Add AI to the software your team already uses
You do not need a new platform to benefit from AI. Blocvibe adds AI features, such as summarizing, drafting and classifying, to your existing software through APIs, so they appear where your team already works.
- Python
- TypeScript
- LLM APIs
- REST APIs
- Document search
The service
What is AI integration?
AI integration is adding AI features, such as summaries, drafting, classification or search, to the software your team already uses, through APIs, instead of moving work into a separate AI tool.
Who it is for
- Teams copying data into chat tools and pasting the results back
- Product teams adding AI features to an existing application
- Managers who need routine reports drafted from verified figures
When to consider it
- People paste records into AI chat tools to summarise them
- The same report is written from scratch every month
- Long notes and tickets take time to read before anyone can act
AI Solutions and Integration
Which AI service fits?
Three related services for three different starting points.
- AI SolutionsAI applied to specific business tasks, with people reviewing the results.
- AI IntegrationThis pageAI features added to the software your team already uses.
- AI Vibe Coding and Software CustomizationEngineering that takes software built with AI coding tools from prototype to production.
The challenge
The business problem
Many teams experiment with AI in separate chat tools, copying data in and results out. This is slow, hard to control and disconnected from the systems where work happens.
- Separate AI tools sit outside the systems where work happens
- Copying data out raises questions of privacy and control
- Costs grow unpredictably without usage limits
- Results vary without review steps and logging
Our capabilities
How we help
Find the right spot
We identify where in your existing software an AI feature would save the most time.
Integrate through APIs
AI features are added to your application or connected tools without replacing them.
Control cost and quality
Usage limits, logging and review steps keep results reliable and costs predictable.
How it works
How an AI feature fits into existing software
Existing software
- Your app or a connected tool
- The record or figures in question
- A button or trigger for the AI step
Checks first
- Data validated before the AI runs
- Sensitive fields left out where needed
- Usage limits
AI step
- A call to an LLM API
- Instructions designed for the task
- Inputs and outputs logged
Review and save
- A person edits or approves
- Saved in the same system
- Corrections recorded
Example workflow
An AI-assisted monthly report
Step 1: At month end, figures are pulled automatically from your existing systems.
Step 2: The data is checked for gaps and obvious errors before any AI step runs.
Step 3: An AI model drafts a written summary of the main changes and trends.
Step 4: A manager reviews, edits and approves the draft inside the reporting tool.
Step 5: The approved report is shared with stakeholders in the usual format.
Limits to keep in mind
Typical use cases
- Summarizing long records, tickets or notes
- Drafting emails, reports or descriptions for review
- Classifying and tagging incoming requests
- Searching internal knowledge from inside your app
What you receive
- Integration design and data flow
- AI feature inside your existing software
- Prompt and configuration documentation
- Logging, usage limits and cost reporting
- Evaluation on representative examples
Technologies and integrations
- Python
- TypeScript
- LLM APIs
- REST APIs
- Document search
Industry applications
Where AI integration helps
Practical examples of how this service applies in different sectors.
Technology Companies and SaaS Startups
- The challenge
- Users of a SaaS product want summaries of long records, but the product has no AI features yet.
- The solution
- An AI summary feature added to the product through an LLM API, with limits for each plan.
- How it works
- A user opens a long record and asks for a summary, the product sends only the text needed to the model, and the summary is shown with options to edit or try again.
- The benefit
- A useful AI feature inside the product, with costs tied to plan limits.
- TypeScript
- LLM APIs
- REST APIs
Energy and Smart Buildings
- The challenge
- Facility managers write monthly energy reports by hand from meter data.
- The solution
- An AI-assisted report that drafts the narrative from verified consumption figures.
- How it works
- Figures are pulled from the metering system and checked, the model drafts a summary of changes and trends, and the facility manager reviews and approves it.
- The benefit
- Reports take less time to write, and the numbers still come from verified data, not from the model.
- Python
- LLM APIs
- REST APIs
Construction and Field Services
- The challenge
- Office staff read long technician notes to work out what follow-up is needed.
- The solution
- Summaries and suggested tags inside the job system.
- How it works
- When a job is closed, its notes are summarised and tagged with suggested follow-up actions, which the coordinator accepts or changes.
- The benefit
- Follow-up work is spotted sooner, without staff reading every note in full.
- Python
- LLM APIs
- REST APIs
Business value
What it changes for your business
AI where the work happens
Features sit inside the tools your team already uses, so nobody copies data in and out of a chat window.
Control over your data
Only the data a task needs is sent, through providers and settings agreed with you.
Predictable costs
Usage limits, suitably sized models and usage reports keep spending in view.
Results you can review
People approve or edit AI output before it is used, and corrections are logged.
Our development process
How the project runs
Step 1: Discover
We learn how your business works today, what is not working and what success should look like.
Step 2: Define
We agree on a written scope, priorities, estimate and timeline before any development begins.
Step 3: Design and build
The solution is designed and built in short cycles, with working software to review along the way.
Step 4: Validate
We test with your team and real or representative data, and fix what needs fixing before launch.
Step 5: Deploy
The solution goes live in your environment with documentation and a handover for your team.
Step 6: Support and improve
We monitor, maintain and improve the solution as your business and its users’ needs change.
Why Blocvibe
Why choose Blocvibe
The right technology should simplify work, solve meaningful problems and leave room to grow. We focus on practical solutions shaped around your goals.
Business-first thinking
Every project starts with the problem that needs solving, not the technology that happens to be trending. We map how work really moves through your team, exceptions included, before deciding what to build.
Purposeful engineering
Tools and architecture chosen for your requirements, maintainability and long-term value.
Connected digital experiences
Software, AI, mobile apps and connected devices brought together where they genuinely help.
Quality at every stage
Usability, responsiveness, accessibility and testing get attention throughout development, not just at the end.
Frequently asked questions
Can AI be added to software we did not build?
Often, yes, if the software has an API or supports extensions. Where it does not, AI features can run in a connected tool that exchanges data with it.
Which AI models do you use?
We choose models based on the task, data sensitivity and cost, and design the integration so the provider can be changed later if needed.
How do you keep AI costs under control?
Through usage limits, caching, choosing appropriately sized models and reporting on usage, so costs stay predictable.
What happens if an AI result is wrong?
AI steps are designed so that a person reviews or approves the result before it is used. Corrections are logged, which helps improve the instructions and measure quality over time.
How long does it take to add an AI feature?
A focused feature is usually a small project: confirming the task and data, building and testing it on representative examples, then releasing it with limits and logging. The estimate follows discovery.
Discuss your AI integration project
Share what you want to build or improve. We’ll reply by email and suggest a practical first step.