Generative AI development that holds up in production.
From retrieval-augmented assistants to domain copilots and document automation, we build generative AI applications with grounding, evaluation, and cost control, not demos that fall over on the second prompt. LLM systems engineered to be accurate, fast, and yours.
What we do
- 01
RAG over your private knowledge
We build retrieval pipelines over your documents and data with chunking, embeddings, and reranking tuned for relevance, so answers are grounded and cite their sources.
- 02
Copilots and assistants
Domain copilots that live inside your product or internal tools, scoped to your data and workflows, with streaming responses and a UX that feels instant.
- 03
Evaluation and prompt versioning
We measure faithfulness, relevance, and hallucination rate on a reference dataset and version every prompt, so quality is tracked rather than guessed.
- 04
Cost and latency control
Model routing, caching, and right-sized context keep response times low and spend predictable as usage grows.
What we do
Capabilities built for production.
RAG over your private knowledge
We build retrieval pipelines over your documents and data with chunking, embeddings, and reranking tuned for relevance, so answers are grounded and cite their sources.
Copilots and assistants
Domain copilots that live inside your product or internal tools, scoped to your data and workflows, with streaming responses and a UX that feels instant.
Evaluation and prompt versioning
We measure faithfulness, relevance, and hallucination rate on a reference dataset and version every prompt, so quality is tracked rather than guessed.
Cost and latency control
Model routing, caching, and right-sized context keep response times low and spend predictable as usage grows.
Safe, grounded output
Citations, confidence signals, and fallbacks keep the system honest, telling users when it does not know rather than inventing an answer.
What you get
Deliverables, owned by you.
Concrete output at the end of the engagement, with full source and IP ownership. No lock-in, no black boxes.
- Production LLM application with grounded, cited responses
- Retrieval pipeline with embeddings, reranking, and a vector store
- Evaluation dataset and scoring for faithfulness and relevance
- Streaming UI with citations and graceful fallbacks
- Cost and latency instrumentation, with full code and IP ownership
Technology we use
A pragmatic, modern stack. We pick the right tool for the job rather than forcing a favourite.
How we work
A clear path from idea to launch.
- 1
Frame the use case
We define the questions the system must answer, the sources it can use, and the bar for a correct response.
- 2
Build retrieval and prompts
We ingest your content, tune retrieval for relevance, and craft prompts that ground answers in real sources.
- 3
Evaluate and tune
We score outputs on a reference set, close the gaps on faithfulness and relevance, and version what works.
- 4
Ship with controls
We deploy with streaming UX, caching, monitoring, and cost controls, then iterate against live feedback.
FAQ
Questions, answered.
The things teams ask before they start. Still unsure? Talk to a senior engineer, not a salesperson.
What is RAG and why do we need it?
How do you stop the model from hallucinating?
Which AI models do you use?
How do you control AI running costs?
Related
Where teams go next.
Let's build your generative ai development.
Senior engineers, real evaluation, and code you own. Tell us what you are building and we will scope it with you.