AI development and integration
AI capabilities for understanding information in products and systems
This service adds capabilities that understand information: search across documents and organizational knowledge, classification and extraction, content summaries, and intelligent assistants. AI is useful when wording varies or when meaning has to be found in existing material. When a path, a condition, and a fixed action can be defined in advance, ordinary business rules are usually enough.Want to check whether AI fits the information you need to understand?
Tell us what information needs to be searched, classified, extracted, or summarized, and who should approve the result. We will come back with fit-check questions.
Understanding information inside the product, without a promise of perfect accuracy
The work starts from the information that needs to be understood: finding a document or procedure, extracting fields, classifying an item, summarizing content, or answering from defined sources. We do not train foundation models from scratch and do not commit to a specific provider. Existing AI services are integrated as needed, with permissions, quality checks, and human approval when a result affects a decision or a stored record. Full accuracy is not guaranteed.New or existing products
Add an understanding capability to an existing product, or build a first version around one defined information task.Sources and permissions
The connection is to documents, procedures, and knowledge sources, and only to information the user is allowed to see. Without suitable access, there is nothing reliable to answer from.Quality controls
Representative examples are checked, and a person approves a result before it is saved or presented as a decision when the case needs that.
Possible uses to develop
These examples are possible uses to develop. They are not presented as delivered projects, and they are not a promise of accuracy or a business result.Search across documents and organizational knowledge
A possible build: search that finds a procedure, document, or relevant passage even when the user does not know the exact name, with a source reference when that helps.Classification and extraction
A possible build: identify an item’s type or extract fields from text or a file, then show the result for review before it is saved.Content summaries
A possible build: summarize a document, conversation, or long source into points that can be read. The summary is not treated as a final fact without review.Intelligent assistants
A possible build: an assistant inside the product that answers from defined sources, drafts wording, or suggests a phrasing. The answer is assistance, not an action that changes data by itself.
What the client can receive
The exact work depends on the project. Not every product needs every part, but these are the pieces that should be considered before AI becomes part of a real product:- Defining the problem and the action the user needs to complete
- Choosing models and services by quality, cost, and response time
- Connecting data sources and APIs
- Building the interface and user flows
- Access permissions for information and actions
- Quality checks with representative examples
- Handling failures, usage limits, and cost monitoring
- Implementation and continued improvement based on real use
An answer is not a guarantee of accuracy, and it is not a system action
An AI capability can be wrong, especially when a source is missing, outdated, or outside the user’s permission. Permissions, quality checks on examples, and human approval are planned according to the case. A reply, summary, or draft is not the same as updating a system, sending a message, or running a process. Running a process across systems is planned separately, and only when a suitable interface and permission exist.
How the work proceeds
When there is meaningful uncertainty around answer quality, response time, or usage cost, it is often better to start with a proof of concept before committing to a full product build.Focused business use
Define who the user is, which action they need to complete, and what a useful result looks like.Data and permissions
Review which documents, data, and APIs are available, and what each user is allowed to see or do.Proof of concept
When uncertainty is high, build a small test to see whether the direction works before expanding it.Quality, cost, and latency
Test representative examples, usage limits, response times, and expected third-party service costs.Product integration
Integrate into the product, watch real usage and failures, and expand based on what works.
What affects development scope
Cost and timeline depend on the product, the data, and the systems being connected. No invented prices or external usage costs are presented as included by default:- Process and interface complexity
- Number and condition of data sources
- Required integrations
- Level of controls and permissions
- Usage volume and quality-check approach
Development cost vs ongoing usage cost
Development cost covers building the interface, connections, logic, and controls. Separately, there may be ongoing costs for models, storage, search, infrastructure, and third-party services. Those costs depend on the provider, configuration, and usage volume, and are not included in a development or maintenance package unless agreed otherwise.
Related services
Understanding information can be combined with a process that runs across systems. For example, classifying or summarizing an inquiry, and then an automation that opens a task or updates the CRM, if the system has a suitable interface and permission.- To add AI capability inside an iPhone and Android appMobile app development
- To connect AI to a business system, portal, or admin interfaceWeb system development
- When the next step is to run a process: capture a lead, update a system, send a notification, or request approval.Business automations and bots
AI development and integration questions
Can AI be added to an existing system?
Yes, if the system can be connected to the needed data, screens, or APIs. Before starting, we review the system structure, permissions, data quality, and the user action the AI should support.
Can the assistant answer from the business documents?
Yes, when the documents are available in a suitable format and access permissions can be managed. Answers can be designed around source material, and in some cases show references so the user can check the source.
Do we need to train a new model?
In most cases, the work does not start by training a foundation model from scratch. It usually starts with existing models and AI services, business context, data connections, and a product flow around them.
How is answer quality checked?
We prepare representative examples of questions, documents, or actions and test whether the answers help complete the task. The process can include marking weak answers and improving prompts or source material.
What happens when there is not enough information to answer?
That behavior should be designed intentionally: the product can say information is missing, ask for another detail, point to a manual source, or send the case to human review. It should not be forced to invent an answer.
When are ordinary business rules enough?
When a form, a condition, and a fixed action can be defined in advance, rules are usually a better fit than AI. AI is useful when the input is free text, or when the task is to find, classify, extract, or summarize meaning. Running the process between systems is a separate service.
Can AI perform actions inside the system?
The work here prepares an answer, a classification, a summary, or a draft. It does not promise an accurate result, and it does not change a business record by itself. If a later step should update a system, send a notification, or start an approval, that process is planned as automation, and only when a suitable interface and permission exist. A person can still be required to approve it.
How are permissions and business data handled?
We define who may see which information and what each role may do. Data handling depends on the providers, settings, and architecture chosen, so those requirements are reviewed before the solution is selected.
What affects usage costs?
Usage volume, model type, context size, document volume, search infrastructure, storage, and third-party services can all affect ongoing cost. These are separate from development cost unless agreed otherwise.