Can an AI chatbot make things up?

Yes. Language models can produce wrong answers, and no provider can remove that risk entirely. CoreFinity Tech reduces it by grounding answers in the client's own documents, limiting what the chatbot will attempt and handing uncertain questions to a person.

Is company data used to train AI models?

Not in AI systems built by CoreFinity Tech. CoreFinity Tech uses provider settings that exclude client data from model training, never reuses client material for anyone else, and puts this commitment in its terms.

How much does an AI chatbot cost to run?

AI model usage is billed by the provider on the client's own account, so running costs scale with usage and stay under the client's control. CoreFinity Tech estimates the monthly cost during scoping and designs the system to keep it predictable.

What happens if an AI provider changes or retires a model?

AI providers change and retire models regularly. CoreFinity Tech builds AI systems so the model provider can be swapped wherever practical, instead of tying the business to one vendor.

AI & Chatbot Development: AI wired into the work you already do.

Assistants that answer from your own material, and automation that removes the queries nobody wants to answer twice.

Talk to an engineer

Plenty of AI projects fail for the same reason. A model is bolted onto the side of a business, given no access to anything real, and asked to be impressive. It answers confidently, it answers wrongly, and people stop trusting it within a fortnight.

The useful version is narrower and duller. A model connected to your actual documents, your actual database and your actual rules, doing a specific job with limits you can see. That is what we build.

( What this covers )

AI & Chatbot Development, in practice

Chatbots that answer from your documents

Retrieval over your handbooks, policies, catalogue or knowledge base, so replies come from your material and can be traced back to the source.

Customer support assistants

Handling the repetitive questions, passing anything unusual to a person, with the handover built in from the start rather than added after complaints.

Document processing

Pulling structured data out of invoices, forms, contracts and scans, and putting it where your systems expect it.

Language model integration

Adding drafting, summarising, classifying or extraction inside the software you already run, rather than in a separate tool nobody opens.

Internal knowledge assistants

Letting your own team ask questions across scattered internal material instead of interrupting the person who happens to know.

Guardrails and evaluation

Tests against real questions, limits on what the system will attempt, and a defined answer for when it does not know.

( Worth talking to us if )

Any of this sounds familiar.

Your support team answers the same questions every day
Information exists but nobody can find it quickly
Data arrives as documents and someone retypes it into a system
You have been sold an AI product that impresses in a demo and fails in practice
You want to use AI but need to know where it should not be trusted

( How we work )

The shape of a typical build

01
Pick a job worth doing
We start from a specific task with a measurable outcome, not from the technology. Some briefs are better solved by ordinary code, and we will say so.
02
Get the data in order
Quality of answers follows quality of source material. We work out what the system can draw on before choosing a model.
03
Build with limits
Retrieval so answers are grounded, guardrails on scope, and a clear route to a human when confidence is low.
04
Test against real questions
We evaluate on questions your business actually receives, not on examples chosen to make the demo look good.

( What you end up owning )

Everything, and no strings.

The repository, including prompts, retrieval logic and evaluation tests
API keys held on your own provider accounts, so usage bills go to you
Documentation of what the system will and will not attempt
An architecture written so a provider can be swapped later

( Built with )

Python
TypeScript
OpenAI
Anthropic
Vector databases
FastAPI
PostgreSQL

Mainstream tools with deep talent pools, chosen so you are never dependent on us to maintain what we built.

Questions about AI & Chatbot Development

Can an AI chatbot make things up?: Yes. Language models can produce wrong answers, and no provider can remove that risk entirely. CoreFinity Tech reduces it by grounding answers in the client's own documents, limiting what the chatbot will attempt and handing uncertain questions to a person.
Is company data used to train AI models?: Not in AI systems built by CoreFinity Tech. CoreFinity Tech uses provider settings that exclude client data from model training, never reuses client material for anyone else, and puts this commitment in its terms.
How much does an AI chatbot cost to run?: AI model usage is billed by the provider on the client's own account, so running costs scale with usage and stay under the client's control. CoreFinity Tech estimates the monthly cost during scoping and designs the system to keep it predictable.
What happens if an AI provider changes or retires a model?: AI providers change and retire models regularly. CoreFinity Tech builds AI systems so the model provider can be swapped wherever practical, instead of tying the business to one vendor.

( In practice )

AI development case studies

All case studies

Education / IELTS Counsel

PKR 1M+ in its first two months, on a platform built in two.

PKR 1M+ revenue in the first two months, reported by the founder

Tell us what you need building.

Send a brief and you get a written scope, a timeline and a fixed price back within 48 hours. It costs nothing and commits you to nothing.