We build custom LLM features and private RAG pipelines inside your cloud, connected to your business data and workflows. You keep control of the data and the models.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
Models such as Llama 3 and Mistral, fine-tuned for your work and hosted entirely in your private cloud.
Knowledge retrieval that checks answers against your sources to cut made-up responses.
Assistants that carry out multi-step tasks across your older ERP and SaaS systems.
Custom image, audio and video pipelines for producing content in high volume.
Testing and hardening models against prompt injection, data extraction and data poisoning.
AI features added to the business software you already run, including older apps that were never built with AI in mind.
Our approach
The usual way
Public AI APIs can let sensitive business data leave your controlled environment.
How we do it
Private LLMs run in your cloud, so the models, data and infrastructure stay under your control.
The usual way
Unchecked AI answers can be wrong, and a wrong answer can cause legal or brand damage.
How we do it
Answers come from your own documents through RAG and are checked against them in several steps before anyone sees them.
The usual way
Stand-alone chatbots that don't connect to your ERP or CRM.
How we do it
AI assistants that carry out workflows across the systems you already use.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
Encrypted pipelines bring in your ERP and CRM data and documents such as PDFs.
Your content is turned into searchable embeddings, with hybrid vector and keyword search.
LLMs hosted in your cloud and fine-tuned on your company's terms and rules.
Agents that complete tasks across your SaaS tools through authenticated API calls.
How we deliver
4 phases, each ending with an output you can review.
We find the AI opportunities worth pursuing and check whether your data sources are ready for RAG, secure and compliant.
Output: Feasibility report
We set up an isolated environment in your cloud and the infrastructure to host your models.
Output: Secure infrastructure
We fine-tune models on your own material and build the retrieval layer for accurate, consistent answers.
Output: Working prototype
Final security testing and production launch, followed by a full handover to your internal team.
Output: Full ownership of the system
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Leads model selection across Llama, Mistral and GPT, and designs how several agents work together on a task.
Tunes vector search and metadata filters, so the model gets the right passages and makes up fewer answers.
Manages GPU clusters and private cloud hosting, so your data stays where you choose.
Runs security and bias testing and checks the system against regulatory requirements.
Trust and control
RAG checks compare model answers against your trusted documents to cut made-up responses.
Defences against prompt injection and data poisoning help keep models and connected systems safe.
Models, training data and weights stay inside your cloud, so you control access and location.
Work is designed to support EU AI Act, NIST AI RMF and ISO 42001 requirements.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We built a secure AI knowledge assistant that answers employees' everyday questions from company policies, SOPs, project documents and shared notes. Staff no longer dig through folders or ask the same colleagues again and again.
We built an AI search assistant that lets business teams ask questions across contracts, policies, terms and internal guidance. It returns a clear answer with the relevant source sections, for a person to check.
We built a generative AI sales assistant that searches case studies, service information, product documentation, pricing guidance and approved proposal content. It helps the team turn the right material into relevant sales documents faster.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
Generative AI cost depends on the use case, how much data must be prepared, whether models are fine-tuned and the GPU hosting you need. Work can start with a fixed-scope readiness review, then move to a fixed-scope build or a managed squad on a flat monthly fee. You get a rough quote range after the first call.
You own the custom model weights, the fine-tuning adapters (PEFT / LoRA), the data connectors and the code built for your project. Ownership of all code and IP is written into the service agreement, and the project ends with a full handover to your internal team.
Your business data stays out of public model training, because processing can run entirely inside your private cloud. The models, training data and weights sit in an environment you control, and you decide who has access and where data is stored, which helps with GDPR and HIPAA requirements.
We ground answers in your own documents using RAG with hybrid vector and keyword search. Each answer is checked against your trusted sources and must cite them. Our context engineer tunes the search and metadata filters, so the model receives the right passages and has less room to invent an answer.
We work with open models such as Llama 3.1 and Mistral, served with vLLM or NVIDIA NIM and fine-tuned with PEFT / LoRA, and consider GPT where it fits. Retrieval runs on Pinecone, Weaviate, Elastic and LangChain, and agents use LangGraph, CrewAI or AutoGen with FastAPI.
We red-team each system before launch, testing it against jailbreaks, prompt injection, data extraction and data poisoning, then harden it against what we find. PII masking keeps personal data out of prompts and logs, and connected tools are reached only through authenticated calls over mTLS.
Running costs come down mainly through model quantisation, which makes models smaller and faster to run, and caching, which avoids repeating the same work. Our LLM ops lead also manages GPU partitioning and response-time tuning in your private cloud, so you pay for the compute you actually need.
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