AI Automation Services
AI development services:
AI AUTOMATION. ENGINEERED.
We design and build custom AI agents that take real work off your team's plate, engineered with the same rigor we bring to hardware, CAD, and manufacturing. Starting with our own domain: automating the engineering workflows we know best, and extending that same discipline across your business.
Engineering Process Automation
This is where we started, and where we have the deepest expertise. Engineering teams run on repetitive, judgment-heavy processes that are hard to automate well — which is exactly why most automation tools fail at them.
CAD & Design Workflows Agents that check drawings against design standards, flag tolerance and fit issues, and generate first-pass BOMs from CAD files.
Reverse Engineering Support Agents that assist in converting scan data and point clouds into structured CAD workflows, flagging geometry that needs a human eye.
Documentation & Compliance Auto-generated engineering change orders, test reports, and compliance documentation drawn directly from your project data — formatted and consistent every time.
Quality Control & Inspection Data Agents that parse inspection and test data, flag out-of-tolerance parts, and summarize QC trends across production runs.
Supplier & Procurement Communication Agents that draft RFQs, track supplier responses, and flag lead-time or pricing anomalies before they become production delays.
We built our automation practice solving these problems for our own engineering work first — so we understand where AI genuinely helps in a technical workflow, and where it needs a human in the loop.
Beyond Engineering: Where Else We Automate
The same approach extends to any business function with high-volume, repeatable, judgment-based work:
Customer Support & Triage — categorize and route tickets automatically, resolve repetitive requests, escalate the rest with full context attached.
Sales & Lead Qualification — research inbound leads, score them, draft outreach, and keep your CRM current.
Back-Office & Finance — invoice reconciliation, expense processing, report generation, and data entry.
Internal Knowledge & Ops — agents connected to your internal docs and tools that answer questions and summarize meetings.
Custom Agent Development — a specific, complex workflow in mind? We design and build purpose-built agents around your exact process.
The Hard Part: Making AI Agents Actually Reliable
Anyone can wire a chatbot to an API. Making an agent that's accurate, safe, and worth trusting with real business processes is a different problem — and it's the one we focus on.
Hallucination Control LLMs will confidently produce wrong answers if left unchecked. We constrain agents with retrieval grounded in your actual data, structured outputs, and guardrails that catch and reject low-confidence responses rather than letting them through.
QA & Validation Every agent we ship is tested against a suite of real-world cases before going live, with ongoing evaluation to catch regressions when models or data change — the same test-before-ship discipline we apply to physical parts.
Cost-Optimized Model Selection Not every task needs a frontier model. We match smaller, cheaper, faster models to the tasks that don't need heavy reasoning, and reserve larger models for the steps that do — keeping your running costs proportional to the value delivered.
Local & Private LLM Deployment For engineering data, proprietary designs, or other sensitive information, we can deploy agents using locally-hosted or private-instance LLMs — so your IP and data never leave your infrastructure or a controlled environment.
Ongoing Monitoring Models and data drift over time. We monitor deployed agents in production and update them as needed, rather than treating a launch as the finish line.
The Advantage of Agents
An agent isn't just a faster way to do a task — it's leverage. Once built, an agent runs at a fraction of the marginal cost of a person doing the same work, doesn't get slower at 4pm, and scales from one task to ten thousand without adding headcount. That changes the math on what a small team can take on.
Leverage, not just labor savings — an agent handles the 500th ticket as easily as the first. The value compounds instead of staying flat.
Always-on capacity — agents work nights, weekends, and holidays without overtime, burnout, or turnover.
Consistency at scale — the same process, applied the same way, every time — no variance from who's on shift.
Compounding capability — as we refine an agent's playbook, it gets better across every task it touches, not just the next one.
This is the same shift the industry is already talking about: the idea of a billion-dollar company run by one person, powered by a fleet of specialized agents doing the work that used to require dozens of employees. Most businesses won't go that far, but the direction is the same for everyone. The teams that build strong AI leverage now will be able to do more, with less overhead, than the teams that don't. We help you start building that leverage where it matters most in your business.
Our Process
Automation Audit: We look at how your team actually works day to day and identify where AI automation will save the most time and pay back fastest.
Design: We map the workflow, define what the agent owns end-to-end, and agree on success criteria and validation methods before writing a line of code.
Build & Test We build in code, integrate with your existing stack, and validate against real cases — including hallucination and edge-case testing — before anything goes live.
Deploy & Monitor: Your agent goes live in stages — starting in a controlled environment, then production — with ongoing monitoring to keep it accurate as models and data evolve.
Why Choose ALT for AI Automation?
Built by engineers, for engineering workflows first — then extended to the rest of your business
Rigorous QA and validation are applied to every agent before it goes live
Cost-conscious model selection — the right model for the job, not the most expensive one by default
Local and private LLM deployment options for sensitive or proprietary data
One team for the full pipeline — from CAD and prototyping to the software and automation that runs your business
100% U.S.-based engineering team
The evolution of AI technology is rapidly progressing towards more efficient and adaptable systems. One significant advancement in this direction is the development of incremental learning techniques, which allow models to update and improve their knowledge base without the need for extensive retraining cycles. This approach is complemented by Retrieval-Augmented Generation (RAG), a method that enhances AI's capabilities by enabling it to interact with external databases and information sources. RAG allows AI systems to access and utilize vast repositories of knowledge, greatly expanding their ability to provide accurate and contextually relevant responses.
In the realm of data management and retrieval, vector databases are gaining prominence as a more efficient means of querying content. These databases store and retrieve information based on semantic similarity rather than exact matches, allowing for more nuanced and context-aware information retrieval. This technology is particularly valuable in applications requiring fast and accurate searching of large datasets, such as recommendation systems and content discovery platforms.
Natural Language Processing (NLP) continues to be a crucial component in bridging the gap between human communication and machine understanding. Advanced NLP algorithms now enable robots to comprehend voice inputs as text and generate natural language outputs, facilitating more intuitive human-robot interactions. This capability is essential for the development of voice-activated assistants, automated customer service systems, and even social robots designed to engage in conversation.
Physical AI
In the field of robotics, machine learning plays an indispensable role in enabling systems to learn from data and adapt to complex, dynamic environments. Computer Vision, a subset of machine learning, is particularly important for Robot Perception. By implementing sophisticated computer vision algorithms, robots can analyze visual inputs such as images and videos to extract critical information about their surroundings. This allows them to classify objects, track movements, and understand the overall scene, which is fundamental for tasks ranging from autonomous navigation to object manipulation.
Reinforcement Learning (RL) represents another significant advancement in robotics AI. This approach enables robots to optimize their behaviors through trial and error, learning to perform tasks more efficiently over time. RL has proven particularly effective in improving robotic capabilities such as grip strength and precision, gait optimization for walking robots, navigation in complex environments, and various other interactions with the physical world. By allowing robots to learn from their experiences, RL contributes to the development of more adaptable and resilient robotic systems.
The concepts of Transfer Learning and Fine-tuning are also making substantial contributions to the field of robotics. These techniques leverage pre-trained models that have been developed using large datasets, allowing researchers and engineers to adapt these models for specific robotic tasks with minimal additional training. This approach significantly reduces the time and computational resources required to develop specialized AI systems for robotics, accelerating the pace of AI innovation and enabling the creation of more sophisticated and capable robots across a wide range of applications.