AI chatbot development: assistants that do real work.
Large language models are good at reading, writing and sorting text, which is a large part of most office work. The hard part is not calling a model. It is connecting it to your own data safely, keeping its answers grounded in facts, and fitting it into the screens your team and customers already use.
How we approach it
Lacspace builds AI into its own products, including an assistant inside our business webmail, and builds the same kind of features for clients. We start from a specific job, such as answering customer questions from your policies or drafting replies, and build the smallest assistant that does it reliably, with clear limits and a human in the loop where it matters.
Customer support chatbots
Assistants on your website or app that answer from your own help content, product data and policies, and hand over to a person when they should.
Answers from your documents
Search and question answering over manuals, contracts and knowledge bases, with links back to the source so answers can be checked.
AI inside your software
Drafting, summarising, classifying and extracting fields added to the tools your team already uses.
Automations
Routine work such as sorting enquiries, tagging tickets or reading forms handled by AI, with rules for when a person must approve.
Provider choice
Built so the model provider can be switched, keeping cost and lock-in under your control.
Safety and checks
Moderation, privacy filters, prompt-injection defences and test sets that catch wrong answers before users do.
How the project runs
- 01
First call
The job you want AI to do, the data it would need, and the risks of getting it wrong.
- 02
Scope and quote
A specific assistant or feature, success checks, data sources, timeline and price, in writing.
- 03
Prototype on your data
A working prototype tested on real questions, so you can judge quality before committing to the full build.
- 04
Build and integrate
We connect it to your systems, add guardrails and a hand-over path to people, and test it against an evaluation set.
- 05
Launch and tune
We watch real conversations, fix weak answers, keep content current and track cost under a support plan.
The technology, in plain words
Most of our assistants use retrieval: your documents are split, indexed and searched at question time, and the model answers only from what it finds, with links to the source. This keeps answers grounded and makes it easy to update knowledge without retraining anything.
We keep the AI layer provider-agnostic, so the same feature can run on different model providers as prices and quality change. The building blocks, from chunking and embeddings to evaluation and moderation, come from open-source packages we publish and use in our own products.
Who it is for
- Businesses answering the same customer questions every day
- Teams buried in documents, emails or forms that need sorting
- Software companies adding AI features to an existing product
- Organisations that want AI but need control over data and cost
- Support teams that want faster first replies without losing the human touch
Where you can see this work
We build this into products we run ourselves.
Questions buyers ask
Can a chatbot answer from our own documents?
Yes. We index your help content, policies or manuals and the assistant answers from them, showing where each answer came from. When the documents change, the assistant's knowledge is updated without retraining.
Will the chatbot make things up?
We reduce that risk by having the assistant answer only from your indexed content, telling it to say when it does not know, and testing it against a set of real questions before launch. For sensitive tasks, a person reviews before anything is sent.
Which AI model do you use?
It depends on the job, budget and data rules. We build the AI layer so the provider can be changed later, which keeps you free to move as models and prices change.
Is our data safe?
We send a model only what it needs for the task, strip personal data where possible, and use provider settings that keep your data out of model training. Access to conversations and documents follows your existing permissions.
Can you add AI to software we already have?
Yes. Many projects add AI features such as drafting, summarising or classifying to an existing system through its API, without rebuilding the whole product.
Related reading
- How AI-Powered Chatbots Are Revolutionizing Customer Service in 2025Artificial Intelligence
- Build RAG and AI Agents in TypeScript Without Vendor Lock-InDevelopers
- How to Prepare Your Business for the AI EraArtificial Intelligence
- How an AI-Assisted Newsroom Stays Honest: WeNepal’s Seven Checks, Two Bylines and Public CorrectionsArtificial Intelligence
Related services
- Custom software developmentCustom web platforms, internal tools, APIs and integrations, scoped in writing and built in short releases by a team that runs its own software products.
- ERP and CRM software developmentCustom ERP and CRM software for orders, stock, branches, staff and cash: one system built around your workflow, with web and mobile access and clear reports.
- Website developmentBusiness websites designed, built and hosted with search, speed and accessibility built in, plus enquiry forms that reach your team, and easy to update.
- Every service we offer
Tell us what you need built.
Start with a call. You get a written scope and price before anything is agreed.