AI engineering · LLM · ML · agents · RAG · multimodal
We build AI systems. Not AI demos.
From LLM applications and RAG systems to AI agents, model fine-tuning, multimodal AI and private model deployment — we engineer AI systems around real business requirements.
- Business problem
- Data
- Retrieval
- Model
- Tools
- Business action
AI is moving beyond chat.
Systems can now understand documents, search internal knowledge, reason over business data, use tools, execute workflows, process images and audio, and interact through voice. We build those systems.
Chat
AI answers questions. Useful, and where most companies stop.
Build
AI becomes part of the software, the product and the workflow itself.
Operate
AI retrieves information, calls tools, executes actions, monitors processes and takes part in operations.
Our goal isn't to make your company use AI. It's to make AI useful inside your company.
What we actually build.
LLM applications
Custom applications powered by foundation models, not a chat box bolted on the side.
RAG systems
Responses grounded in your own knowledge, documents and databases, with citations.
AI agents
Reasoning, tool calling, retrieval and workflow execution under real constraints.
Multimodal AI
Text, image, audio, video and documents handled in one system.
Model customisation
Fine-tuning, adapters, quantization and domain specialisation where it earns its cost.
Private AI
Open-weight models deployed inside infrastructure you control.
Machine learning
Prediction, classification, recommendation, forecasting and optimisation.
AI automation
AI wired into the software and workflows your business already runs on.
The stack underneath a working AI system.
A demo is one call to one model. Production is eight layers that have to hold together. Select a layer to see what sits in it.
Data
Documents, databases, APIs, tickets, contracts, catalogues — whatever you already hold.
PDF parsing, OCR, connectors, metadata extraction
AI is bigger than Silicon Valley.
Serious AI engineering means knowing the whole model landscape. Chinese labs in particular are shipping increasingly capable models across reasoning, coding, multimodal work, long-context workloads and open-weight deployment. We evaluate on workload, not on brand.
Frontier
Hosted commercial models, accessed through a provider API.
OpenAI · GPT family
General-purpose reasoning, coding and multimodal work through a hosted API.
Anthropic · Claude family
Long-context reasoning and code work, with strong instruction following.
Google Gemini · Google DeepMind
Multimodal input across text, image, audio and video with large context.
xAI · Grok family
Hosted reasoning and coding models.
Mistral · Mistral AI
Hosted and open-weight European models, several small enough to self-host.
Cohere · Cohere
Retrieval and enterprise search oriented models, including rerankers.
Open weights
Models you can download, quantize and run on your own infrastructure.
Meta Llama · Meta
Widely deployed open weights with a large tooling and fine-tuning ecosystem.
Gemma · Google
Small open models suited to edge and latency-sensitive deployment.
Mixtral · Mistral AI
Mixture-of-experts open weights balancing quality against inference cost.
Phi · Microsoft
Small models for constrained hardware and on-device inference.
Chinese ecosystem
Labs producing increasingly capable open and hosted models.
Qwen · Alibaba
Broad family covering language, coding, reasoning and vision-language work.
DeepSeek · DeepSeek
Reasoning and coding oriented open weights.
GLM · Zhipu AI
ChatGLM lineage, with open and hosted options.
Kimi · Moonshot AI
Long-context and language workloads.
MiniMax · MiniMax
Multimodal, language and voice oriented ecosystem.
Baichuan · Baichuan AI
Chinese foundation-model family.
Yi · 01.AI
Bilingual open-weight family.
We are model-agnostic on purpose. Problem, then data, then model, then infrastructure, then cost, security and scale — in that order.
There is no universal best model.
Pick what your workload actually demands and see which families are worth evaluating. This narrows the shortlist — it does not rank anything.
17 model families
Frontier
OpenAI
GPT family
General-purpose reasoning, coding and multimodal work through a hosted API.
Frontier
Anthropic
Claude family
Long-context reasoning and code work, with strong instruction following.
Frontier
Google Gemini
Google DeepMind
Multimodal input across text, image, audio and video with large context.
Frontier
xAI
Grok family
Hosted reasoning and coding models.
Frontier
Mistral
Mistral AI
Hosted and open-weight European models, several small enough to self-host.
Frontier
Cohere
Cohere
Retrieval and enterprise search oriented models, including rerankers.
Open weights
Meta Llama
Meta
Widely deployed open weights with a large tooling and fine-tuning ecosystem.
Open weights
Gemma
Google
Small open models suited to edge and latency-sensitive deployment.
Open weights
Mixtral
Mistral AI
Mixture-of-experts open weights balancing quality against inference cost.
