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Not a thin wrapper around an API key — a production LLM application grounded in your data, scoped to your use case, and wrapped in the guardrails, evals, and cost controls that keep it reliable.
The demo took a weekend. Production takes engineering.
Anyone can wire a chat box to an LLM API. The gap between that demo and an assistant your customers or employees rely on is where most projects stall.
Production LLM apps need grounding so they don't make things up, guardrails so they stay on-topic and safe, streaming and latency work so they feel fast, cost controls so a viral day doesn't bankrupt you, and an eval harness so you can change the prompt without silently breaking quality. That's the work we do.
What a production LLM app actually includes.
Grounding
Retrieval over your docs, database, or APIs so answers are based on your data — not the model's training set.
Tool calling
The assistant can look things up and take actions through functions you control, with permissions.
Guardrails
Input/output filtering, topic boundaries, PII handling, and refusal behavior tuned to your risk tolerance.
Streaming UX
Token streaming, stop/regenerate, citations, and a chat interface that feels native to your product.
Evals
A golden dataset and automated scoring so prompt and model changes are measured, not guessed.
Cost + observability
Per-conversation cost tracking, caching, rate limits, and full request logging.
Weekend wrapper vs. production app.
| Concern | API wrapper | What we ship |
|---|---|---|
| Accuracy | Hallucinates on your data | Retrieval-grounded with citations |
| Safety | Whatever the base model does | Tuned guardrails + refusals |
| Cost | Unbounded | Caching, limits, per-user budgets |
| Quality over time | Drifts silently | Eval harness blocks regressions |
| Actions | Chat only | Permissioned tool calling |
Ways to engage.
- Working grounded assistant
- One data source
- Deployed to a staging URL
- Grounding + tool calling
- Guardrails + eval harness
- Cost controls + observability
- 30-day support
Streaming, tools, and guardrails — wired in.
Not a chat box around an API key: permissioned tool calls, topic and PII guardrails, and grounded citations.
const stream = await assistant.run({ messages, tools: [searchDocs, createTicket], // permissioned actions guardrails: { topics: ["billing", "product"], pii: "redact" },})for await (const token of stream) { send(token) // stream to the client}// every answer carries grounded, verifiable citationsThe model is treated as untrusted: tools are permissioned, output is validated, and anything irreversible waits for a human.
An eval harness so you can change things safely.
The reason most LLM apps quietly degrade is that no one can measure quality. We ship a golden dataset and automated scoring from day one.
Swap the model, tune the prompt, change the retrieval — and know within minutes whether quality went up or down, before it reaches a customer.
Start an LLM appCommon questions.
Which model should we use?
Can we self-host the model?
How do you stop it hallucinating?
What about prompt injection?
Related reading
How we build and secure the model behind your app.
Tell us what your assistant should do.
A 30-minute call on the use case, the data it needs, and the quality bar. We'll tell you honestly what it takes to ship.