AI & Data
LLM Apps
development
Large language models are easy to demo and hard to ship. We build LLM features that hold up in production: the right model for the job, prompt and context engineering, fine-tuning where it pays, and evals plus monitoring so quality is measured, not guessed.
- Type
- Applied AI
- Techniques
- Prompt · RAG · Tune
- Quality
- Eval-driven
- Models
- Closed & open
- Best for
- Product features
In short
LLM Apps, at a glance
- Frontier or open models chosen per task, balancing capability, cost and latency.
- Caching, routing and smaller models keep latency and token bills under control.
- Structured outputs, validation and fallbacks turn a flaky demo into a dependable feature.
- RAG and context engineering connect models to your knowledge, not just their training.
What we build with LLM Apps.
A clever prompt makes a great demo; a reliable feature needs engineering. We treat LLM work like software: versioned prompts, structured outputs, context pipelines, fallbacks and a test suite that scores quality on every change.
We pick models pragmatically — frontier APIs where capability matters, smaller or open models where cost, latency or privacy do — and fine-tune only when evals prove it beats a well-engineered prompt.
LLM features
Summarisation, extraction, classification and generation built into your product.
Prompt & context
Structured prompts and context pipelines that produce reliable, typed outputs.
Fine-tuning
Targeted fine-tuning and distillation when it measurably beats prompting.
Evals & monitoring
Quality scoring in CI and live monitoring for drift and cost.
The case for LLM Apps.
Right model, right job
Frontier or open models chosen per task, balancing capability, cost and latency.
Fast and affordable
Caching, routing and smaller models keep latency and token bills under control.
Reliable outputs
Structured outputs, validation and fallbacks turn a flaky demo into a dependable feature.
Grounded in your data
RAG and context engineering connect models to your knowledge, not just their training.
Wired into product
LLM calls become typed services with the same rigour as the rest of your stack.
Eval-driven quality
Every prompt or model change is scored against real cases before it ships.
How we engineer
with LLM Apps.
LLM feature development
Design and ship product features powered by LLMs, from summarisation to generation.
- Structured outputs
- Typed LLM services
- Fallback handling
The stack we pair
with LLM Apps.
Models
Frameworks
Serving & tuning
Quality
Outcomes, not just output.
A six-step cycle, repeated until it's right.
Transparent, predictable and collaborative. You always know what's shipping next and why.
Discovery
We map the business, users and constraints, then pressure-test the problem before a line of code.
Planning
Architecture, scope, and a sprint roadmap with clear milestones, budgets and success metrics.
Design
Research-led UX and high-fidelity interfaces, validated with prototypes before build.
Development
Senior-led engineering in two-week sprints with demoable increments and continuous review.
Testing & QA
Automated and manual testing, security review and performance hardening before release.
Launch & Care
Confident deployment, monitoring and SLA-backed support that keeps things humming.
LLM Apps questions, answered.
Still unsure if LLM Apps is right for your project? A senior engineer will tell you straight on a free call.
It depends on the task. Frontier APIs like Claude or GPT win when raw capability matters; smaller or open models (Llama, Mistral) win on cost, latency or when data must stay in your environment. We benchmark options against your task and recommend honestly.
Usually not at first. A well-engineered prompt with good context beats a mediocre fine-tune and is far cheaper to maintain. We fine-tune when evals show a clear, durable win, often to make a smaller model match a bigger one on your specific task.
We treat them like software: structured, validated outputs, fallbacks for failures, version-controlled prompts, and an evaluation suite that scores quality on every change. In production we monitor quality, cost and drift.
Caching repeated work, routing easy requests to cheaper models, trimming context, and using smaller fine-tuned models where they suffice. We track spend on dashboards so cost never surprises you.
Yes. Most of our LLM work is embedding features into existing products: an assistant, smart search, drafting, classification or extraction, shipped as typed services that fit your current architecture.
Considering an alternative stack?
Ready to build with LLM Apps?
Book a free 30-minute consultation. We'll pressure-test your idea and map a LLM Apps approach, whether or not we end up working together.
What happens after you hit send.
You book in 60 seconds
Share a few details below. No lengthy forms, no sales gatekeeping.
A 30-minute strategy call
You talk to a senior engineer about your actual problem, not an account manager.
A clear path forward
You leave with concrete recommendations and a rough scope, whether or not we work together.
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