LLM applications
LLM application development across Claude, GPT and Gemini
Most LLM features are a prompt, a model call and a text box. That holds up until the output has to parse, the latency has to be under two seconds, the bill has to fit a unit price, and the provider has an outage on a Tuesday afternoon.
- Structured output
- Streaming interfaces
- Model routing
- Provider fallbacks
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.
Signs you need this
We build the application around the model. Typed structured output, streaming UI, routing between models by task and cost, caching, fallbacks across providers, and per-feature spend tracking from the first deploy.
- JSON output that breaks parsing a few percent of the time
- One model used for every task regardless of cost
- No fallback when a provider rate limits or goes down
- Inference spend that nobody can attribute to a feature
- Responses that take eight seconds to start rendering
- Prompts living in a dashboard instead of version control
What you get
What is included
Structured output
Schema-validated responses with typed parsing, repair on failure, and tests for the edge cases that break naive JSON mode.
Streaming interfaces
Token streaming, partial structured rendering and tool-call progress in the UI, so users see work start in under a second.
Model routing
Cheap fast models for classification and extraction, frontier models for reasoning, routed per task with measured quality on each.
Provider fallbacks
Automatic failover across Anthropic, OpenAI, Google and self-hosted models, with retries and timeouts tuned per call.
Cost controls
Prompt caching, response caching, token budgets per user and per feature, and a dashboard that shows spend by route.
Prompts as code
Prompts versioned in the repo, reviewed in pull requests, and tested by the eval suite before they ship.
How it runs
How we deliver LLM applications
- 01
Define the unit economics
Target latency, acceptable error rate and cost per request, written down before any prompt is drafted.
- 02
Prototype against real data
Compare models on your actual inputs with a scored test set, not on a vendor benchmark.
- 03
Production build
Structured output, streaming, routing, fallbacks and observability wired into your application.
- 04
Tune and hand over
Cut cost and latency against the eval baseline, then hand over with dashboards and runbooks.
Industries we build this for
LLM applications questions
Something else? Email t@moher.ai and an engineer will answer it.
Which LLM should we use?
Usually more than one. We test Claude, GPT, Gemini and open-weight models on your own data and route each task to the cheapest model that meets the quality bar.
Can you add AI features to our existing app?
Yes. Most of our LLM work ships inside an existing product. We work in your codebase, your CI and your review process.
How do you control inference cost?
Prompt caching, smaller models for simple steps, response caching where answers repeat, and hard token budgets per user. Every route reports its own spend.
Should we self-host an open-weight model?
Sometimes. It makes sense for strict data residency, very high volume on a narrow task, or fine-tuned behaviour. For most products, hosted APIs are cheaper once you count the GPU and the on-call.
How do you handle sensitive data?
Zero-retention API agreements where providers offer them, PII redaction before model calls where needed, and self-hosted inference for data that can't leave your network.
Tell us what you need built
Four quick questions and an email. An engineer reads every brief and replies within one business day with next steps, and a written scope follows within a week.
- Written scope and price within a week
- Production deploy by week three
- Your repo and cloud from the first commit
- Senior engineers only, no account managers
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.