Accelerate your AI Investments, Measure the Impact
Data is the foundation of AI. If your data is everywhere, for an agent it may as well be nowhere. Agents need context and guardrails. We build the foundation, measure the impact, and keep your data secure.
You've started using AI, and now you need measurable impact
If you have used an AI tool for coding, research, or writing, you know it can dramatically improve your personal productivity, up to a point. Then you need to work with others, and with their agents too. Every request queries the same data, over and over. It learns from you, but not from the organization. It is blind to your business data.
Then comes the typical "AI agency." They will build you some chatbots. The big tech vendor is selling you solutions far out on the horizon compared with where you are now. You need to accelerate for real, and justify the spend.
You are not alone: 91% of middle-market firms use generative AI, and only about one in four has it in core operations (RSM, 2025). A DataMind Diagnostic tells you which side of those numbers you are on.
What we build for you
Data an agent can find
Your data is in too many places for an agent to use. We build one search index and knowledge graph, connected to your agent platform.
An agentic engineering kit
Your developers use AI alone. We hand you a working kit: agents on your tickets and code, connected to your graph.
Answers, not dashboards
Reports pile up and few people open them. An agent pulls the data and builds the chart on the fly.
Agents on customer data, securely
Your agents need customer data, and compliance needs a log. Agents get the same access rules as people.
Four deliverables, each measured
We have built full-cycle SDLC and CI/CD with agents, and full-stack web and data lake applications. We have been in the shiproom for more than 20 years, delivering the metrics that prove the software is live. We bring our best practices from big and small tech to your agent infrastructure. We build the skills, the harnesses, and the knowledge graph, using proven design principles that scale to petabytes and are just as valuable at terabytes or gigabytes.
Data an agent can find. We consolidate your data into an agent-friendly search index and knowledge graph, connected to the agent platform you choose. The agent gets the context: what is, and what is important.
An agentic engineering kit. Our own systems, Automaton and Ticketron, are the starting kit: an agent interface to your tickets and code, and a working coding agent connected to your graph. We train your team on the parts you need.
Answers, not dashboards
Business intelligence should answer whoever asks, a person or an agent, on the fly. An agent pulls the data from your lakehouse and builds the chart when the question is asked, so nobody waits for another report. On average, about a quarter of employees actively use BI tools, a rate that has barely moved in seven years (BARC). We have built hundreds of dashboards. Maybe two had returning users a year later.
We build it on a knowledge graph and the best framework for the job: LangGraph, for example, to run the language model, and Databricks or Python to reach your data.
The stack runs on Snowflake, Microsoft Fabric, and Databricks, so you are not locked into one platform. We are certified on Databricks and recommend it where it fits and when you build from scratch: data engineering, model training, MLOps, and model serving are in one place.
Access rules stay in force: the agent acts with the identity of the person asking, calls only the functions we build and test for it, and every read is logged.
Each one starts with a Diagnostic and is measured from the start: tasks done, success rate, escalations, and cost per task.
We have shipped each of these before
Data an agent can find: at a national ed-tech provider, an agentic AI platform on Databricks, shipped in 3 months, unifying six legacy products behind one AI interface and data platform.
An agentic engineering kit: Automaton, our agent-run SDLC and CI/CD system, built a client's production document product.
Answers, not dashboards: lead data scientist to Microsoft's CSO and CFO. Built Microsoft AI products, PowerPoint Designer and Word Editor: both suggest, and a person accepts or rejects, shipped to millions of people.
Agents on live data, securely: spam detection for major US wireless carriers, a scored model ranking billions of calls, with analysts working the queue it produced and MLOps that moved releases from every 18 months to monthly. At Bing, the detection that kept click bots out of ad spend and metrics.
Co-inventor, Microsoft patent US 10,839,149 B2. MBA, Economics, Entrepreneurship and Technology. MS, Data Science.
Databricks Certified Data Engineer Professional
We build it with our network of experts: senior engineers, data scientists, and security partners we have worked with for years, brought in when the work needs them.
References
Microsoft Learn, user authorization for apps on Azure Databricks, updated October 7, 2026
Bring one job your team hates.
In a free discovery session we pick one task, define what done looks like, and sketch the first version. Low risk: it drafts and your person approves. If a setting in a tool you already own would do it, we say so. Not sure which job to hand an agent first? The DataMind Diagnostic scores your options on your own data, for a fixed fee.