Open weights
Phi
Microsoft
Small models for constrained hardware and on-device inference.
Chinese ecosystem
Qwen
Alibaba
Broad family covering language, coding, reasoning and vision-language work.
Chinese ecosystem
DeepSeek
DeepSeek
Reasoning and coding oriented open weights.
Chinese ecosystem
GLM
Zhipu AI
ChatGLM lineage, with open and hosted options.
Chinese ecosystem
Kimi
Moonshot AI
Long-context and language workloads.
Chinese ecosystem
MiniMax
MiniMax
Multimodal, language and voice oriented ecosystem.
Chinese ecosystem
Baichuan
Baichuan AI
Chinese foundation-model family.
Chinese ecosystem
Yi
01.AI
Bilingual open-weight family.
Selection depends on task, quality, latency, cost, context length, privacy, hardware, language and deployment model. We are model-agnostic on purpose.
Your data. Your knowledge. Your AI.
A vector store gives a model memory: it retrieves the semantically relevant passages out of a very large collection, so answers are grounded in your manuals, contracts, policies and support history rather than invented.
- Data sources
- Ingestion
- Parser / OCR
- Chunking
- Embedding
- Vector + keyword index
- Hybrid retrieval
- Reranker
- LLM
- Grounded response
Retrieval engineering
- Document ingestion
- PDF parsing
- OCR
- Chunking
- Metadata extraction
- Embeddings
- Hybrid search
- Semantic search
- Keyword search
- Reranking
- Contextual retrieval
- Query rewriting
- Multi-query retrieval
- Citation generation
- Access control
- Document-level permissions
- Retrieval evaluation
- Hallucination reduction
Vector and search stores
- pgvector
- Qdrant
- Milvus
- Weaviate
- Pinecone
- Chroma
- FAISS
- Elasticsearch
- OpenSearch
From answers to actions.
A chatbot
- User
- Model
- Answer
An agentic system
- User
- Think
- Retrieve
- Call tool
- Check result
- Take action
- Respond
Not every project needs an agent. We use one when an agent genuinely solves the problem better than a well-built pipeline would.
Agent capabilities
- Tool calling
- Function calling
- Workflow execution
- Planning
- Memory
- Retrieval
- API execution
- Database interaction
- Human approval
- Multi-agent workflows
- Task decomposition
- State management
- Observability
Framework ecosystem
- LangGraph
- LangChain
- LlamaIndex
- AutoGen
- CrewAI
- Semantic Kernel
- OpenAI Agents SDK
- MCP
Multi-agent workflows
An orchestrator splits the work, specialised agents do their part, and the results are reconciled into one answer — for research, document processing, sales operations, support and internal knowledge work.
- Orchestrator
- Research agent
- Analysis agent
- Action agent
- Final result
When prompting isn't enough.
Fine-tuning is not the default answer. We work the cheap options first and only train when the earlier steps genuinely run out of road.
- Prompting
- RAG
- Tool use
- Fine-tuning
- Base model
- Domain data
- Training
- LoRA / QLoRA
- Evaluation
- Specialised model
Customisation
- Supervised fine-tuning
- Instruction tuning
- LoRA
- QLoRA
- PEFT
- Adapters
- Domain adaptation
- Dataset preparation
- Synthetic data generation
- Quantization
- Knowledge distillation
- Evaluation datasets
- Benchmark design
Run it your way
Open weights downloaded, quantized, optimised and deployed on infrastructure you control — for private AI, internal copilots, confidential documents, offline and edge.
- Hugging Face
- Transformers
- Ollama
- vLLM
- llama.cpp
- Text Generation Inference
- ONNX Runtime
- TensorRT-LLM
- PyTorch
- CUDA
AI that sees, hears, reads and speaks.
- Text
- Image
- Audio
- Video
- Documents
- Computer vision
- OCR
- Document intelligence
- Image understanding
- Image generation
- Speech-to-text
- Text-to-speech
- Video understanding
- Visual question answering
- Document extraction
AI that can talk to your customers
- Caller
- Speech to text
- Model / agent
- CRM & tools
- Text to speech
- Customer
- Speech recognition
- Speech synthesis
- Multilingual voice
- Conversational IVR
- Voice agents
- Call routing
- Call summarisation
- Transcription
- Sentiment analysis
- CRM integration
- Real-time interaction
Beyond generative AI.
Plenty of business problems are still best solved by a well-specified model on your own numbers, not by a language model. Forecasting, fraud, churn and segmentation usually fall in that bucket.
