Schooliee AI-Based School System
Pakistan's first AI-based school management system featuring automated parent WhatsApp notifications, rosters setup, attendance, and fee alerts.
AI & LLM integration
AI and LLM integration for real business workflows
AI and LLM integration means adding language-model features such as drafting, answering, classifying and extracting into software your customers or staff already use. I build these features into web applications and messaging channels such as WhatsApp, with the guardrails that make them dependable: clear scope, cost controls, fallbacks and human review where mistakes matter.
Practical uses for LLM features
- Customer assistants that answer questions from your own catalogue, policies or documentation
- WhatsApp or chat automation for orders, bookings and status updates
- Extracting structured data from emails, PDFs, forms or invoices
- Drafting messages, summaries or reports for staff to review and send
- Search across your documents and records using natural-language questions (retrieval-augmented generation)
- Classifying and routing incoming requests to the right person or workflow
What's included
Use-case assessment
Which tasks AI can genuinely improve, where rules-based automation is simpler, and how to measure whether the feature works.
LLM feature development
Prompting, structured outputs and tool calls built into your application through the OpenAI API or other model providers.
Knowledge retrieval
Indexing your documents or data so answers are grounded in your content, with sources shown where useful.
Messaging automation
WhatsApp Cloud API and other channels connected to your systems, with hand-off to a human when needed.
Guardrails
Input limits, output validation, rate limits, logging and review queues for sensitive actions.
Cost and quality monitoring
Tracking usage, latency and failure cases so running costs stay predictable and quality can be improved over time.
Useful, bounded and measurable
LLMs are strong at language tasks and unreliable at guaranteeing facts. I design features so the model does what it is good at, such as understanding requests, drafting and extracting, while your systems remain the source of truth for prices, stock, policies and records.
Every AI feature needs a definition of success and a test set of real examples. That makes it possible to compare models, adjust prompts and catch regressions, instead of judging quality by impression. Where an error would affect money, health, legal matters or a customer relationship, a person reviews the output before it takes effect.
How the project runs
- Identify the taskChoose a specific, frequent task and collect real examples of inputs and good outputs.
- PrototypeA working proof of concept tested against those examples to confirm quality and cost.
- IntegrateConnect the feature to your application, data and channels, with permissions and logging.
- Guard and reviewAdd validation, fallbacks and human review steps where needed; test failure cases deliberately.
- Monitor and improveTrack usage, cost and quality after launch and refine prompts, retrieval or model choice.
Technology choices
- OpenAI API and other LLM providersLanguage understanding, generation and structured extraction; model chosen per task, cost and data requirements.
- Node.js and Next.jsServer-side integration so API keys and business rules never reach the browser.
- WhatsApp Cloud APIAutomated conversations in the channel many customers already use.
- PostgreSQL and vector searchStoring content and embeddings for retrieval, alongside your existing data.
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What affects cost and timeline
- How well defined the task is and whether good example data exists
- The volume of requests and the model's per-request cost
- Integrations with your data sources and channels
- Review workflows and compliance requirements
- Whether retrieval over your documents is needed
Every estimate starts from your requirements. Request a quote with your users, must-have features, timeline and budget range, and I will reply with questions and a suggested first phase.
Frequently asked questions
Is our data used to train the AI model?
Major API providers offer business terms under which API data is not used for training by default, but terms differ by provider and plan. We review the provider's data policy against your requirements before sending real data.
Will the AI make things up?
Language models can produce confident but wrong answers. Grounding responses in your own data, restricting the scope, validating outputs and adding human review for important actions reduce that risk significantly; they do not remove it entirely.
How much does running an AI feature cost?
Running costs depend on the model, the length of inputs and outputs, and request volume. A prototype with real examples gives a reliable cost-per-task estimate before you commit.
Can you add AI to our existing software?
Yes. Most integrations add a server-side component to an existing application, connected through its API or database.
Do you build chatbots?
Yes, when a conversational interface suits the task, for example on WhatsApp or inside a customer portal. Often a button that drafts, summarises or extracts inside an existing screen is more useful than a general chatbot.
Have a project in mind?
Tell me what you want to build, who will use it and when you need it. I will reply with questions and a practical next step.
Discuss your project