[Solutions · GenAI & Agentic Solutions]
When production issues arise, teams need answers, not more documents. We help manufacturing and automotive enterprises reduce manual effort, shorten response times, and automate repetitive work through GenAI applications, AI agents, and intelligent automation.
Pre-built AI/ML models
Computer Vision Use Cases
GenAI Frameworks
[GenAI & Agentic Capabilities ]
Whether you're solving one workflow or scaling AI across the enterprise, we help turn operational knowledge, documents, and business processes into governed AI experiences people actually use.
Create measurable operational impact.
Give employees instant access to the information they need to troubleshoot issues, answer questions, and make decisions without searching across manuals, SOPs, or disconnected systems.
Automate work that requires coordination across systems, teams, and decisions, reducing delays and manual handoffs.
Reduce repetitive effort across document-heavy and operational processes while improving consistency and turnaround times..
Observability, responsibility, and the platform underneath
Operate AI with visibility into performance, quality, usage, and cost, so teams can scale adoption with confidence.
Put AI into production with the controls needed to manage risk, protect data, and support enterprise adoption.
Build AI on the platforms and cloud services you already run without creating a parallel technology stack.
[Proven Results]
The strongest GenAI programs solve a specific operational problem first. These are the outcomes manufacturing and automotive organizations commonly achieve through AI-powered assistants, automation, and workflow orchestration.
Automation
Documents processed automatically across claims, invoices, and operational workflows
Productivity
Faster employee productivity through instant access to operational knowledge
Savings
Warranty cost savings through faster issue identification and root-cause analysis
Automation
Fewer defects through continuous monitoring and earlier intervention
[Who We Work With]
GenAI initiatives succeed when business, operations, and technology leaders align around a measurable outcome. Our engagements are designed to support all three.
[For the C-Suite]
Prioritize AI investments that improve operational performance, reduce costs, and create measurable business value.
[For Operations & Plant Leaders ]
Give teams faster access to knowledge, automate repetitive work, and improve execution without disrupting existing workflows.
[For Heads of AI, Data & CDOs ]
Deploy AI with the governance, observability, and controls required to scale adoption responsibly across the enterprise.
[Common Questions]
GenAI generates content and answers: copilots that draft documents, summarise data, or answer technical questions. Agentic AI takes action: agents that plan a sequence of steps, call tools, update systems, and complete a workflow with limited human oversight. Most manufacturing engagements use both, a copilot that helps a technician diagnose an issue and an agent that updates the work order, orders the part, and schedules the technician. Which you need depends on whether the goal is faster answers or fewer manual handoffs.
Every agent we build runs with three layers of control: it answers only from sanctioned sources you approve, it shows the reasoning and evidence behind every action, and it pauses for a human approval on actions that change a system of record or affect the line. You decide where the agent acts on its own and where it asks. Confidence is engineered, not promised.
The most common first wins are knowledge agents that compress repair or troubleshooting time on the floor, document AI that reads invoices, claims, or SOPs and updates downstream systems, and copilots that help engineers and analysts get to the answer faster. Each starts narrow, proves the pattern on one workflow, and expands from there. Pick the highest-frequency, highest-cost workflow your people already do manually.
Every agent we build is engineered to run inside your environment, on the cloud accounts and data stores you already control. Retrieval happens against your first-party data through sanctioned connectors; sensitive content is masked or filtered before it reaches a model; nothing trains a third-party model without your explicit approval. The data your agent reasons on never leaves your perimeter unless you say so.
The model follows the task, not a fixed preference. We use proprietary models like Claude, GPT, or Gemini when the workload needs frontier reasoning, and open-source models like Llama or Mistral when cost, latency, or data residency tip the balance. Most production agents use a mix, with model routing that sends each request to the right model for its task. You get the performance you need without locking in to one provider.
[Continue Reading]
Our AI advisory engagements are designed for senior leaders in manufacturing and automotive enterprises who are tired of AI pilots that never reach production and ready to own a measurable outcome inside one quarter.
Solutions · BUILD
The data foundation that keeps the twin in sync with the floor and the map in sync with the network.
Solutions · think
Products. Forecasting, PdM, vision and defect accelerators that ship in 4–6 weeks.
Solutions · think
Six-capability practice plus GenBI natural-language analytics governed by your semantic layer.
