AI INSIDE YOUR WORKSPACE

AI in your CRM and digital products.

We embed AI assistance where your team already works: customer records, support requests and orders. Help employees understand the history, prepare a reply and complete information without switching between tools.

Discuss an AI feature Explore an example

Your interface. Relevant context. Employee control.

CRM interface with a customer record and an AI assistance sidebar.

CONTEXTUAL ASSISTANCE

Less time reviewing the history. More attention to the customer.

One customer record, three ways to help an account manager. Switch examples to see how the assistance changes without changing the underlying workflow.

Fictional data and predefined responses. No real records are changed.

Request context

Partner portal inquiry

The customer needs a dealer catalogue with account-specific pricing.
The account manager confirmed that the company already uses an ERP.
Pricing groups, API access and the launch date are not yet confirmed.

Based on three source notes

What we know

The customer needs a dealer portal with account-specific pricing and integration with the existing ERP. Pricing rules, integration capabilities and the expected launch date are still needed to scope the project.

The suggestion does not invent a budget or deadline. The team can see what is missing before making the next decision.

When AI belongs in your product

A practical starting point is a recurring task with available information and a clearly identified user.

Employees keep rereading the history
An account manager opens long conversations to understand a customer’s situation. A summary brings together key facts, open questions and references to the original records.
Every reply starts from scratch
Employees repeatedly look up information and write similar explanations. A contextual draft provides a starting point they can check and adapt.
Useful information stays inside messages
Needs, agreements and contact details never reach the relevant fields. AI can suggest field values alongside the source passage.

Features built around the task

We select useful capabilities rather than adding an AI button to every screen.

Conversation and record summaries
A concise account of events, customer needs and unresolved questions from the available history.
Message drafts
Replies informed by the context and company materials. The employee can edit the text before sending.
Suggested field values
Turn relevant facts into suggested field values, with source references and format validation.
Suggested next steps
Suggestions based on the request’s state and agreed rules. A recommendation does not change records without permission.
Knowledge next to the task
Find a relevant instruction or answer from approved materials without leaving the record.
Feedback on quality
Employees can flag an unhelpful suggestion, giving the team examples to improve rather than only a click count.

Experience with AI inside a digital product

Coffee e-commerce with AI assistance — frame from the Draxon case study

AI features in an e-commerce platform

The coffee e-commerce case uses AI for guided product discovery and personalized interaction. It is evidence of embedded product AI, not a separate CRM implementation case.

View the case study

Assistance should belong in the interface

An AI feature is useful when people understand where a suggestion came from and what happens if they use it.

Visible context
Make it clear which record, conversation or task the result belongs to. Keep different customers’ information separate.
Room to edit
Drafts remain editable. Suggested fields can be reviewed before they are saved.
A working fallback
A model error or slow response should not prevent employees from using the existing workflow.

Customer record with an editable AI draft beside the working information.

From a workflow problem to a useful feature

Validate usefulness on one screen before extending the solution.

01. Understand the user’s task
Identify the repeated action, the information it needs and how to evaluate the result.
A task brief, required data and evaluation criteria.
A task brief, required data and evaluation criteria.
02. Design the assistance
Prototype the interaction: trigger, waiting, result, editing, errors and dismissal.
An interface prototype covering the main interaction states.
An interface prototype covering the main interaction states.
03. Connect the context
Provide only the information needed for the task, respecting the employee’s access permissions.
Connected context that respects access permissions.
Connected context that respects access permissions.
04. Evaluate with the team
Compare suggestions with expected responses, collect failure examples and check responsiveness.
Evaluation findings and agreed improvements.
Evaluation findings and agreed improvements.
05. Roll out gradually
Release to an agreed group, observe usage and refine the interaction.
A pilot feature and user feedback.
A pilot feature and user feedback.

ESTIMATE

Which feature should come first?

Show us a working screen and a time-consuming task. We will use that to identify the required context, prototype format and dependencies.

Discuss an AI feature

User and task
Who will use the suggestion, and what decision do they need to make?
Available information
Which records, conversations and materials may be used?
System capabilities
Can the interface be changed, extensions added or APIs used?
A useful outcome
What will the employee review, edit and save after receiving the suggestion?

QUESTIONS & ANSWERS

Questions about AI in CRM and products

In-interface assistance: summaries, drafts and suggestions. Not an autonomous agent or a CRM replacement.

Questions and answers

Not necessarily. We first check the APIs, interface extension options and access to relevant information. Depending on the system, the feature may be a built-in module, an extension or a connected interface.

Here, the main user is an employee working inside the product. AI helps summarize, write or fill in information while the person directs the workflow. An agent can carry out a sequence of agreed actions using tools.

Yes. Start with a request summary or reply draft, for example. This lets the team evaluate quality, speed and practical usefulness before adding more capabilities.

For this type of feature, we design a draft that the employee reviews. Automatic sending is a separate capability requiring agreed rules and checks, not hidden behavior behind a suggestion button.

The feature should receive only the context the current user is allowed to access for the task. Access rules are enforced by the application, not solely by instructions to the model.

We provide a clear error state, a retry option and a manual path. An auxiliary AI feature should not make the existing interface unusable.

Evaluate representative tasks: whether facts are correct, what employees had to change and whether the suggestion helped them finish. Metrics depend on the scenario; we do not promise the same time saving for every team.

It depends on data availability, product extension options, the number of workflows, interface requirements and evaluation. Model usage and maintenance are estimated separately from development.

START WITH YOUR INTERFACE

Show us a task your teamrepeats every day.

We will identify where AI assistance would help, which information it needs and how to embed it without adding friction.

Discuss an AI feature All AI services

AI in your CRM and digital products.

Your interface. Relevant context. Employee control.

When AI belongs in your product

A practical starting point is a recurring task with available information and a clearly identified user.

Employees keep rereading the history

An account manager opens long conversations to understand a customer’s situation. A summary brings together key facts, open questions and references to the original records.

Every reply starts from scratch

Employees repeatedly look up information and write similar explanations. A contextual draft provides a starting point they can check and adapt.

Useful information stays inside messages

Needs, agreements and contact details never reach the relevant fields. AI can suggest field values alongside the source passage.

Features built around the task

We select useful capabilities rather than adding an AI button to every screen.

Conversation and record summaries

A concise account of events, customer needs and unresolved questions from the available history.

Message drafts

Replies informed by the context and company materials. The employee can edit the text before sending.

Suggested field values

Turn relevant facts into suggested field values, with source references and format validation.

Suggested next steps

Suggestions based on the request’s state and agreed rules. A recommendation does not change records without permission.

Knowledge next to the task

Find a relevant instruction or answer from approved materials without leaving the record.

Feedback on quality

Employees can flag an unhelpful suggestion, giving the team examples to improve rather than only a click count.

Assistance should belong in the interface

An AI feature is useful when people understand where a suggestion came from and what happens if they use it.

Visible context

Make it clear which record, conversation or task the result belongs to. Keep different customers’ information separate.

Room to edit

Drafts remain editable. Suggested fields can be reviewed before they are saved.