LLM application development
Shipping an LLM feature into a live product is a software engineering problem. We build the streaming, validation, provider abstraction, caching and cost controls that make it something your team can own and operate.
What is llm app development?
LLM application development is the work of embedding language-model features into a product that already exists and already has users, which makes it a software engineering problem more than a model problem. What decides success is streaming that feels fast, structured output validated against a schema before it touches your data, a provider abstraction so models can be swapped without a rewrite, and per-tenant cost controls.
Why teams call us
Almost every engagement starts from one of these. If none of them sound familiar, you probably do not need us yet.
A response that takes eight seconds to appear reads as broken, even when the answer is good. Without streaming, optimistic UI and useful intermediate states, adoption stalls regardless of output quality.
Users try the feature once and do not come back to it.
Vendor client code appears in a dozen modules, prompts are hard-coded next to business logic, and model names are string literals. Changing model becomes a multi-week refactor, exactly when a price change or deprecation forces one.
A routine provider change turns into an unplanned engineering project.
The model returns something slightly off-shape, the code assumes the happy path, and malformed data is written into a system of record. These bugs surface weeks later as data-quality incidents with no obvious cause.
Silent data corruption that is expensive to detect and worse to unwind.
Spend is visible only as one provider invoice. There is no per-feature or per-tenant breakdown, so it is impossible to tell which customers are unprofitable or which feature to optimise.
Pricing decisions made without knowing unit economics.
Scope
Streaming responses, useful loading and partial states, editable output, and an honest way to communicate uncertainty. The interaction design determines whether people trust the feature enough to keep using it.
One internal interface, multiple providers behind it, with prompts and model selection in configuration. Each step routes to the cheapest model that passes evaluation for that step.
Schema-enforced generation, validation at the boundary, and repair-retry handling so a malformed response never becomes a data-quality incident.
Prompt and response caching, per-tenant budgets enforced in code, and dashboards that attribute cost per feature and per customer.
How it runs
Fixed checkpoints with a named deliverable at each one, so you can stop the engagement at any stage and still own something useful.
We define what the feature does, what it refuses to do, how it streams, and how it behaves when the model is wrong or unavailable — before writing the integration.
The service boundary, provider abstraction, schema contracts, validation and caching, with tests that do not require a live model call.
The user-facing feature, wired to the evaluation set so quality is measured rather than assumed.
Staged traffic ramp, model routing tuned against real usage, and per-tenant limits enabled.
Results
The interesting part of shipping AI inside an existing product is how little of the work is about the model. The model call is a function invocation. Everything around it — the boundary it sits behind, the schema it must conform to, the way partial output renders, what happens on a timeout, who pays for the tokens — is ordinary software engineering, and it is where these projects succeed or fail.
That framing is useful because your team already knows how to do ordinary software engineering well. The failures we get called in to fix are rarely exotic. They are vendor SDK calls sprinkled across twelve modules, prompts hard-coded beside business logic, no validation between the model and the database, and no idea which customer is generating the inference bill.
It is tempting to call the vendor SDK directly and move on. The reason not to is empirical: model providers change prices, deprecate versions on their own timetable, and periodically ship a model that is materially better or cheaper than what you are using.
If your application talks to your own interface, swapping is a configuration change plus an evaluation run. If it talks to a vendor SDK in a dozen places, the same swap is a refactor you did not schedule. The abstraction costs perhaps a day to build and pays for itself the first time either of those happens — which, at the current pace, is roughly every few months.
Structured output modes have made model responses far more reliable, and they are still not a guarantee. Assume conformance and you will eventually write malformed data into a system of record, discover it weeks later as a data-quality incident, and spend longer tracing the cause than the original feature took to build.
The rule is simple: parse, validate against the schema in your own code, and treat a failure as a retryable error with a repair prompt. Never regex free text, and never persist anything that did not pass validation.
A single provider invoice tells you nothing actionable. You need cost per feature and per tenant, because the two questions you will be asked are “which feature is expensive” and “which customers are unprofitable” — and neither is answerable retrospectively without the logging in place.
The same instrumentation makes routing possible. Once you can see that intent classification is 60% of your call volume, moving it to a small model is an obvious decision rather than a speculative one.
Next Step
A 30-minute technical call with an engineer who has shipped this before — not a sales qualification round. You leave with a feasibility read, a rough shape for the build, and an honest answer about whether it is worth doing at all.