dooopSoftware · Product · 12 min

How to Prepare Data for AI in Features

Before connecting databases, validate whether each piece of data changes the AI's response, has clear permission, and arrives on time in the product's real flow.

Published on September 6, 2026

CORE THESIS

Available data is not context. It only enters if it changes the response and can operate in the real flow.

Relevance comes before integration. Access, permission, and exclusion define the first version.

Preparing data for AI in the product does not mean cleaning everything, connecting everything, and waiting for artificial intelligence to find value on its own. The work begins by transforming inventory into operational context: verifying if each piece of data is relevant to the feature’s task and accessible at the real moment of use. If the data does not change the response, cannot be used with clear permission, or arrives too late, it is still just inventory.

When Data Exists but Does Not Serve the Feature

A common mistake starts in an apparently pragmatic meeting. Someone asks what data the company has. The list grows quickly: usage history, customer registration, knowledge base, tickets, internal notes, product events, conversations, commercial documents, service records.

The list may seem sufficient to create an intelligent feature.

But existence is not utility.

An artificial intelligence feature in software needs data that helps a specific task. Data for AI in the product is not an abstract lake of information. It is selected evidence to improve a decision within a flow. Anthropic defines context engineering as the selection and maintenance of information available to the model during inference, including instructions, tools, external data, and history, within a limited window (Anthropic). This definition helps separate two things many teams confuse: having stored data and delivering useful context at the moment the AI needs to respond.

The question, therefore, is not “which databases can we connect?”. The question is: “which information changes the response of this feature, for this user, at this moment in the flow?”.

This difference changes leadership work. Instead of approving a generic data organization effort, leadership approves a scope: task, evidence, permissions, update, access, and trust limits.

Define the Task Before Listing Databases

An intelligent feature needs to fit into a product verb. It can suggest, classify, summarize, fill, prioritize, explain, compare, or trigger a flow. If the team cannot name this verb, data preparation is still too early.

“Use AI in support” is broad. “Suggest an initial response for the agent based on the ticket, the contracted plan, and current articles from the knowledge base” already allows discussing data. “Help the account manager” is broad. “Summarize recent changes in product usage before a meeting with the client” already creates a context question.

This definition reduces two scope risks.

The first is preparing data because it is available, not because it is necessary. The second is confusing a product feature with an exploration project. Discovery is the moment to understand the opportunity. Hypothesis is an explicit bet on what can improve. Experiment is the controlled test of this hypothesis. Skipping this separation leads to costly integrations that still do not answer a product question.

A useful formulation is: “to perform this task, what evidence would make the AI respond differently?”.

If the evidence does not change the recommendation, classification, summary, or action, it should not enter the first version. It may be interesting for future analysis. It may help another flow. It may feed a business decision. But it is not, therefore, relevant data for this feature.

This reasoning also connects to a larger discussion about product direction. A business-connected artificial intelligence strategy helps prioritize where AI should enter. In this article, the scope is smaller and more operational: once the candidate feature exists, which data deserves to enter its context?

Use Relevance as the First Filter for AI Data in the Product

Relevance is the relationship between data and the decision the feature needs to support. Relevant data reduces ambiguity, confirms a constraint, updates a condition, or prevents an inappropriate response. Irrelevant data only increases noise.

Before discussing connectors, pipelines, or models, it is worth applying five simple filters:

Direct relation to the task: does the data influence the expected response or just seem complementary?
Ambiguity reduction: does it help choose between possible responses?
Compatible update: will the information be sufficiently current for the decision the AI will support?
Minimal traceability: is it possible to know where it came from and why it was used?
Risk of error: if wrong, outdated, or out of context, could it induce a worse response than the absence of data?

Fictional example: imagine a support SaaS that wants to suggest responses to the agent. The feature will not send the message automatically. It only prepares a suggestion for human review within the ticket screen.

In this case, some data seem relevant: ticket category, description sent by the client, contracted plan, current articles from the knowledge base, and last resolved tickets from the same client. Each can change the suggested response. The contracted plan can limit an instruction. The ticket category can direct the correct article. Recent tickets can prevent the AI from repeating guidance that already failed.

Other data may exist but remain outside the first version: old internal comments, loose commercial notes, undated documents, messages from parallel channels, and records without clear ownership. They may have value in another process but, to suggest a support response, they may confuse the context or expose information that should not appear in the agent’s flow.

This is an operational and decisive point. Excess context can degrade the product by increasing latency, cost, data exposure, and chance of contradiction. Anthropic recommends starting with the simplest solution and adding complexity when necessary while discussing agent patterns and flows with models (Anthropic). In the same reference, Anthropic distinguishes flows with predefined paths from agents that dynamically decide their process and tool use. Here, the same principle applies to data: start with the minimal set that supports the task.

Verify Access at the Real Moment of Use

After relevance comes access. And access is not “someone can export this data”. Access, in product, means the feature can receive the right data, in the right format, with adequate permission, at the moment the AI responds.

This verification needs to pass operational questions:

Permission: can the current user see this data? Can the feature use it for this purpose? Can the model receive it in that context?
Availability: is the source accessible during the flow or does it depend on later reconciliation?
Update: does the data reflect the current situation or could it be outdated?
Acceptable latency: does fetching this data make the experience too slow for the task?
Format: is the information structured enough to be used or will it require fragile interpretation?
Granularity: does the AI need the entire document, a snippet, an attribute, or a calculated state?
Origin: is there a preferred source when two databases diverge?
Profile restriction: do administrators, agents, managers, and clients see the same context?

