Let's build together
Four ways to work with us, from a strategy workshop to a full delivery team. All of them end with something running in production.
Capabilities
What we build
The skills we bring to every engagement, whichever model you choose.
AI agents & MCP integrations
Agents that do real work inside your systems: read, decide, act and leave a trace. We build the tools they call, the guardrails around them and the MCP servers that expose your data safely.
- MCP servers with OAuth2, fine-grained authorization and distributed tracing
- Agent workflows on LangGraph, Agno or plain Python, with a human in the loop where it matters
- Evaluation harnesses so every prompt, tool or model change is measured before it reaches users
- Track record: a Fortune 500 company's first production MCP server and an agentic research platform spanning 12 data sources
LLM applications & retrieval
Retrieval, extraction and generation pipelines over your documents, images and events. We care as much about what the model gets wrong as what it gets right.
- RAG and vector search tuned on your data, from thousands of PDFs to millions of images
- Structured extraction and metadata pipelines that turn unstructured content into queryable tables
- Model choice by measurement: Claude, Gemini, OpenAI, Mistral or open weights, whichever wins your eval
- Cost and latency budgets agreed up front, with caching and routing to stay inside them
ML systems & forecasting
Classical machine learning still pays the bills. Anomaly detection, fraud and bot detection, forecasting and personalization, deployed with monitoring rather than left in a notebook.
- Anomaly detection across tens of thousands of SKUs that cut an e-commerce client's operational costs by about 80%
- Fraud and bot detection with 700+ features running in production
- Personalization A/B tests that delivered a 5% revenue uplift
- Marketing mix models and demand forecasts your finance team can actually use
Data platforms & MLOps
The unglamorous layer everything else depends on. We build warehouses, pipelines and deployment paths a small team can run, and we keep the cloud bill honest.
- Warehouses and pipelines on BigQuery, Snowflake, ClickHouse, dbt and Airflow
- Training and serving on SageMaker, Vertex AI or Kubernetes, with Terraform for all of it
- Cloud cost reviews that have cut spend by 70% without slowing anyone down
- Observability for data and models: lineage, drift, tracing and alerts
Engagement
How we work
Pick the engagement model that fits where you are today.
Training & Strategy
Strong data competencies and a clear strategy separate companies that talk about AI from companies that run on it. We help you set up both: project management that fits data work, reliable data engineering and modern data science and AI practice.
We have built analytics teams and delivered 250 hours of hands-on data science training for a $3B+ revenue enterprise. Sessions are practical and, where possible, run on your stack and your data.
Start with a workshop. Leave with a written plan and a first project scoped.
Mention "Training & Strategy" in your message so we can route it quickly.
Sample recommendations
Team Augmentation
Most in-house teams are not built around data scientists and ML engineers, and many AI initiatives start as a proof of concept nobody has headcount for. That is where a temporary senior hire makes sense.
We fill the competency gap with people who have shipped production systems, and back them with the frameworks and checklists we have refined across our own projects.
You keep ownership of the code, the roadmap and the knowledge. We make sure it is documented before we leave.
Mention "Team Augmentation" in your message so we can route it quickly.
Sample recommendations
End-to-end Projects
Some projects need a project manager, analysts, data scientists, data and DevOps engineers and a full-stack developer working together. We bring the whole team and the processes that let them start on day one.
That is how we took an e-commerce client from a data strategy to a cloud implementation and an ML anomaly detection system that cut operational costs by about 80%.
Where a project needs a rare skill, we draw on a network of engineers we have worked with before.
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Sample recommendations
Non-standard Projects
A creative mess sometimes produces the best ideas. If you have an unorthodox use case or simply suspect there is value hiding in your data, talk to us before you commit budget.
Our ideation workshops have turned rough ideas into scoped projects. Just as often, an early conversation about data limitations has saved a partner months of effort and a lot of money.
Either outcome is a good one.
Mention "Non-standard Projects" in your message so we can route it quickly.