AI integration: document search, summaries and enquiry processing

• Reduce time spent on repetitive tasks
• Test quality on your own examples
• Retain control of access, actions and cost

A project begins with an operational question: which task takes time, what information must be consulted and how do we recognise a good answer? A general ambition such as “bringing AI into the business” allows neither the work to be priced nor its outcome to be checked. We define a use case, its users and permitted actions before choosing technology.

Possible use cases include searching internal documentation, drafting sales responses, classifying enquiries, summarising reports and extracting fields from documents. A conventional rule remains preferable when sufficient: checking that a field is completed or routing an enquiry by postcode does not necessarily require a generative model.

Planning distinguishes time spent producing a suggestion from time needed to check it. A quick task whose output is difficult to verify may offer less value than repetitive work with clear criteria. We select an initial scope whose results can be compared with your current method.

You may already have an AI feature in your CRM, support tool or document environment. We check its capabilities before adding a subscription. A custom solution becomes worthwhile when rules, sources or the user journey are not adequately covered. The choice considers expected quality, access, cost and ease of maintenance.

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A demonstration using three examples is not enough to decide. Constraints often emerge with long documents, ambiguous cases and permissions across users.

Imagine a fictional company whose teams search for procedures across several folders. The pilot uses an approved collection: current documents, identified owners and known access rules. The assistant retrieves relevant passages, prepares an answer and lets users open the documents used. This approach, often called RAG, supplies documentary evidence to the model when it answers.

A question such as “What should be checked before closing this case?” may produce a list of steps accompanied by sources. If the document collection does not specify who authorises an exception, the assistant should identify that gap and direct the user to the appropriate person. It must not quietly complete the procedure with an invented rule.

The journey also includes updating and withdrawing documents. An archived former procedure must not continue informing current answers. Permissions are checked before retrieving content: an assistant must not give users information they could not access directly. Document references make verification easier; they do not, on their own, guarantee an accurate answer.

AI can suggest an email, category or summary. That does not mean it may send a message, change a record or delete a document without checks. We separate the model’s suggestion from the executed action. Authorised operations, their parameters and the person’s permissions are checked within the application.

Documents, messages and pages consulted may contain unwanted instructions. They are treated as information to analyse, not as instructions capable of changing permissions. Access remains limited, outputs are checked according to their destination and sensitive actions require explicit human approval. No single filter removes all risks associated with such manipulation.

The planning document specifies what data is sent to the provider, retention conditions to verify and functions that can be disabled. We also provide a usage limit and a way to stop the workflow. A technical integration and a review appropriate to your circumstances are necessary; adding AI does not automatically establish compliance.

Together, we build a set of representative situations separate from the examples used to tune the solution. It includes simple cases, ambiguous requests, incomplete documents and questions the tool should not answer. Expected answers and acceptance criteria are defined with someone who understands the work.

For document extraction, we check critical fields individually: correct amount, date and case, with missing data properly flagged. For an assistant, we assess relevance, documentary support, omissions, appropriate human escalation and the absence of prohibited access. An answer that reads well can still be unusable or incorrect.

The test report presents errors, their severity and required corrections. If a model version or important document changes, the same test set helps detect regressions. The decision to open the tool to more users depends on these observations, cost and the ability to handle exceptions, not a marketing percentage selected in advance.

Metrics follow completed work: total time per task, proportion of suggestions accepted, corrections needed, serious errors, requests escalated to a person and average cost. We compare similar volumes and difficulty levels before and after the pilot. The number of conversations measures usage; it does not prove profitability.

Purely illustrative example: preparing and checking a report takes twenty minutes. AI assistance reduces preparation time, but the full task still takes twelve minutes including review. Across 150 reports, the potential is twenty hours freed up. Administration, incidents and any rework must then be deducted. If checking takes as long as before, the benefit needs to be reconsidered.

Costs include licences, model calls, text volume processed in tokens, storage, document retrieval and necessary connections. We track an understandable unit, such as cost per case processed, with a cap and alerts where the infrastructure supports them. ROI compares genuinely usable gains with total investment over an agreed period; hypothetical gains remain labelled as such.