This point avoids a frequent trap. In a demonstration, it is possible to manually prepare a data set, paste snippets into the prompt, and get a convincing response. In the product, the question is different: when a real user triggers the feature, will the AI receive that context without improvisation?

If the answer is no, the data should not yet be treated as part of the feature. It may be a future dependency. It may require work on permission, governance, integration, or flow design. But it should not support the first version’s scope.

The discussion approaches organizational maturity. An AI maturity diagnosis does not depend only on the chosen technology. Maturity appears in the ability to define context, control access, measure behavior, and decide limits.

Build a Minimal Inventory for the First Version

Useful inventory is not an encyclopedia of corporate databases. It is a decision list for the feature.

For each candidate data, record in simple language:

Necessary data: what information the AI needs to receive.
Origin: system, document, database, or event where the data comes from.
Owner: area or role responsible for the quality and meaning of that data.
Purpose in the response: why this data changes the suggestion, classification, or action.
Update frequency: when it changes and when it becomes risky to use.
Permission: which profiles can access it and under what conditions.
Access method: how the feature will receive the data in the real flow.
Risk if absent: what the AI should do when the information does not exist.
Manual alternative: how the user solves it today without automation.
Exclusion criterion: in which situation the data leaves the context.

The presence of the “exclusion criterion” is deliberate. Many initiatives only have criteria to add data. Few define when to remove. This creates features increasingly full of context but not necessarily more reliable.

In the fictional support SaaS example, the team could decide that knowledge base articles only enter if they have an owner, validity date, and published status. An old article without a clear responsible should not appear just because it is stored. Similarly, resolved tickets may enter as reference, but internal comments marked as restricted remain blocked.

Less data, when well chosen, can generate a more controllable experience. Not because simplicity is an abstract virtue, but because each included data creates an obligation: maintain, authorize, update, explain, and monitor.

This inventory also helps build an AI roadmap with real dependencies. Instead of “connect CRM”, the task becomes “allow the response suggestion to consider the contracted plan with reliable update and permission by profile”. It is a small difference in phrase and a big difference in execution.

Test the Hypothesis Before Expanding the Context

After selecting relevant and accessible data, the team has not yet proven that the feature delivers value. It has only created a testable version.

The hypothesis needs to be explicit. In the fictional example: “if the AI has access to the contracted plan, ticket category, current articles, and last resolved tickets, then suggestions will tend to present fewer incompatibilities with the client’s situation in a controlled sample”.

This is a hypothesis, not a result. The next step is to define how to observe if it holds.

Microsoft describes its ExP platform as a way to incorporate experimentation into the development cycle, validate hypotheses, measure impact, and iterate products (Microsoft Research). This reference does not mean every product needs a sophisticated platform nor that user feedback automatically retrains a model. The applicable point is more direct: treat the feature as a product hypothesis that needs measurement before expanding scope.

For a first evaluation, the team can compare samples of suggestions with and without certain data, review cases where the AI used outdated information, identify responses blocked by lack of permission, and observe when the agent discarded the suggestion due to incompatibility with context. Before connecting new sources, define which evidence will show that the data improved the feature.

It is also worth separating service reliability metrics. Google SRE defines service level objectives as reliability goals that guide engineering decisions, with agreement on targets, error budgets, and review processes (Google SRE). In an intelligent feature, this logic helps discuss operational limits: when the response should be delivered, when it should fail safely, and when the product should route to a human flow.

It is not necessary to turn this agenda into a deep discussion of latency, cost, or production. But it is dangerous to ignore that data have operational behavior. A slow, unstable, or overly permissive source can make the feature inadequate even when the model seems good.

Relevance and Access Matrix Before Building

Use this matrix as an approval conversation before investing in building the first version.

User Task

Does the feature have a specific task or is it trying to use AI generically?

If the task does not fit into a product verb, such as classify, suggest, summarize, or prioritize, data preparation is still premature.

Relevance

Does this data change the AI’s response, recommendation, or action?

If the data only seems interesting but does not alter the feature’s decision, it stays out of the first version.

Moment of Use

Will the data be available when the AI needs to respond?

If access depends on manual export, later reconciliation, or intervention outside the flow, it should not be treated as operational context.

Permission

Can the user, feature, and model access this data in that context?

If permission is not clear by profile, account, or purpose, the data should be blocked until review.

Update

Is the update frequency compatible with the decision the AI will support?

If outdated data could generate an inappropriate recommendation, the feature needs alert, alternative, or exclusion of that data.

Reliability

Is there a criterion to know when the data is absent, divergent, or insufficient?

If the AI cannot recognize absence or low confidence, the product should limit the response or route to a human flow.

Risk of Excess

Does adding this data increase noise, cost, latency, or exposure without clear gain?

If the gain is not demonstrable in testing, the data does not enter the initial context.

Decide What Stays Out of the Feature

Preparing data for an intelligent feature is also deciding what does not enter.

Data without reliable update, clear permission, direct relation to the task, or with high exposure risk should stay out of the first version. This is not technological conservatism. It is product design. An intelligent feature does not improve because it received more information. It improves when it receives the right context to support a real task, with limits the product can explain and operate.

Leadership should approve a simple relevance and access matrix: which data the feature needs, why they influence the response, who can use them, with what update, at which moment in the flow, and with what reliability limit.

The matrix should end with a deliberate exclusion: which data, if wrong or absent, makes the feature worse than the current flow?

Until this answer exists, the first version should be smaller, more verifiable, and easier to operate.

If you want to discuss this decision in your company’s context, talk to dooop.

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