LLM application development that holds up in production.
A model call is easy. A reliable LLM application is engineering: retrieval that grounds answers in your data, prompts designed for the task, structured outputs your code can trust, and evals that prove it works before it reaches users.
The difference is grounding and evaluation
Large language models are powerful, and confidently wrong by default
Large language models (LLMs) generate fluent text, but on their own they answer from general training, not your business, and they state a wrong answer as confidently as a right one. A prototype that impresses in a demo often falls apart the moment real users ask real questions.
We build the layer that closes that gap: retrieval that grounds answers in your own data, structured outputs your code can rely on, guardrails that keep the model in scope, and evaluation that measures accuracy on real cases. That's what turns a capable model into an application you can ship.
What you get
The LLM engineering that makes output reliable
Retrieval, prompt design, and structured outputs, backed by the evals and guardrails that make answers safe to trust.
Retrieval grounded in your data
A RAG pipeline, ingestion, chunking, embeddings, vector search, and reranking, so answers come from your content instead of the model's guesswork.
Prompt and context engineering
Prompts, context assembly, and few-shot examples designed for your task, so you get the right answer within the token and cost budget.
Structured, typed outputs
Schema-validated JSON the model must conform to, so downstream code can rely on the shape of what comes back, not parse free text.
Evals that gate releases
Test sets that score accuracy and faithfulness on real cases, so a change ships only when the numbers hold, not because a demo looked good.
Guardrails and safety
Input and output checks, refusal on out-of-scope requests, and hallucination limits, so the app stays safe and on-topic in the wild.
Model selection and routing
Model-agnostic by design, routing each request to the model that fits on quality and cost, so you're not tied to one provider.
Streaming, caching, cost control
Streamed responses, prompt and result caching, and token budgeting, so the app feels fast and the bill stays predictable at scale.
Owned, production-grade code
The LLM layer engineered type-safe, tested, and observable, with full account ownership, so you can maintain and extend it yourself.
How we build it
From your data to an LLM app you can trust
A clear path from scoping the task to a deployed, evaluated LLM application, reviewed at every stage.
- 01
Discovery & data
We define the task the LLM should do, review the data that will ground it, and agree how we'll measure a good answer.
- 02
Retrieval & prompt design
We design the RAG pipeline and the prompts and context around it, then prototype on your real data before committing to a build.
- 03
Build the LLM layer
We build retrieval, prompting, structured outputs, and the APIs around them, typed and tested, shipped in reviewable iterations.
- 04
Evaluate & harden
We run evals on real cases, tighten guardrails, and tune cost and latency, so accuracy holds up under messy, real inputs.
- 05
Deploy & improve
We ship with tracing on prompts and responses, watch quality and cost in production, and iterate as your data and models change.
- Python
- TypeScript
- Anthropic Claude
- OpenAI
- pgvector
- LangChain
Why it matters
The gap between a demo and a product is the engineering
Most LLM projects stall in the same place: a prototype wows everyone, then real usage exposes wrong answers, unpredictable output, and costs that balloon. The model was never the problem. What was missing is the grounding, structure, and evaluation that make output dependable.
Building that layer well is what lets you put an LLM in front of customers with confidence, measure whether it's actually accurate, and keep cost in check as usage grows. Because you own the code and it stays model-agnostic, you can adopt better models as they ship instead of rebuilding each time.
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FAQ
LLM application development, answered
Thinking about an LLM app? Book a call and we'll scope it with you.
What counts as an LLM application, versus just calling an API?
Calling a model API is one line; an LLM application is the engineering around it that makes the output reliable: retrieval that grounds answers in your data, prompt and context design, structured outputs your code can trust, evals that measure quality, and guardrails that keep it safe. That layer is the difference between a demo and something you can ship to users.
What is RAG, and why do our answers need it?
RAG, retrieval-augmented generation, means the app first retrieves relevant passages from your own content and gives them to the model as context, so answers are grounded in your documents rather than the model's general training. It's what lets an LLM answer about your products, policies, or records accurately, and it's usually the single biggest factor in whether answers are correct.
How do you keep the model from making things up?
We ground responses with retrieval so the model answers from your data, constrain it to structured outputs where possible, and add guardrails that make it decline or flag low-confidence cases instead of guessing. We also build evaluation sets that score faithfulness on real questions, so we measure hallucination rather than hope it's low.
Which LLM should we use?
It depends on the task, your accuracy bar, latency needs, and budget, so we stay model-agnostic and choose per case, working with providers like Anthropic Claude and OpenAI, and open models where privacy or cost calls for it. We often route different requests to different models, and because the app is built to swap models, you're never locked to one as the market shifts.
How do you handle our data and privacy?
Your data is used to ground answers through retrieval, not to train models without your explicit agreement. We keep access least-privilege, can keep sensitive data in your own infrastructure, and choose providers and deployment to match your privacy and compliance requirements. Where it matters, we can run open models so data never leaves your environment.
How long does an LLM application engagement take?
A focused feature grounded in a defined set of data can reach a useful prototype in a few weeks; broader or higher-stakes applications take longer because they need more evaluation and tighter guardrails. We scope it with you on a call and ship in iterations you can review, so you see working software early rather than waiting for one big release.
Let's build something worth shipping
Tell us what you're working on. Book a call to talk through it, or send a few details and we'll get back to you with next steps.