RAG · AI · Software engineering
Elarxsys designs, builds, and operates retrieval-augmented generation systems and the software around them, so your teams get accurate, cited answers from the knowledge you already have.
Intelligence, Innovation, Impact
Grounded answers
Answers are generated only from passages retrieved from your own content, with citations your team can check. If your content doesn't cover a question, the assistant tells you instead of guessing.
Standard vendor agreements require 60 days' written notice1. Agreements signed under the 2024 template allow 30 days' notice for cause2.
Services
We focus on retrieval-augmented generation, backed by the AI and software engineering needed to run it reliably.
Knowledge assistants and search that ground every answer in your own documents and data.
Practical AI built into the products and workflows your business already runs.
Dependable engineering that supports, integrates, and scales your AI systems.
Retrieval-augmented generation
Most RAG prototypes look impressive on a handful of questions and break on real users. We build and measure every stage of the pipeline so answers stay accurate as your content and usage grow.
Connect and parse documents, wikis, tickets, and databases, keeping structure, tables, and metadata intact.
Split content along meaningful boundaries and attach titles, dates, and permissions to every passage.
Combine vector embeddings with keyword search for recall on both concepts and exact terms.
Find the most relevant passages for each question and filter them by what the user is allowed to see.
Produce answers grounded only in the retrieved sources, with citations, and say so when the answer isn't there.
Track retrieval quality, faithfulness, latency, and cost against a test set, before and after launch.
Answers from policies, handbooks, and wikis, without searching ten tools.
Suggested replies grounded in product docs and past resolved tickets.
Find clauses, compare versions, and summarise long documents with references.
Natural-language search across manuals, specs, and engineering docs.
Consistent, traceable answers on regulations and internal controls.
Draft RFP responses and proposals from approved, up-to-date content.
Approach
A clear path from idea to a system your team relies on, with evidence at each step.
Understand your users, data sources, constraints, and what a good answer looks like.
Build a working proof of concept on your real data, not a generic demo.
Measure quality against agreed test questions before investing in scale.
Roll out to production with monitoring, access control, and continuous improvement.
Every system ships with an evaluation set, so quality is tracked with numbers rather than impressions.
We can deploy inside your own cloud account, with data access governed by your existing permissions.
Security, observability, latency, and cost are designed in from the start, not added at the end.
We choose models and infrastructure on merit for your use case and avoid unnecessary lock-in.
Technology
We build on the models, data stores, and cloud platforms your team already uses, and add only what the use case needs.
RAG pairs a language model with a search step over your own content. When someone asks a question, the system first retrieves the most relevant passages from your documents, then has the model answer using only those passages and cite them. The result is answers that reflect your organisation's knowledge and can be checked against the source.
Fine-tuning changes how a model behaves; RAG changes what it knows at the moment of answering. RAG is usually the better fit for knowledge that changes often, needs citations, or must respect user permissions, because updating the content doesn't require retraining. The two can be combined when needed.
Yes. We can deploy into your Google Cloud, AWS, or Azure environment, or on-premises, so documents and queries stay within infrastructure you control.
We're model-agnostic. We select hosted or open-weight models based on your accuracy, latency, cost, and data-residency requirements, and design systems so models can be swapped later.
Together with your team we build a set of representative questions with expected answers. We use it to score retrieval quality and answer faithfulness, compare design choices, and catch regressions as content or models change.
With a short conversation about your data, users, and goals. From there we typically propose a scoped proof of concept on your real data, so decisions are based on results rather than assumptions.
Contact
Tell us about your data, your users, and the questions you need answered. We'll follow up to discuss scope and next steps.
Email hello@elarxsys.com