For assistance with enquiry processing, the test record can fit into a document readable by the person making the purchase decision. The following example is entirely fictional: it illustrates a calculation and validation method, not Gloria’s performance or a model’s price.

The pilot prepares a suggestion; the responsible person decides whether to use it

The selected task is to extract request type, preferred date and missing information, then prepare an internal summary. The model chooses neither the price nor the response sent to the customer. The test set contains 50 anonymised cases representative of the situations to handle and distinct from tuning examples. The business team prepares expected fields and errors considered unacceptable before testing.

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A cheap model call can produce an expensive correction task

Suppose 40 suggestions are accepted after checking and 10 cases are entirely reworked manually. The test’s technical usage costs a fictional €20. Checking all cases and reworking them takes three hours, valued here at €30 per hour, or €90. The operational total is therefore €110, or €2.75 per AI suggestion actually used; counting only the 50 calls would have hidden correction costs.

This calculation deliberately excludes the project’s fixed costs. To decide, add planning, integration, fixed subscriptions, training and monitoring, then compare against manual processing of the same tasks. Freed time becomes a saving only if the business can actually use it or reduce expenditure. These illustrative figures are neither a commercial estimate nor a guaranteed target.

The final decision records remaining errors, authorised volume and stop conditions. If quality is insufficient, the scope can be limited, conventional automation retained or the pilot stopped. The quote also explains how to retrieve original documents, export results and return to manual processing. Changing provider may require adaptation and fresh testing; it is not presented as instantaneous or free.

The first phase may cover one team, document type or information source. It delivers a usable workflow, processing rules and test results. We present successful cases and those still requiring intervention. Users know whom to notify about an error and how to continue working if the service is unavailable.

Delivery specifies the accounts used, source responsibilities, update frequency and how to export or remove the relevant data. Usage instructions explain when to check an answer, when to escalate a case and what information must not be entered. Connection secrets are stored server-side, and logs avoid unnecessary sensitive content.

Maintenance is defined according to scope: monitoring, updating a connection, replacing a model or fresh testing. A CRM connection or dedicated interface can follow in a second phase once the first has demonstrated its usefulness.

To assess your need, provide a few anonymised task examples, the tools involved, users and expected volume. Specify an acceptable result and the consequences of an error. Internal writing assistance has a different scope from a public assistant connected to customer records.

The quote separates planning, data preparation, configuration or development, testing, integration and support. Recurring vendor and infrastructure expenses are identified separately. A projection shows costs at several usage levels with the assumptions used; it avoids presenting a low subscription fee as the complete service cost.

These services are quoted individually and are not automatically included in the standard website creation packages. You can start with a limited pilot and decide what follows from the results. Describe a task you would like to simplify: we can assess whether an available product, conventional automation or an AI solution is appropriate.

Do we need to develop our own model?

Not necessarily. Most initial projects considered involve integrating an available product or model with data and a workflow. Custom development or training must address a demonstrated need, with costs and constraints made explicit.

Can AI answer customers on its own?

A public assistant can be considered within a defined scope, with human handover and clear limits. Commitments, sensitive changes and uncertain cases require appropriate approval rules. Autonomy is not the default objective.

Does a document assistant prevent every error?

No. Document retrieval provides context, but the model may misinterpret a source or omit a qualification. Collection quality, permissions, the test set, displayed references and human verification remain necessary.

How do we know whether the project is worth its cost?

The pilot compares the current and assisted methods on similar tasks. We count checking and correction time, usage costs and errors. The decision to expand rests on those measurements, without guaranteed gains.

If your activity is regulated, we identify the rules and competent authority before choosing channels, messages and data to process. A service available to an ordinary business may be excluded for your profession: we do not offer paid search to French notaries.

Content, testimonials, forms and AI uses are scoped according to professional secrecy and applicable rules. Choices and approvals are documented with you before publication. See the official references and practical implications by country.