About

About Our AI Team

We're a team of builders who happen to work with artificial intelligence. No grand promises about changing the world—just focused work on making AI systems that actually function in the real world.

How We're Organized

Our team is split into four verticals, each handling a distinct part of the AI development lifecycle. Think of it like a restaurant kitchen: you need people prepping ingredients, others cooking, someone plating, and someone making sure the whole operation runs smoothly. Everyone has their role, and the magic happens when these roles work together.

graph TD
    A[Data Pipelines & Engineering] --> C[AI-ML Model Building]
    A --> D[AI Solutioning]
    C --> B[LLM Ops]
    D --> B
    D --> C

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#ffe1f5
    style D fill:#e1ffe1

The Four Verticals

Data Pipelines & Engineering

This is where everything starts. This vertical handles the unglamorous but critical work of getting data from wherever it lives into a form that's actually usable.

What they do:

  • Build systems that collect data from sensors, databases, APIs, documents—anywhere information exists
  • Clean and transform messy real-world data into structured formats
  • Set up streaming pipelines for real-time data processing using tools like Kafka and MQTT
  • Manage time-series databases (InfluxDB, TimescaleDB) and vector databases for semantic search
  • Handle the infrastructure that moves gigabytes of data daily without breaking

Why it matters: AI models are only as good as the data they're trained on. This team ensures we're not building castles on sand. They deal with sensor noise, missing values, inconsistent formats—all the chaos of real-world data—so downstream teams can focus on solving problems rather than fighting data quality issues.

Technologies: Python, Apache Kafka, Redis, PostgreSQL, TimescaleDB, InfluxDB, Chroma, Weaviate, MQTT, Docker

AI Solutioning

This vertical sits at the intersection of business problems and technical possibilities. These are the translators—people who understand both what clients need and what AI can realistically deliver.

What they do:

  • Design system architectures for AI applications
  • Build Retrieval-Augmented Generation (RAG) systems that combine document search with language models
  • Create natural language interfaces for databases and APIs
  • Implement search systems that understand intent, not just keywords
  • Design multi-modal systems that combine different types of data (text, images, sensor readings)
  • Prototype solutions quickly to validate ideas before full-scale development

Why it matters: Having powerful AI models means nothing if they're solving the wrong problem or if users can't interact with them effectively. This team figures out how to package AI capabilities into systems that people actually want to use. They're the ones asking "should we build this?" before others ask "can we build this?"

Technologies: LangChain, LlamaIndex, OpenAI APIs, Anthropic Claude, RAG frameworks, FastAPI, REST APIs, OpenAPI specifications

flowchart LR
    A[Business Problem] --> B[AI Solutioning]
    B --> C[Architecture Design]
    B --> D[Prototype]
    C --> E[Data Engineering]
    C --> F[Model Building]
    D --> G[Validation]
    G -->|Success| H[Production]
    G -->|Iterate| B
    
    style B fill:#e1ffe1

AI-ML Model Building

This vertical is the core technical engine where algorithms meet reality. This is where we build, train, and refine the models that power our systems.

What they do:

  • Develop computer vision models for image analysis and object detection
  • Build time-series forecasting models for predictive analytics
  • Create anomaly detection systems that spot problems before they escalate
  • Train deep learning models (CNNs, LSTMs, Transformers) on domain-specific data
  • Implement classical machine learning algorithms when they're the right tool
  • Fine-tune pre-trained models for specific use cases
  • Optimize models for accuracy, speed, and resource efficiency

Why it matters: This is where the "intelligence" in artificial intelligence lives. These models need to be accurate enough to trust, fast enough to be useful, and robust enough to handle messy real-world inputs. A model that works beautifully in a lab but fails in production is worthless.

Technologies: PyTorch, TensorFlow, scikit-learn, OpenCV, YOLO, Faster R-CNN, ARIMA, Random Forests, XGBoost, Sentence-BERT, NumPy, Pandas

graph TD
    A[Raw Data] --> B[Feature Engineering]
    B --> C[Model Training]
    C --> D[Validation]
    D -->|Poor Performance| E[Hyperparameter Tuning]
    E --> C
    D -->|Good Performance| F[Model Optimization]
    F --> G[Production-Ready Model]
    
    style C fill:#ffe1f5

LLM Ops

This newest vertical emerged as large language models became production-ready. This team makes sure AI systems don't just work once—they keep working, day after day, under real-world conditions.

What they do:

  • Deploy models to edge devices (Raspberry Pi, Jetson) and cloud infrastructure
  • Set up monitoring systems that track model performance in production
  • Implement caching strategies to reduce latency and costs
  • Manage model versioning and deployment pipelines
  • Detect model drift when real-world data starts diverging from training data
  • Build infrastructure for A/B testing different model versions
  • Handle the operational side of running LLM-based applications at scale

Why it matters: Getting a model to work in development is one thing. Keeping it working reliably when thousands of users are hitting it simultaneously, when input patterns change, when infrastructure hiccups—that requires a different skill set entirely. This team ensures that "it works on my machine" becomes "it works everywhere, always."

