.

Recommendation EngineNLPBehavioral Modeling

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.

0M+

Additional clicks in 14 days

0.0M+

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 / 06
01

Discover

Before we touch technology, we assess where you are today. A focused working session covering your operations, data infrastructure, team readiness, and business goals. We score potential use cases on two axes: how critical is this to your business, and how complex will it be to deploy. The sweet spot is high criticality, low complexity — the problems that matter most and can be solved fastest. You leave with a specific, ranked list of where AI will move your numbers and where it will not. We are straightforward about both, because that honesty is the foundation everything else is built on.

02

Design

We architect the complete solution: models, infrastructure, integration points, data requirements, and success metrics. You see the full blueprint. You approve it. Then we build. The success metrics are defined here — before deployment — so we have a shared definition of what "working" means, what confidence thresholds trigger a go/no-go decision, and what the baseline measurement is. Clear targets, clear accountability.

03

Build

We develop, test, and iterate against your real data, in your environment, under conditions that approximate production load. Weekly working demos keep you in full control — live demonstrations of what the system does today and what it will do next week. Every system is built for production from day one, because we have learned that the gap between "works in testing" and "works under load" is where most projects fail.

04

Deploy & Scale

We deploy into your environment, train your team to operate the system independently, and monitor production performance from day one. When confirmed results arrive — and we share the measurement methodology so there is full transparency — we scale across your operations. Crawl, then walk, then run. Prove it works in one facility, one department, one process. Then expand with confidence.

Responsible AI

Responsible 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.

01

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.

02

Accountability

Clear ownership of outcomes assigned before deployment. A named person on both sides who owns the result. Accountability is specific, not distributed.

03

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.

04

Privacy

CCPA, GDPR, and applicable data regulation compliance built into the architecture from day one. Privacy is a design requirement, not a retrofit.

05

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.

06

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.

Find out where AI will move your numbers. One conversation. Straight answers.

Thirty minutes with the person who would design and deploy your system. You will leave with a specific, ranked view of where AI fits your operations, what the realistic timeline looks like, and what the right first step is. If AI is not the right move for you right now, we will tell you that. And if we are not the right fit for your specific problem, we will point you toward someone who is. We would rather earn your trust than win a project.

Book 30 Minutes

30 minutes. No obligation. No pitch deck. Just a direct conversation about what AI can do for your business.

You own the IP from day oneNDA before first conversationData processing agreements standardFixed-fee engagements — no billable hour surprises