Parallax/Services/AI product engineering
LLM apps, RAG and assistants that people actually use
Parallax designs and builds production AI applications: assistants grounded in your documents and data, natural-language access to your databases, structured extraction from messy inputs, and voice and multimodal interfaces. We build the whole product around the model — interface, backend, integrations, evaluation and deployment — and we stay until it’s adopted.
Service
What do we build?
- Knowledge assistants (RAG)Answers grounded in your wikis, tickets, contracts and PDFs — with citations, permissions and freshness built in.
- Natural language → SQLAnalysts and operators ask questions in plain English and get correct, explainable queries against your warehouse.
- Structured extractionTurn invoices, contracts, emails and forms into validated, typed data your systems can use.
- AI searchSemantic and hybrid search across text, images and video, so people find things by meaning.
- Voice & multimodalReal-time voice agents, speech-to-speech translation and apps that see images and video.
- Team-specific assistantsAssistants with the tools, context and guardrails for one team’s workflow — sales, legal, support, editorial.
How do we make LLM applications reliable?
Most AI demos fail in production for the same reasons: nobody measured quality, the model sees the wrong context, and nobody can tell why it gave an answer. We design against those from day one.
- Evaluation sets built from your real cases, run on every prompt, model or retrieval change.
- Context engineering: retrieval, reranking and context management designed for your data, not a generic chunk-and-embed.
- Structured outputs and tool calls instead of free text wherever software consumes the result.
- Guardrails and human review where the cost of a wrong answer is high.
- Observability: every answer traceable to its sources, prompts, tools and model version.
Which model should we use?
It depends on the task, and it will change. We benchmark candidate models on your evaluation set for quality, latency, cost and privacy — commercial APIs, open-weight models and self-hosted inference — and design the system so switching models is a configuration change, not a rewrite.
How much does it cost and how long does it take?
Most clients start with a two-to-three-week Prototype Sprint from $20k, which puts a working version in front of real users and ends with a costed build plan. Production builds typically take six to twelve weeks and start at $60k, depending on integrations, data and compliance requirements.
Questions
What is retrieval-augmented generation (RAG)?
RAG is a pattern where an AI system first retrieves relevant passages from your own documents or data, then uses a language model to answer using only that material, with citations. It lets an assistant answer accurately about your business without training a model on your data.
Can the assistant respect our existing permissions?
Yes. We enforce access control at retrieval time, so people only get answers drawn from documents they are already allowed to see, using your existing identity provider.
Do you fine-tune models?
When it helps. Most business problems are solved faster and more cheaply with better retrieval, context and structured outputs. We fine-tune when evaluation shows a clear gain in quality, latency or cost that prompting cannot reach.
Updated 2026-10-23