- Predictive analytics
- Classification
- Regression
- Recommendation systems
- Anomaly detection
- Fraud detection
- Forecasting
- Clustering
- Ranking
- Computer vision
- NLP
- Time-series modelling
- Optimisation
- Customer segmentation
- Python
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- LightGBM
- Hugging Face
- ONNX
- MLflow
A demo can look smart. Production needs proof.
- Model
- Inference
- GPU
- Container
- Orchestration
- Monitoring
- Production
Infrastructure
Models need somewhere to run, and inference cost is a design decision, not an invoice.
- CUDA
- TensorRT
- vLLM
- Ollama
- Docker
- Kubernetes
- AWS
- Azure
- Google Cloud
- Private GPU servers
Evaluation
A demo can look smart. Production AI needs evidence that it is right often enough.
- Accuracy evaluation
- Retrieval evaluation
- Hallucination testing
- Latency testing
- Token and cost analysis
- Model comparison
- Benchmark datasets
- Regression testing
- Safety testing
- Human evaluation
- Observability
Security
Private data needs private thinking — especially once a model can call tools.
- Data isolation
- Access control
- Prompt injection defence
- PII protection
- Tenant isolation
- Audit logging
- Output validation
- Tool permission boundaries
- Secure model deployment
Higher-risk systems get security testing folded into the lifecycle. Cybersecurity services
AI should work with your business.
Connected to the systems you already run, not standing beside them as one more isolated tool.
- CRM
- ERP
- E-commerce
- Accounting
- HR systems
- Databases
- REST APIs
- GraphQL
- Webhooks
- SaaS platforms
- Internal software
Customer operations
AI support, voice agents, knowledge assistants, ticket automation.
Sales
Lead qualification, sales assistants, CRM intelligence, proposal generation.
Operations
Workflow automation, document processing, data extraction, decision support.
Knowledge
Enterprise search, RAG, internal copilots, document intelligence.
Product
AI-powered SaaS, AI features, in-product assistants, recommendation engines.
Management
Business intelligence, forecasting, decision support, automated reporting.
Building the surrounding systems too? Digital foundation
How AI adoption actually works.
Nobody should say “we'll add AI”. This is the sequence that turns a business problem into something running in production.
- 01
Discover
What the business actually needs, before any model is mentioned.
- 02
Identify opportunities
Where AI creates value — and where it plainly does not.
- 03
Audit data
What data and infrastructure exist, and what state they are in.
- 04
Select architecture
Model, retrieval strategy, deployment shape, cost envelope.
- 05
Prototype
The smallest thing that proves or kills the idea.
- 06
Evaluate
Accuracy, latency, cost and failure modes measured, not assumed.
- 07
Build
The production system, with guardrails and observability from the start.
- 08
Deploy
Into your infrastructure, with the security posture the data requires.
- 09
Monitor
Quality, drift, cost and behaviour watched once real users arrive.
- 10
Improve
Retrieval tuning, routing, prompts, models — an operating loop.
The AI engineering stack.
Models, serving, frameworks, vector stores, classical ML, fine-tuning and the infrastructure underneath.
Showing 40 of 60 technologies
OpenAI
Foundation models
Anthropic
Foundation models
Google Gemini
Foundation models
xAI
Foundation models
Mistral
Foundation models
Meta Llama
Foundation models
Qwen
Foundation models
DeepSeek
Foundation models
GLM
Foundation models
Kimi
Foundation models
MiniMax
Foundation models
Baichuan
Foundation models
Yi
Foundation models
Gemma
Foundation models
Phi
Foundation models
Hugging Face
Model platforms
Ollama
Model platforms
vLLM
Model platforms
llama.cpp
Model platforms
TensorRT-LLM
Model platforms
ONNX Runtime
Model platforms
Text Generation Inference
Model platforms
LangChain
LLM & agents
LangGraph
LLM & agents
LlamaIndex
LLM & agents
CrewAI
LLM & agents
AutoGen
LLM & agents
Semantic Kernel
LLM & agents
OpenAI Agents SDK
LLM & agents
MCP
LLM & agents
pgvector
Vector & search
Qdrant
Vector & search
Milvus
Vector & search
Weaviate
Vector & search
Pinecone
Vector & search
Chroma
Vector & search
FAISS
Vector & search
Elasticsearch
Vector & search
OpenSearch
Vector & search
PyTorch
Machine learning
AI is not a feature. It is a new computing layer.
Tell us what you are trying to automate, predict, build or improve. We'll work out what AI can do, what it shouldn't do, which model fits, what architecture makes sense, and how to take it into production.
- What you want to automate
- What data you hold
- Where it must run
- What it connects to
- What accuracy is worth
