RAG pipelines
Ingestion, chunking, embeddings, hybrid search and re-ranking for accurate answers over your data.
AI Engineering Services
Production-grade AI engineering
The engineering behind reliable AI: data pipelines, retrieval systems, fine-tuning, evaluation and MLOps on secure, scalable infrastructure.
- Since 2014
- Full code & IP ownership
- NDA on request
Docs
Chunk
Embed
Vector DB
LLM
Evaluationrelease 1.8
Faithfulness96%
Answer relevance91%
Performance
- Latency p951.4 s
- Cost / 1k req$0.42
- Cache hit rate38%
Prompt-injection testsAll passed
Overview
The hard part of AI is engineering
Getting a model to answer a question is easy. Making it accurate, fast, affordable, secure and observable for thousands of users is an engineering discipline — data pipelines, retrieval, evaluation, deployment and monitoring.
Our engineers build the foundations that AI products depend on, whether you are scaling an existing AI feature, moving to self-hosted models or building a retrieval system over millions of documents.
AI Engineering Services at a glance
- RAG pipelinesIngestion, chunking, embeddings, hybrid search and re-ranking for accurate answers over your…
- Fine-tuning & custom modelsFine-tune open-source or commercial models for your domain, tone and tasks.
- LLM evaluationAutomated test suites and metrics that measure quality, safety and regressions.
- MLOps & LLMOpsVersioning, deployment pipelines, monitoring and rollback for models and prompts.
What we offer
Engineering services
Accurate, fast, affordable and observable AI
Fine-tuning & custom models
Fine-tune open-source or commercial models for your domain, tone and tasks.
LLM evaluation
Automated test suites and metrics that measure quality, safety and regressions.
MLOps & LLMOps
Versioning, deployment pipelines, monitoring and rollback for models and prompts.
Self-hosted models
Deploy open-source models in your cloud or on-premise for privacy and cost control.
AI data pipelines
Collect, clean, label and transform data for training, retrieval and analytics.
Capabilities
Engineering capabilities
Engineering depth that keeps your product fast, secure and easy to evolve.
- Document ingestion from PDFs, websites, databases and drives
- Embedding models and vector index design
- Hybrid search with keyword + vector and re-ranking
- Fine-tuning with LoRA / QLoRA
- Evaluation datasets and LLM-as-judge scoring
- Latency optimisation, batching and streaming
- Cost optimisation with caching and model routing
- GPU inference with vLLM and quantised models
- Prompt and model versioning with safe rollouts
- Tracing and observability for every request
- Security: prompt-injection defences and data isolation
- Infrastructure as code on AWS, Azure or GCP
Our approach
How we think about every project
Strategy first
We start with your business goals, users and constraints, and turn them into a clear scope, architecture and roadmap before writing code.
Solid engineering
Clean architecture, code reviews, automated tests and secure coding practices keep your product fast, stable and easy to extend.
Predictable delivery
Short sprints, regular demos and transparent communication mean you always know what is done, what is next and what it costs.
Technology
The stack we use
Proven, well-supported technologies chosen for your requirements.
- Python
- FastAPI
- PyTorch
- Hugging Face
- LangChain / LlamaIndex
- OpenAI
- Claude
- Gemini
- Llama
- Mistral
- Qwen
- pgvector
- Pinecone
- Qdrant
- Weaviate
- Elasticsearch
- MLflow
- Weights & Biases
- LangSmith / Langfuse
- vLLM
- Docker & Kubernetes
- AWS SageMaker & Bedrock
- Azure OpenAI
- Google Vertex AI
How we work
A clear process from idea to launch
- STEP
Assess
Review current AI features, data, quality issues and infrastructure.
- STEP
Design
Plan retrieval, models, evaluation and deployment architecture.
- STEP
Build pipelines
Implement ingestion, indexing, inference and evaluation pipelines.
- STEP
Evaluate
Measure quality, latency and cost against agreed targets.
- STEP
Deploy
Release with versioning, monitoring and rollback.
- STEP
Operate
Monitor, retrain and optimise as data and usage grow.
We interviewed shops in many countries, and finally we got some professional help in making a decision. We decided to go ahead with Webcarving in India. It is really the best decision we have made so far for the business.
Mike BozorgiCTO, OrcaParts
Deliverables
What you get
- AI architecture documentation
- Production RAG or model pipelines
- Evaluation datasets and dashboards
- CI/CD for models and prompts
- Monitoring and alerting
- Cost and performance report
Frequently asked questions
Should we fine-tune a model or use RAG?
RAG is usually best for answering from changing knowledge; fine-tuning helps with style, format and specialised tasks. Many systems combine both — we test which works for your case.
Can we run AI models on our own servers?
Yes. We deploy open-source models in your cloud or on-premise with GPU inference, keeping data inside your environment.
How do you measure AI quality?
With evaluation datasets built from real questions, automated scoring and human review, tracked on every release.
Our AI feature is slow and expensive. Can you help?
Yes. Caching, smaller or routed models, better retrieval and batching often reduce latency and cost significantly.
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Learn moreHave an idea? Let’s build it together.
Tell us about your project and get a free consultation with a clear plan, timeline and estimate.