Generative AI

Production GenAI, not prototypes.

We build RAG systems, fine-tuned models, and agentic pipelines that run at scale. No slidedeck handoffs — just working systems your team can own and extend.

See our work

Bedrock certified

Azure OpenAI partner

SOC 2 ready

Capabilities

What we build

Every engagement starts with your data and ends with a system you can run, monitor, and extend without us.

RAG Architectures

End-to-end retrieval-augmented generation pipelines. Vector stores, hybrid search, re-ranking, and hallucination control built for production query volumes.

PineconepgvectorOpenSearch

Fine-tuning & Adaptation

Domain-specific model adaptation using LoRA, QLoRA, and full fine-tuning. We handle data curation, training infrastructure, and evaluation harnesses.

LoRAQLoRAPEFT

Evaluation Pipelines

Systematic LLM evaluation: faithfulness, relevance, toxicity, latency. CI/CD gates that catch regressions before they reach users.

RAGASDeepEvalPromptflow

Agentic Systems

Multi-step reasoning agents with tool use, memory, and human-in-the-loop checkpoints. Stateful workflows that don't collapse under real-world complexity.

LangGraphAutoGenBedrock Agents

Multi-modal Pipelines

Vision, audio, and document understanding integrated into LLM workflows. OCR-to-RAG, image captioning, and structured extraction at scale.

GPT-4oClaude 3Gemini Pro

Safety & Guardrails

Prompt injection defence, output filtering, PII redaction, and content policies. Compliance-ready guardrails for regulated industries.

Guardrails AILlama GuardCustom

How we engage

From discovery to production.

A repeatable process that keeps quality high and timelines honest.

Discovery sprint

We map your data landscape, user journeys, and quality bar. Two weeks, fixed fee. You leave with an architecture decision record and a build plan.

Data & indexing

Ingestion pipelines, chunking strategies, embedding models, and vector store setup. We tune retrieval quality before writing a single line of application code.

Application layer

API design, prompt engineering, streaming responses, and frontend integration. Production-ready — not a Jupyter notebook dressed up as an app.

Evaluate & harden

Automated evals, load testing, cost modelling, and security review. We don't hand over a system we wouldn't run ourselves.

Technologies

The stack we deploy on.

AWS BedrockLLM Platform

Azure OpenAILLM Platform

Anthropic ClaudeModel

OpenAI GPT-4oModel

Llama 3OSS Model

LangChainFramework

LangGraphAgents

PineconeVector DB

pgvectorVector DB

RAGASEvaluation

WeaviateVector DB

Weights & BiasesExperiment tracking

Outcomes

Outcomes we've shipped.

11 wk

Avg. time to production

from kickoff to first live query

62%

Faster response times

vs legacy search tooling

$1.8M

Avg. annual savings

versus full build or vendor

8.4M

Queries / month

largest single RAG deployment

What clients say

Built by people who ship.

CGX shipped our RAG platform in 11 weeks. Two of their engineers replaced an entire vendor pursuit. They write code, not slideware.

Priya Narayanan

VP Engineering · Helix Health

We'd tried two other vendors before CGX. The difference was they came in having already thought about evaluation — most teams treat that as an afterthought.

Daniel Orth

Head of AI · Meridian Capital

FAQ

Common questions.

Don't see yours? Ask us directly — we usually reply within a working day.

It depends on your latency budget, compliance requirements, and the nature of your queries. We run benchmarks on your actual data before committing to a model. The answer is almost never 'the most expensive one'.

Through a combination of retrieval quality improvements, faithfulness scoring, citation grounding, and output filtering. We build eval pipelines that measure hallucination rates before you go live — not after users complain.

Yes. We handle data cleaning, formatting, training on Bedrock or Azure ML, and rigorous evaluation comparing the fine-tuned model against few-shot prompting. Fine-tuning is often unnecessary — we'll tell you honestly if it is.

We implement automated evals using frameworks like RAGAS or DeepEval, covering faithfulness, answer relevancy, context recall, and toxicity. These run in CI so every change gets scored before deployment.

We route sensitive requests through Bedrock or Azure OpenAI in your chosen region with private endpoints. No data leaves your VPC without explicit configuration. We've done this for UK financial services, EU healthcare, and US government clients.

◐ Currently booking — Q3 2026

Ready to ship GenAI that actually works?

Book a 30-minute call. We'll review your use case, your data, and tell you exactly how we'd approach it — no commitment required.

hello@cybergenesisx.com