Services
Generative AI
LLM-powered features: chat interfaces, retrieval-augmented generation over your own data, and agents that take action, built on top of existing model APIs rather than training models from scratch.
Generative AI work here means building features on top of large language models: chat interfaces, document Q&A over your own content through retrieval-augmented generation (RAG), and agents that can take multi-step actions rather than just answering a question. We integrate with existing LLM APIs rather than training foundation models ourselves, since that's the practical, cost-effective approach for the vast majority of business use cases.
The engineering work is in the details that make or break these systems in production: retrieval quality so the model answers from your actual documents instead of guessing, prompt design that produces consistent output, and guardrails so the system fails safely instead of confidently making something up. We're direct with clients about hallucination risk and where a generative AI feature is and isn't appropriate.
Business benefits
Why this matters for your team
Grounded in your own data
RAG pipelines mean answers come from your documents and systems, not just the model's general training.
Built on proven model APIs
We integrate existing LLM APIs rather than the slower, costlier path of training a model from scratch.
Honest about failure modes
We design for hallucination and errors explicitly instead of pretending the model is always right.
Fits into existing systems
Chat and agent features connect to your actual data sources and tools, not a disconnected demo.
Features
What's included
RAG pipeline design
Retrieval systems that fetch the right context from your documents before the model generates an answer.
Chat interface development
Conversational interfaces wired to your data and use case, not a generic chatbot widget.
Prompt engineering & evaluation
Prompts designed and tested systematically for consistent output, not tuned by trial and error alone.
Agentic workflows
Multi-step processes where the model can call tools or take actions, with guardrails on what it's allowed to do.
Document ingestion pipelines
Turning your existing PDFs, wikis, or databases into a searchable knowledge base the model can draw on.
Output guardrails
Validation and fallback behavior so the system degrades safely instead of confidently returning a wrong answer.
Development process
The same process regardless of scope
Scope
We turn your problem into a written scope and a fixed-price or time-and-materials plan, in days, not weeks.
Build
Weekly working demos, not a black box until launch. You see real progress every week, not a status slide.
Harden
Load testing, security review and edge cases get handled before launch, not after your first incident.
Operate
We stay on for support and iteration after launch. No handoff-and-disappear.
Technologies
What we build it with
FAQs
Common questions
Ready to start your Generative AI project?
Tell us what you're building. You'll hear back from an engineer, not a sales queue.