AI features that actually ship
There's a big gap between a cool ChatGPT demo and a feature your customers rely on. We build LLM capabilities into your product as real, tested integrations: server-side, rate-limited, cost-controlled, and resilient when the model returns something unexpected.
- Chat assistants and support bots grounded in your own content
- Summarization, extraction, and classification of your documents or data
- Semantic search and RAG (retrieval-augmented generation) over your knowledge base
- Content generation and drafting inside your existing workflows
- Transcription, translation, and voice via speech-to-text models
- Structured output the model returns as clean JSON your app can use
The engineering that makes it reliable
The model is the easy part — vendors give you an endpoint. The real work is everything around it: keeping API keys off the client, controlling spend, handling timeouts and rate limits, and validating output before it reaches a user or your database.
- API keys kept server-side, never exposed in the browser or app
- Token and cost tracking with budgets and per-user limits
- Retries, timeouts, and fallbacks when the API is slow or errors
- Streaming responses for a responsive, real-time feel
- Output validation and guardrails against bad or unsafe responses
- Caching and prompt design to cut cost and latency
Grounding AI in your data (RAG)
Off-the-shelf models don't know your business. To make answers accurate and specific, we connect the model to your own content with retrieval — embeddings and a vector store — so responses draw from your data instead of guessing. We're also honest that LLMs can still be wrong, and we design for that.
- Embeddings plus a vector database (pgvector, Pinecone, and similar)
- Retrieval so answers come from your documents, not the model's guesses
- Source citations so users can verify what the AI told them
- Evaluation of accuracy before you put it in front of customers
- Honest guardrails: a human in the loop for high-stakes outputs
- Model-agnostic design so you can switch providers if pricing or quality shifts
More on apis & integrations
Frequently asked questions
Will the AI make things up?
It can — that's inherent to LLMs. We reduce it by grounding answers in your real data with retrieval and adding source citations, and for high-stakes uses we keep a human in the loop. We won't pretend an LLM is a source of guaranteed truth; we design so mistakes are catchable.
How do we keep API costs from spiraling?
We track token usage, set per-user and total budgets, cache repeated requests, and pick the right model for each task rather than defaulting to the most expensive one. Cost control is part of the architecture, not something you discover on the first bill.
Should we use OpenAI, Claude, or something else?
It depends on the task, your budget, and any data or privacy requirements. We build the integration model-agnostic where practical so you're not locked in, and we can compare a couple of options against your actual use case before committing.