AI Strategy & Implementation
Turn scattered technical knowledge into intelligence
We implement AI assistants and agents that let your technical teams find relevant answers across internal knowledge bases in seconds.
A "Jarvis" for your R&D, engineering and technical operations teams.
The Knowledge Is There. The Access Isn't.
Your people spend hours upon hours a week, searching.
Institutional knowledge is walking out the door.
Your team is solving problems that are already solved.
How It Works.
We deploy custom Retrieval-Augmented Generation (RAG) AI assistants and agents that connect to your existing internal documents.Your team gets a "Jarvis" with which they can interact in plain language and gets relevant answers with source citations, drawn from your own data.
1. We ingest your knowledge base documents.
Research papers, design files, equipment manuals, maintenance logs, failure analysis reports, design standards, past project files, SOPs. PDFs, Word docs, spreadsheets, even scanned documents. We handle the messy reality of how technical organizations actually store information.
2. AI encodes the meaning, not just the words.
The system converts your documents into semantic representations. It understands related concepts, even if different people used different terminology. This is what makes it fundamentally better than traditional keyword search or a shared drive.
3. Your team asks questions and gets answers.
Engineers, technicians, and researchers query just like with ChatGPT or Claude. Except it retrieves the most relevant information from across your entire document library, synthesizes an answer, and cites the specific sources so your team can verify the answer.
What This Looks Like in Practice.
Your senior engineer with 30 years of experience is retiring next year.
Their knowledge about why certain design decisions were made, which suppliers deliver on time, and how a recurring weld defect was finally traced to a heat treatment process — none of that is written down in any single place. With a knowledge system built from maintenance records, project files, and engineering memos, that expertise becomes searchable and permanent.
New engineers get up to speed faster. Lessons learned don't need to be re-learned. Critical context survives personnel changes.
Your factory equipment throws an error at 2 AM.
Instead of your technician spending 45 minutes digging through binders and PDF manuals, they ask the system: "Startup error code 4517 on conveyor drive 117A — what's the fix?" They get an answer in seconds, with references to the relevant manual section and three past incidents where the same issue was resolved.
Every hour of unplanned downtime costs real money. Cutting troubleshooting time from 45 minutes to 5 minutes on even a few incidents per quarter pays for the entire system.
Your team is designing a new surgical robotic end-effector and needs understand prior decisions.
A decision on the prior generation of robot was made two years ago and the rationale is spread across reports, test results, and slide decks. An engineer queries the system: "What drove the max grip force specification on the Gen 2 end-effector, and what test data supported it?" They get the relevant summary, simulation outputs, and test results — with direct citations to each source document.
When engineers can trace the full rationale behind inherited specifications in minutes instead of days, design iterations move faster, and fewer requirements get re-validated unnecessarily.
Your R&D team is investigating a materials compatibility issue.
Before starting from scratch, they query the system and discover that a different team already tested the same material combination two years ago — complete with test results, supplier notes, and the decision rationale. What would have been a week of duplicated effort becomes a 10-minute search.
Researchers and engineers reclaim hours every week that were previously spent re-discovering what the organization already knows.
See It in Action.
See it in action on the publicly available NASA Technical Reports Server — over 600K+ engineering and science document records spanning decades.It finds relevant research across the entire library using meaning, not just keywords.We can rapidly deploy similar capability with your knowledge base.
We Understand Complex R&D and Technical Operations.
Our team's backgrounds include mechanical and aerospace engineering, R&D and scientific research, hardware product development, robotics, medical devices, biomedical engineering, industrial manufacturing, and business transformation. We've worked in the environments where these systems need to perform.We work with engineering and technical operations teams across several industries, including:Manufacturing | Aerospace | Biomedical Engineering | Mechanical Engineering | Academic Research | Industrial automation and controls | Electronics and hardware engineering
Your Data Never Has to Leave Your Environment.
This is the first question, and it should be. We built our approach around the assumption that data security is non-negotiable.
We deploy on your infrastructure.
Every solution we build can run on your existing or preferred cloud platform: AWS, Azure, Google Cloud, or any other provider you trust.
You choose the AI models.
We're model-agnostic. We deploy leading commercial API like OpenAI, Anthropic, or local open-source models like Llama and Mistral.
No vendor lock-in. No data extraction.
We don't host your data. The system we build is yours, running on your infrastructure, under your IT team's control.
Need on-premise solutions?
We can design fully air-gapped deployments using local models on on-premise hardware. No external API calls, no cloud dependency, no data leaving your facility.
Start Small. See Results. Then Decide.
We start with a low-risk proof-of-concept or pilot. Scoped to a real workflow and users, using your actual knowledge base, with outcomes defined upfront.
1. Discovery Call (30 min)
We learn about your team, pain points, your knowledge base, and where we could drive value. No slide decks, just a conversation about what's actually painful and whether this is a good fit.
2. Proof-of-Concept (4-6 weeks)
We build a working prototype on a focused subset of your knowledge base, targeted at one specific use case. Let your team see real results on your real data before scaling.
3. Evaluate and Expand
You've learned something concrete about where AI can and can't help your organization. Once the Proof-of-Concept delivers value, we scale it. More data, more use cases, more users, more functionality.
San Francisco, USA | Zurich, Switzerland
Copyright © 2026 Alterra Cognitive. All rights reserved.
Let's Talk.
Tell us what your team is struggling to find, and we'll tell you if we can help. No pitch, no pressure. If it's a fit, we'll scope a proof-of-concept. If it's not, we'll tell you that too.
Book a Discovery Call:
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San Francisco, USA | Zurich, Switzerland
Copyright © 2026 Alterra Cognitive. All rights reserved.