Service
AI implementation and integration
Useful AI features that run on your own data, inside your own systems, with the guardrails a business needs. We build search, assistants and automation that people actually trust and use.
Overview
Large language models can read, summarize and answer questions in ways that were impractical a few years ago. The hard part is not calling an API. It is making the answers correct, grounded in your documents, safe to expose to customers or staff, and fast and affordable enough to run every day.
NaVasSys builds those systems end to end. We design the data pipeline that prepares your content, the retrieval layer that finds the right passages, the prompts and orchestration that turn them into answers with sources, and the evaluation and monitoring that tell you whether it keeps working after launch.
Our background is enterprise engineering, so AI features are built like any other production system: tested, observable, secured and integrated with the applications and identity systems you already run. Our toolbox includes LangChain for orchestration, OpenAI models for generation and embeddings, and Elasticsearch for hybrid keyword and vector search.
Where AI delivers value for our clients
Staff cannot find answers in internal documents
Retrieval-augmented generation (RAG) searches policies, manuals, tickets and wikis, then answers in plain language and cites the passages it used, so people can verify.
Customer support answers the same questions repeatedly
Assistants grounded in your product documentation handle common questions and hand over to a person, with context, when they should.
Search on our site or product is poor
Hybrid search combines exact keyword matching with semantic understanding, usually the biggest quality jump available for the least risk.
Manual document and data work
Extraction, classification, summarization and routing for contracts, forms, emails and reports, with human review where mistakes are costly.
We are worried about data privacy and wrong answers
We design access control, redaction, logging and evaluation up front, and tell you honestly where a model is not the right tool.
What you get
What we deliver
Use-case assessment
A short, honest review of where AI will pay off for you, where it will not, and what data and access it needs.
Data and retrieval pipeline
Ingestion, cleaning, chunking, embeddings and indexing in Elasticsearch, kept current as your content changes.
LLM application
The assistant, search experience or workflow itself, built with LangChain and OpenAI APIs and connected to your applications and permissions.
Evaluation harness
A test set of real questions and expected behaviour, run automatically, so changes to prompts or models are measured instead of guessed.
Safety and cost controls
Access control per user, prompt-injection defenses, output checks, rate limits and spend monitoring.
Handover
Architecture documentation and a clear picture of running costs, so your team can operate and extend the system.
How we work
Our approach
Frame the problem
We agree what a good answer is, who uses it and how success will be measured before building anything.
Prove it small
A narrow prototype on real data shows quality and cost early, so you decide with evidence.
Harden
Security, evaluation, monitoring and integration move the prototype to production quality.
Improve
We review real usage, fix weak answers and tune cost after launch.
Frequently asked questions
What is RAG, and why use it?
Retrieval-augmented generation finds relevant passages in your own content and gives them to the language model along with the question. Answers are grounded in your material and can cite their sources, which reduces made-up answers and avoids retraining a model on your data.
Will our data be used to train public models?
We configure services and contracts so that your data is not used to train public models, and we explain the data flow in writing. The exact terms depend on the provider and plan you choose, and we check them with you.
Can the AI make mistakes?
Yes. Any language model can. We reduce the risk through grounding, source citations, evaluation sets, guardrails and human review for high-stakes decisions, and we are upfront about residual risk.
Do we need a lot of data to start?
Not necessarily. A focused set of good documents often produces a useful first version. We will tell you if your data is not ready and what to fix.
Have a technical problem worth solving?
Tell us what is going on. We will reply with an honest view on whether and how we can help.
Book a free consultation