AI cannot understand your business if your business meaning is scattered across docs, dashboards, Slack, and people’s heads. This article shows how to build a context layer so every AI tool knows your definitions, rules, and trusted sources at the moment it answers.
Published on: June 24, 2026·8 min read
Your AI assistant says "active customer" means one thing in a renewal email, another thing in a revenue report, and a third thing in a support answer.
The model is not stupid. It is missing your company's meaning layer.
Most AI failures inside companies are not reasoning failures. They are meaning failures. The AI does not know whether "customer" includes trials. It does not know which revenue number Finance trusts. It does not know that enterprise renewals need legal approval, or that one customer segment follows a different onboarding path.
Humans know these things because they work inside the company. AI tools do not, unless that knowledge is written down, owned, kept current, and available at the moment they answer.
That is what a context layer does.
A context layer is the maintained source of your company's definitions, rules, ownership, and provenance, served to every AI tool at inference time. It is not a bigger prompt. It is not a wiki. It is the part of your AI stack that tells the model what your business means.
Here is the practical path to building one without boiling the ocean or creating a chore nobody maintains.
Step 1: Start where your AI is already wrong
Do not try to document the whole company. List the questions your AI tools get wrong today.
Maybe "active customer" has three different answers. Maybe the support bot quotes the wrong refund window. Maybe an agent reports last quarter's revenue using a definition Finance does not trust.
That list is your backlog. It is short, specific, and tied to real failures. Everything else can wait.
For example, take "active customer." One team may mean any account with a paid subscription. Another may mean any account with product usage in the last 30 days. Finance may exclude trials, paused accounts, internal test accounts, and customers with failed payment. If AI tools do not know which definition applies, they will sound confident and still be wrong.
Step 2: Write down what your data means, but do not start from a blank page
A blank glossary is a project nobody finishes. So do not author one from scratch.
Pull a first draft from where your definitions already live: dbt models, warehouse tables, metrics layers, dashboards, CRM fields, finance docs, and internal notes. Then correct it.
That is what turns a note into something an AI tool can trust.
For "active customer," the definition might come from a billing table, usage model, and revenue dashboard. The context layer should know which source is authoritative, which exclusions apply, and who owns the definition when it changes.
Step 3: Capture the rules that are not in any schema
Schemas tell you what fields exist. They do not tell you how the business actually works.
This is where the real knowledge lives: why one account gets a discount, how a renewal gets approved, which customers are sensitive, what counts as a successful onboarding, when support should escalate, and which revenue number should be used in a board update.
Get those rules out of people's heads, Slack threads, and buried docs into something structured. Keep the review step light: the owner of each area confirms what becomes canonical.
You are not writing a policy manual. You are writing down the handful of rules your AI keeps getting wrong.
For "active customer," the rule may be:
Include paid customers with meaningful product usage in the last 30 days
Exclude trials, sandbox accounts, paused subscriptions, internal workspaces, and accounts with unresolved payment failure
Use Finance's definition for revenue reporting and CS's definition for health scoring
Same phrase, different business use. That distinction is exactly what AI needs.
Step 4: Serve it to the tools, not to a wiki
A wiki helps a human who already has context. It does little for an agent answering a question at 2am.
The point is inference-time access: the AI tool reads the relevant context at the moment it answers.
In practice, that means exposing definitions, rules, and provenance through an interface AI tools can use, such as MCP, the emerging standard for connecting AI systems to external tools and context. ChatGPT, Claude, Cursor, and your own agents should all read from the same source, filtered by who is asking and what they are allowed to see.
One source, every tool. Not a separate explanation for every model, prompt, assistant, workflow, and agent.
Step 5: Make every answer traceable
If you cannot tell what an AI answer was based on, you cannot safely put it in front of a customer or an executive.
Every answer should trace back to the definition, rule, and data it used.
The useful standard is not "the AI said so." It is:
The AI used this definition, owned by this person, confirmed on this date, using these source systems.
For an "active customer" answer, the trace might point to the Finance-owned definition, the billing table, the product usage model, the exclusion rule for paused accounts, and the date the definition was last approved.
That is what makes AI operational instead of merely impressive.
Step 6: Keep it fresh without a governance team
Context that goes stale is worse than no context, because then the AI cites wrong definitions with your company's authority behind them.
The system has to maintain itself as much as possible.
Refresh from the sources you connected. Detect drift when a dashboard, dbt model, CRM field, billing rule, or policy doc changes. Surface the difference to an owner as a quick confirmation, not a weekly governance ceremony.
If keeping the context current requires a dedicated team, it will rot, especially at a 40-to-200-person company that does not have one.
The work should be curation, not authoring. Confirmation, not archaeology.
Why this matters now
This used to be annoying. Now it is dangerous.
When AI only drafted internal copy, missing business context caused rework. When AI answers customers, summarizes accounts, queries revenue, recommends renewal actions, or updates CRM, missing context causes wrong decisions.
The more useful AI becomes, the more it needs access to the meaning behind your company data. Otherwise, every team ends up teaching every tool separately, and every answer becomes a little harder to trust.
What not to do
Do not put it all in the system prompt. It does not travel across tools, does not update when a definition changes, and breaks when the model composes queries against live data.
Do not document everything first. Start with what your AI is getting wrong. Coverage comes later.
Do not build a beautiful wiki and stop. If no tool reads it at inference time, it is decoration.
Do not make it a manual authoring project. Seed it from existing sources and confirm. Starting from scratch is how these efforts die in week three.
Do not let every team define the same metric separately. That is how AI ends up giving different answers depending on which workflow asked the question.
How small a team can do this
You do not need a governance function or a quarter-long rollout.
A 40-to-200-person company can stand this up with one part-time owner if the system seeds itself from existing tools and keeps itself current. The work is confirming and curating, not writing a corporate encyclopedia.
Start with the five to ten questions your AI already gets wrong. Fix those. Add ownership and provenance. Serve them to the tools. Then expand from there.
The shorter version
AI does not understand your business because your business meaning is scattered across schemas, docs, dashboards, Slack, and people's heads.
The fix is not a bigger prompt or another wiki. It is a context layer: one governed source of definitions, rules, ownership, and provenance that every AI tool can read at the moment it answers.
That is what we are building Sento to do: AI that knows your company.
Frequently asked questions
How do you make AI understand your business?
Write down what your data means and how your company works, give each definition and rule an owner, serve that context to every AI tool at the moment it answers, make every answer traceable, and keep the context current automatically. Start with the questions your AI already gets wrong.
What is a context layer?
A context layer is the maintained source of company meaning for AI: definitions, rules, ownership, provenance, and permissions, served to AI tools at inference time.
Where do you start?
Start with the questions your AI tools get wrong today, not with documenting the whole company. That short list tells you which context matters first.
Do you have to write all the definitions by hand?
No. Seed a first draft from existing sources like dbt, the warehouse, dashboards, CRM, docs, and support policies. Then have the right owner confirm or correct it.
Why not just use a wiki?
A wiki is written for humans who know where to look. AI tools need structured, permission-aware access to the right context at the moment they answer.
How big a team does this need?
A 40-to-200-person company can run it with one part-time owner if the system seeds itself and keeps itself current. The work is curation, not governance theater.