Technologies: Docker, Kubernetes, MLflow, Grafana, Prometheus, Redis, FastAPI, AWS/cloud services, model quantization tools, edge computing platforms

How the Verticals Work Together

Here's a typical flow through our team:

  1. AI Solutioning talks to stakeholders, understands the problem, and designs an architecture
  2. Data Pipelines builds the infrastructure to collect and process the necessary data
  3. Model Building develops and trains the AI models using that data
  4. LLM Ops deploys the system and monitors it in production
  5. Data Pipelines continues feeding new data to keep models fresh
  6. Model Building retrains models when performance degrades
  7. AI Solutioning iterates on the design based on user feedback
sequenceDiagram
    participant S as AI Solutioning
    participant D as Data Pipelines
    participant M as Model Building
    participant O as LLM Ops
    
    S->>D: We need data from sensors X, Y, Z
    D->>D: Build ingestion pipeline
    D->>M: Here's clean, structured data
    M->>M: Train and validate models
    M->>O: Here's the trained model
    O->>O: Deploy to production
    O->>S: System is live, here's performance data
    S->>S: Gather user feedback
    S->>D: We need additional data source
    Note over D,O: Continuous iteration

Our Philosophy

We don't chase hype. Every few months there's a new "breakthrough" in AI. We evaluate technologies based on whether they solve real problems, not whether they're trending on tech forums.

We build for production. A demo that works 80% of the time isn't good enough. We focus on reliability, monitoring, and graceful failure handling because that's what separates toys from tools.

We respect specialization. Each vertical has deep expertise in their domain. Data engineering requires different skills than model development, which requires different skills than operations. We don't expect everyone to be experts at everything.

We communicate constantly. The verticals are interdependent. When Data Pipelines discovers data quality issues, Model Building needs to know. When LLM Ops sees performance degradation, AI Solutioning needs to reconsider the architecture. We have short feedback loops.

The Technical Stack

Our technology choices reflect pragmatism over dogma:

  • Languages: Python dominates (PyTorch, TensorFlow, FastAPI, Pandas), with JavaScript for interfaces
  • Data: PostgreSQL for structured data, InfluxDB/TimescaleDB for time-series, vector databases for semantic search
  • Streaming: Kafka for high-throughput, MQTT for IoT devices
  • ML Frameworks: PyTorch for deep learning, scikit-learn for classical ML, specialized libraries for computer vision and NLP
  • LLMs: OpenAI APIs, Anthropic Claude, open-source models when appropriate
  • Infrastructure: Docker for containerization, cloud platforms for scalability, edge devices for low-latency applications
  • Monitoring: Grafana, Prometheus, MLflow for tracking model performance

Team Composition

Each person has a primary vertical but the team is small enough that everyone understands what others are doing. Flexibility matters.

What We Value

Measurable results over impressive demos. We track metrics: accuracy, latency, uptime, cost per inference. If we can't measure it, we can't improve it.

Documentation and reproducibility. Every pipeline, model, and deployment should be reproducible. Future us (or new team members) shouldn't have to reverse-engineer what past us built.

Incremental progress. We ship working systems, then iterate. Perfect is the enemy of shipped.

Learning from failure. Models fail. Pipelines break. Deployments have bugs. We do post-mortems, document what went wrong, and build better systems.

The Reality of AI Work

Most of our time isn't spent training cutting-edge models or architecting elegant systems. It's:

  • Debugging why a data pipeline stopped working at 3 AM
  • Figuring out why a model that worked yesterday is failing today
  • Optimizing inference speed because users won't wait 10 seconds for results
  • Writing documentation so we remember what we built
  • Handling edge cases we never anticipated

This is the unglamorous reality of production AI. We've made peace with it.

Looking Forward

AI is evolving rapidly. New models, new techniques, new tools emerge constantly. Our structure—four verticals with clear responsibilities—gives us flexibility to adopt new technologies without reorganizing the entire team.

When a new capability becomes production-ready, we evaluate:

  • Does it solve a real problem better than current solutions?
  • Can we integrate it into our existing infrastructure?
  • Do we have the expertise to deploy and maintain it?
  • What's the cost-benefit tradeoff?

If the answers are favorable, we experiment, iterate, and potentially incorporate it. If not, we keep watching and wait for the technology to mature.

We're not the biggest AI team. We're not the flashiest. But we're focused on building systems that work, that solve real problems, and that we can maintain long-term. That's the work that matters.