AI Systems That Ship, Not Slide Decks
We build AI tools that take over the slow, repeated work eating up your team's day, so your people spend that time solving problems instead of doing data entry. Our Text-to-SQL engine is benchmarked against a published academic baseline and beats it — that's the standard we hold every system we ship to.
What We Do
We start with your problem, not with the technology.
Multi-agent platforms built with LangGraph that route natural-language requests to the right specialist agent — RAG, SQL, or task automation — turning manual lookups that took minutes into answers in seconds.
Production retrieval pipelines using hybrid search (BM25 + pgvector embeddings) with reranking — evaluated with RAGAS so retrieval quality is measured, not assumed.
Hybrid collaborative + content-based recommendation engines served through low-latency FastAPI and Redis architectures, validated with structured A/B tests before rollout.
WebSocket, token-streaming assistants with confidence-based escalation to a human — backed by Kafka pipelines processing thousands of events per minute.
Deployments across AWS and GCP with Docker, Kubernetes, and Terraform — backed by CI/CD pipelines with automated evaluation gates, so a regressed model never reaches production.
Analysis and dashboards (SQL, Power BI, Tableau) that turn operational data into the specific intervention that moves the metric you actually care about.
Our Work
A few examples of what we've built — with the real results, not just marketing talk.
Our state-of-the-art Text-to-SQL model: a six-layer architecture — input processing, prompt engineering, LLM generation, execution, validation/repair, and output generation — driven by an iterative, execution-driven automated repair mechanism. It improved on the no-repair baseline by 20.8 percentage points across 1,034 questions and 20 databases, beating the referenced STRUG result.
A FastAPI service wrapping Google's Gemini API with input screening for prompt injection and jailbreak patterns, plus output validation to catch unsafe, empty, or leaked-secret content — with structured allow/block decisions and category codes.
Collaborative + content-based filtering serving 50,000+ monthly active users at p95 latency below 120ms. Validated with a structured four-week, 10% holdout A/B test, then deployed on Kubernetes with Terraform-managed infrastructure and CI/CD evaluation gates.
Analysed 5,000+ diabetes clinic appointment records to identify the key drivers of missed appointments — age, rurality, deprivation, and appointment type — and built Power BI dashboards that shaped a targeted SMS-reminder intervention.
Method
Understand the workflow, the data, and the actual business metric we're trying to move.
Architect the simplest system that solves the problem — not the trendiest one.
Ship working software in short, visible iterations you can react to early.
Production infrastructure with CI/CD, monitoring, and evaluation gates from day one.
Iterate against real usage data after launch — not assumptions from before it.
Get started
Our clients use AI to automate the everyday work in their business — answering questions, finding information, handling repeat tasks — so their team can spend time on bigger things. Book fifteen minutes, or just submit your problem below: we read every message ourselves, and we'll get back to you with a solution within one business day.
Fifteen minutes, no obligation. We'll tell you honestly whether AI is the right fix for what you're dealing with.
Book a callTell us what's slow, manual, or eating up your team's time — we'll get back to you with how AI can fix it.
Thanks — we've got it and will get back to you within one business day.
About
VirtueAI is a UK-based AI and machine learning engineering studio. We design, build, and deploy production AI systems — LLM agents, retrieval pipelines, recommendation engines — for businesses that need a specific operational problem solved, not a proof of concept that stalls after the demo.
Our flagship system, a Text-to-SQL engine with an automated, execution-driven repair mechanism, scores 83.8% exact-match accuracy on the Spider 1.0 benchmark, ahead of the published STRUG baseline.
We're a small, senior team — every engagement is led hands-on by the people who build the system, not handed off to a junior team after the pitch. VirtueAI is led by Bhanu Prakash Adireddy, MSc Artificial Intelligence (Cardiff University).
Direct contact
Email, call, or find the work itself — whichever's easiest.
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