AI applied to specific business tasks
AI is most useful when it handles a defined task, such as answering questions from your documentation or extracting data from invoices. Blocvibe builds AI solutions around specific tasks, with human review built in where accuracy matters.
- Python
- LLM APIs
- Document search
- REST APIs
The service
What is AI solutions?
AI solutions are software that applies artificial intelligence to a specific business task, such as answering questions from your documents, extracting data from forms or sorting incoming requests. Blocvibe builds them so that results show their sources and uncertain cases go to a person.
Who it is for
- Support teams answering repeat questions from manuals and policies
- Operations teams re-keying data from invoices, orders and forms
- Businesses whose data could support forecasts or spot unusual patterns
When to consider it
- Staff spend hours searching documents for the same answers
- Data from PDFs and emails is typed into systems by hand
- Incoming requests are read and routed manually
AI Solutions and Integration
Which AI service fits?
Three related services for three different starting points.
- AI SolutionsThis pageAI applied to specific business tasks, with people reviewing the results.
- AI IntegrationAI 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
Teams spend hours searching for answers in documents, re-keying data from PDFs and emails, and preparing the same reports each month. General-purpose AI chat tools can help individuals, but they are not connected to your data or your workflow, and their answers are hard to check.
- General chat tools are not connected to your data or workflow
- AI answers are hard to trust when they do not show their sources
- Sensitive data needs controls over where it is sent and stored
- Accuracy is often assumed rather than measured
Our capabilities
How we help
Start with one task
We pick a specific, measurable task where AI can save time, rather than a broad AI strategy.
Use your own data
Solutions answer from your documents and records, and show the sources they used.
Keep people in control
Uncertain results are flagged for review, and nothing important is sent without human approval.
How it works
How a document-grounded AI assistant works
Your knowledge
- Manuals, policies and help articles
- Records and forms
- Access rules for each source
Indexing
- Text split into passages
- Search by meaning
- Updated as documents change
AI model
- Finds the relevant passages
- Drafts an answer from them
- Cites its sources
Human review
- A person checks the draft
- Uncertain cases flagged
- Feedback improves the content
Example workflow
A support knowledge assistant
Step 1: Your help articles, manuals and policies are indexed so they can be searched by meaning.
Step 2: A support agent asks a question in plain language.
Step 3: The assistant finds the most relevant passages and drafts an answer from them.
Step 4: The draft shows the source documents, so the agent can check it quickly.
Step 5: The agent edits and sends the reply; unanswered questions are logged to improve the content.
Limits to keep in mind
Typical use cases
- Support knowledge assistants that answer from your documentation
- Extracting fields from invoices, orders and forms
- Classifying and routing incoming emails or tickets
- Forecasting and anomaly detection on business data
What you receive
- Task definition and success measures
- Working AI feature or assistant
- Review screen for flagged results
- Evaluation results on your sample data
- Usage and cost monitoring
Technologies and integrations
- Python
- LLM APIs
- Document search
- REST APIs
Industry applications
Where AI solutions helps
Practical examples of how this service applies in different sectors.
Technology Companies and SaaS Startups
- The challenge
- Support agents answer the same product questions repeatedly, searching the documentation each time.
- The solution
- A support knowledge assistant that drafts answers from the product documentation, with its sources.
- How it works
- An agent asks a question, the assistant retrieves the relevant passages and drafts a reply, and the agent checks the sources and edits it before sending.
- The benefit
- Agents find answers faster, and unanswered questions show where the documentation needs work.
- Python
- LLM APIs
- Document search
Logistics and Fleet Management
- The challenge
- Delivery notes, invoices and order forms arrive as PDFs and photos, and their details are typed into the operations system.
- The solution
- Document data extraction that reads the key fields and checks them before they are entered.
- How it works
- Incoming documents are read, fields such as references, dates and quantities are extracted and checked against existing records, and anything uncertain goes to a person to confirm.
- The benefit
- Less manual re-keying, with a person still confirming anything the system is unsure about.
- Python
- LLM APIs
- REST APIs
Finance and professional services
- The challenge
- Client emails and requests are read and routed to the right team by hand.
- The solution
- Classification and routing of incoming messages by topic and urgency.
- How it works
- New messages are classified, tagged with a suggested team and priority and placed in the right queue, where staff can correct the label.
- The benefit
- Requests reach the right person sooner, and the corrections help measure and improve accuracy.
- Python
- LLM APIs
- REST APIs
Business value
What it changes for your business
Time back on repetitive work
Searching, re-keying and sorting are handled by software, so people spend their time on judgement.
Answers you can check
Results show the documents or records they came from, so a reviewer can confirm them quickly.
People stay in control
Uncertain results are flagged, and nothing important goes out without a person’s approval.
Measured, not assumed
Accuracy is tested on your own sample data before launch, so expectations are based on evidence.
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
Will AI replace our staff?
That is not the goal. These solutions take on repetitive parts of a task, such as searching or data entry, while people make decisions and review results.
Is our data used to train public AI models?
Solutions are configured with AI providers’ business offerings and settings that do not use your data for training, where the provider supports this. Data handling is agreed before any data is processed.
How accurate will it be?
Accuracy depends on the task and the quality of your data. We measure it on your own sample data before launch and set up review steps for uncertain results.
What data do you need to start?
A representative sample of the documents or records the solution will work with, and examples of correct results. Accuracy is measured on that sample before launch.
Can an AI solution run in our own environment?
Depending on the task and requirements, solutions can use AI providers’ business services or models deployed in a cloud environment you control. The options and their trade-offs are reviewed during discovery.
Discuss your AI solutions project
Share what you want to build or improve. We’ll reply by email and suggest a practical first step.