An agent walks into your data warehouse and asks, "Who is our biggest customer?" It gets three answers and a 2019 dashboard nobody has the heart to delete.
We fix that first: matched records, agreed definitions, clear sources, and sensible access rules. It's the unglamorous foundation the exciting stuff stands on, and we build it around one real use case at a time.
The warehouse is solid, the dashboards mostly behave, and someone just asked whether an agent can use it. We test the platform against the use case, keep what works, and name the specific gaps worth fixing.
Customer records don't match. "Revenue" has three definitions and a footnote. Reporting depends on one heroic analyst. We connect the sources that matter for the job and write down the rules the business will live by.
We don't open with a platform pitch. We review what your teams already run and separate data problems from process problems and software problems, because each one has a different fix. If a vendor's built-in agent does the job, we'll help you feed it properly. If a simpler change is enough, the engagement ends there and you keep the budget.
Your CRM thinks Acme is one company. Billing thinks it's four. Marketing spelled it "ACME, Inc." The context layer settles the argument: one customer, one definition of "active," one rule for which system wins when they disagree, all written down so reports and agents read the same answer.
Customer records Matched across the systems the project needs. Uncertain matches get flagged for a human, never guessed.
Definitions Active customer, product usage, revenue. Each has an owner and a change history, so nobody relitigates it in the QBR.
Sources and freshness Where every fact came from and how old it's allowed to be. Stale or missing data stays visible.
Access Your rules on who sees what and what an agent may do with it. Actions that need a human stay with a human.
We pick one report, question, or workflow that's painful today. We trace the systems and definitions behind it, including the work your team has already done, and agree what has to change for that use case. The records and rules we set up become the starting point for the next one, and we check each new use case on its own merits.
A seller has a renewal call at 2pm. The brief pulls the right account, recent activity, open support tickets, and notes from the last conversation, with a timestamp on each and a clear flag where something's missing. The seller still decides what to say. They just stop spending lunch reconciling tabs.
We write the test before we build: the questions it must answer, the data it can touch, and the mistakes it can never make. For an agent, that means checked answers, access and allowed-action tests, human-review rules, and a record of every action. Your reviewers see the evidence and make the call.
It depends on the scope. Typical outputs include an architecture assessment, tested data models, a shared customer record across selected systems, a metric catalog, a business context repository, approved ways for agents to retrieve information, and evaluation results against an agreed test set. We document what it does, what it doesn't, and who keeps it running.
Tell us the report, decision, or AI use case that's hard to support today. We'll tell you what it would take to make it boring.