Answers
How do I give AI accurate business context?
You give AI accurate business context by supplying the four things a model cannot infer on its own: one resolved identity per customer across every system, a single governed definition for each business metric, data current at the moment of the question, and unstructured sources like calls and tickets joined back to accounts. Prompting and model choice do not affect any of the four.
What “business context” actually means
Business context is not your data. It is the set of agreements that make your data interpretable: that these six records are the same customer, that “active” means this and not that, that this revenue number is the one finance would defend, and that the reason a deal stalled is sitting in a call transcript rather than a field.
A language model arrives with none of those agreements. It is a capable reasoner with no knowledge of your company, so it reconstructs the missing agreements on the fly, per question. That reconstruction is where wrong answers come from — not from the reasoning, which is usually fine.
This is why the problem does not look like failure. The model returns a specific number with a confident explanation. Nothing in the output signals that it guessed which rows belonged together.
Why prompting and bigger models don't fix it
Prompt engineering changes how a question is framed and how an answer is worded. It cannot tell the model that two customer records are the same company, and it cannot establish which of three competing definitions of churn your board uses.
The same is true of upgrading models. A stronger reasoner applied to ambiguous inputs produces a more articulate version of the same ambiguity. Context is an input problem, not a reasoning problem.
Why connecting the model to your systems isn't enough
The usual next step is to connect an assistant directly to source systems, typically over MCP. This is a real improvement on two fronts: it reaches systems that were previously unreachable, and a live query beats last night's batch export.
What it does not do is create agreement between those systems. MCP has no concept of a resolved account key, so the model still infers the join — and infers it differently on each query. It has no shared glossary either, so a definition gets invented per question. Two people can ask the same thing and get two different numbers, both citing real records.
Access and agreement are separate problems. Connecting more systems to a model that cannot reconcile them tends to produce more confident disagreement, not less.
What a business context layer supplies
A context layer sits between your systems and whatever AI consumes them, and it owns the agreements rather than leaving them to the model. Entity resolution produces one verified account key, so a join is looked up rather than guessed. A governed glossary gives each metric one enforced definition. Monitored pipelines keep data current and make staleness visible instead of silent.
It also brings unstructured data into scope. Calls, tickets, and emails hold the reasons behind the numbers, and they are useless to an AI until they are joined to the accounts they describe.
The last property matters most in practice: every answer carries its sources and lineage, so a person can verify it before acting. An answer nobody can check is not usable for a decision, however accurate it happens to be.
Build it or buy it
Each piece is a known discipline with a known cost. Entity resolution is a master data management programme. A governed glossary is a semantic layer plus the governance to keep it true. Freshness is orchestration and pipeline observability, which means someone is on call permanently. Unstructured coverage is an ingestion and retrieval pipeline.
These are all buildable. The reason teams buy instead is that the work never finishes — every new system, metric, and acquisition reopens all four — and the cost lands on data engineers who were hired to do something else.
Riley provides the four as a managed layer, and exposes them through MCP so the assistants your teams already use keep working as they do today.
Related questions
Is business context the same as a data warehouse?
No. A warehouse centralises storage; it does not establish that two records are the same customer or that a metric has one definition. Riley works alongside the warehouse and treats it as a source, not a replacement.
Do I need to clean my data before giving AI business context?
A cleanup project improves the inputs but leaves interpretation to each tool separately, so the same ambiguity returns the moment a new system is added. A context layer holds the agreements centrally, which is why it is usually faster than cleaning first.